{"id":6607,"date":"2023-10-19T16:10:44","date_gmt":"2023-10-19T07:10:44","guid":{"rendered":"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/?p=6607"},"modified":"2023-10-19T16:10:44","modified_gmt":"2023-10-19T07:10:44","slug":"%e9%87%8f%e5%ad%90%e3%82%a2%e3%83%8b%e3%83%bc%e3%83%aa%e3%83%b3%e3%82%b0%e3%81%ab%e3%82%88%e3%82%8b%e9%85%8d%e9%80%81%e8%a8%88%e7%94%bb%e5%ae%9f%e8%b7%b5%e7%b7%a8","status":"publish","type":"post","link":"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2023\/10\/19\/%e9%87%8f%e5%ad%90%e3%82%a2%e3%83%8b%e3%83%bc%e3%83%aa%e3%83%b3%e3%82%b0%e3%81%ab%e3%82%88%e3%82%8b%e9%85%8d%e9%80%81%e8%a8%88%e7%94%bb%e5%ae%9f%e8%b7%b5%e7%b7%a8\/","title":{"rendered":"\u91cf\u5b50\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u306b\u3088\u308b\u914d\u9001\u8a08\u753b(\u5b9f\u8df5\u7de8)"},"content":{"rendered":"<p><a href=\"https:\/\/colab.research.google.com\/github\/T-QARD\/t-wave\/blob\/main\/notebooks\/cvrp\/CVRP.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\"><img decoding=\"async\" src=\"https:\/\/colab.research.google.com\/assets\/colab-badge.svg\" alt=\"Open in Colab\" \/><\/a><\/p>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_83 counter-hierarchy ez-toc-counter ez-toc-white ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title ez-toc-toggle\" style=\"cursor:pointer\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2023\/10\/19\/%e9%87%8f%e5%ad%90%e3%82%a2%e3%83%8b%e3%83%bc%e3%83%aa%e3%83%b3%e3%82%b0%e3%81%ab%e3%82%88%e3%82%8b%e9%85%8d%e9%80%81%e8%a8%88%e7%94%bb%e5%ae%9f%e8%b7%b5%e7%b7%a8\/#%E6%A6%82%E8%A6%81\" >\u6982\u8981<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2023\/10\/19\/%e9%87%8f%e5%ad%90%e3%82%a2%e3%83%8b%e3%83%bc%e3%83%aa%e3%83%b3%e3%82%b0%e3%81%ab%e3%82%88%e3%82%8b%e9%85%8d%e9%80%81%e8%a8%88%e7%94%bb%e5%ae%9f%e8%b7%b5%e7%b7%a8\/#%E6%96%87%E7%8C%AE%E6%83%85%E5%A0%B1\" >\u6587\u732e\u60c5\u5831<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2023\/10\/19\/%e9%87%8f%e5%ad%90%e3%82%a2%e3%83%8b%e3%83%bc%e3%83%aa%e3%83%b3%e3%82%b0%e3%81%ab%e3%82%88%e3%82%8b%e9%85%8d%e9%80%81%e8%a8%88%e7%94%bb%e5%ae%9f%e8%b7%b5%e7%b7%a8\/#%E5%95%8F%E9%A1%8C\" >\u554f\u984c<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2023\/10\/19\/%e9%87%8f%e5%ad%90%e3%82%a2%e3%83%8b%e3%83%bc%e3%83%aa%e3%83%b3%e3%82%b0%e3%81%ab%e3%82%88%e3%82%8b%e9%85%8d%e9%80%81%e8%a8%88%e7%94%bb%e5%ae%9f%e8%b7%b5%e7%b7%a8\/#%E3%82%AF%E3%83%A9%E3%82%B9%E3%82%BF%E3%83%AA%E3%83%B3%E3%82%B0\" >\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2023\/10\/19\/%e9%87%8f%e5%ad%90%e3%82%a2%e3%83%8b%e3%83%bc%e3%83%aa%e3%83%b3%e3%82%b0%e3%81%ab%e3%82%88%e3%82%8b%e9%85%8d%e9%80%81%e8%a8%88%e7%94%bb%e5%ae%9f%e8%b7%b5%e7%b7%a8\/#Part1_%E5%85%83%E8%AB%96%E6%96%87%E3%81%AB%E8%BC%89%E3%81%A3%E3%81%A6%E3%81%84%E3%81%9F%E6%89%8B%E6%B3%95\" >Part1 \u5143\u8ad6\u6587\u306b\u8f09\u3063\u3066\u3044\u305f\u624b\u6cd5<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2023\/10\/19\/%e9%87%8f%e5%ad%90%e3%82%a2%e3%83%8b%e3%83%bc%e3%83%aa%e3%83%b3%e3%82%b0%e3%81%ab%e3%82%88%e3%82%8b%e9%85%8d%e9%80%81%e8%a8%88%e7%94%bb%e5%ae%9f%e8%b7%b5%e7%b7%a8\/#Part2_%E5%AE%B9%E9%87%8F%E3%81%AE%E5%B9%B3%E5%9D%87%E5%80%A4%E3%81%AB%E5%AF%84%E3%81%9B%E3%82%8B%E6%89%8B%E6%B3%95\" >Part2 \u5bb9\u91cf\u306e\u5e73\u5747\u5024\u306b\u5bc4\u305b\u308b\u624b\u6cd5<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2023\/10\/19\/%e9%87%8f%e5%ad%90%e3%82%a2%e3%83%8b%e3%83%bc%e3%83%aa%e3%83%b3%e3%82%b0%e3%81%ab%e3%82%88%e3%82%8b%e9%85%8d%e9%80%81%e8%a8%88%e7%94%bb%e5%ae%9f%e8%b7%b5%e7%b7%a8\/#%E3%83%AB%E3%83%BC%E3%83%86%E3%82%A3%E3%83%B3%E3%82%B0\" >\u30eb\u30fc\u30c6\u30a3\u30f3\u30b0<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2023\/10\/19\/%e9%87%8f%e5%ad%90%e3%82%a2%e3%83%8b%e3%83%bc%e3%83%aa%e3%83%b3%e3%82%b0%e3%81%ab%e3%82%88%e3%82%8b%e9%85%8d%e9%80%81%e8%a8%88%e7%94%bb%e5%ae%9f%e8%b7%b5%e7%b7%a8\/#%E3%81%BE%E3%81%A8%E3%82%81\" >\u307e\u3068\u3081<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2023\/10\/19\/%e9%87%8f%e5%ad%90%e3%82%a2%e3%83%8b%e3%83%bc%e3%83%aa%e3%83%b3%e3%82%b0%e3%81%ab%e3%82%88%e3%82%8b%e9%85%8d%e9%80%81%e8%a8%88%e7%94%bb%e5%ae%9f%e8%b7%b5%e7%b7%a8\/#%E3%81%82%E3%81%A8%E3%81%8C%E3%81%8D\" >\u3042\u3068\u304c\u304d<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"%E6%A6%82%E8%A6%81\"><\/span>\u6982\u8981<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u8a18\u4e8b\u300c<a href=\"\/T-Wave\/?p=1729\">\u91cf\u5b50\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u30de\u30b7\u30f3\u306b\u3088\u308b\u914d\u9001\u8a08\u753b<\/a>\u300d\u3067\u6271\u3063\u305f\u8ad6\u6587\u3067\u306f\u3001CVRP(\u5bb9\u91cf\u5236\u7d04\u6709\u308a\u306e\u914d\u9001\u8a08\u753b\u554f\u984c)\u3092\u91cf\u5b50\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u30de\u30b7\u30f3\u3068\u53e4\u5178\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u3092\u7528\u3044\u305f\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9\u306a\u624b\u6cd5\u3067\u89e3\u304d\u3001\u305d\u306e\u6027\u80fd\u3092\u53e4\u5178\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u3068\u6bd4\u8f03\u3057\u3066\u3044\u307e\u3057\u305f\u3002\u672c\u8a18\u4e8b\u3067\u306f\u3001\u8ad6\u6587\u4e2d\u3067\u767b\u5834\u3057\u305f<a href=\"http:\/\/vrp.galgos.inf.puc-rio.br\/index.php\/en\/\">CVRPLIB<\/a>\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3064\u3044\u3066\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306fSA\uff08\u30b7\u30df\u30e5\u30ec\u30fc\u30c6\u30c3\u30c9\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\uff09\u3001\u30eb\u30fc\u30c6\u30a3\u30f3\u30b0\u306f<a href=\"https:\/\/developers.google.com\/optimization\">Google\u306eOR-Tools<\/a>\u3092\u5229\u7528\u3057\u3066\u89e3\u3044\u3066\u3044\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2><span class=\"ez-toc-section\" id=\"%E6%96%87%E7%8C%AE%E6%83%85%E5%A0%B1\"><\/span>\u6587\u732e\u60c5\u5831<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<ul>\n<li>1\u3064\u76ee\u306e\u8ad6\u6587\n<ul>\n<li>\u30bf\u30a4\u30c8\u30eb\uff1aA Hybrid Solution Method for the Capacitated Vehicle Routing Problem Using a Quantum Annealer<\/li>\n<li>\u8457\u8005\uff1aSebastian Feld, Chrisoph Roch, Thomas Gabor, Christian Seidel, Florian Neukart, Isabella Galter, Wolfgang Mauerer, and Claudia Linnhoff-Popien<\/li>\n<li>\u66f8\u8a8c\u60c5\u5831\uff1a<a href=\"https:\/\/doi.org\/10.3389\/fict.2019.00013\">https:\/\/doi.org\/10.3389\/fict.2019.00013<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<ul>\n<li>2\u3064\u76ee\u306e\u8ad6\u6587\n<ul>\n<li>\u30bf\u30a4\u30c8\u30eb\uff1aAn Approach to the Vehicle Routing Problem with Balanced Pick-up Using Ising Machines<\/li>\n<li>\u8457\u8005\uff1aSiya Bao, Masashi Tawada, Shu Tanaka, Nozomu Togawa<\/li>\n<li>\u66f8\u8a8c\u60c5\u5831\uff1a<a href=\"https:\/\/ieeexplore.ieee.org\/document\/9427355\">https:\/\/ieeexplore.ieee.org\/document\/9427355<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2><span class=\"ez-toc-section\" id=\"%E5%95%8F%E9%A1%8C\"><\/span>\u554f\u984c<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u3053\u306e\u5b9f\u8df5\u8a18\u4e8b\u3067\u306f\u3001\u4ee5\u4e0b\u306e2\u7a2e\u985e\u306e\u624b\u6cd5\u3067CVRP\u3092\u89e3\u3044\u3066\u307f\u307e\u3057\u305f\u3002<\/p>\n<ol>\n<li>\u5143\u8ad6\u6587\u306b\u8f09\u3063\u3066\u3044\u305f\u624b\u6cd5<\/li>\n<li>\u5bb9\u91cf\u306e\u5e73\u5747\u5024\u306b\u5bc4\u305b\u308b\u624b\u6cd5<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h3><span class=\"ez-toc-section\" id=\"%E3%82%AF%E3%83%A9%E3%82%B9%E3%82%BF%E3%83%AA%E3%83%B3%E3%82%B0\"><\/span>\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h4><span class=\"ez-toc-section\" id=\"Part1_%E5%85%83%E8%AB%96%E6%96%87%E3%81%AB%E8%BC%89%E3%81%A3%E3%81%A6%E3%81%84%E3%81%9F%E6%89%8B%E6%B3%95\"><\/span>Part1 \u5143\u8ad6\u6587\u306b\u8f09\u3063\u3066\u3044\u305f\u624b\u6cd5<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><a href=\"\/T-Wave\/?p=1729\">\u8ad6\u6587\u7d39\u4ecb\u306e\u8a18\u4e8b<\/a>\u3067\u306f\u3001\u5bb9\u91cf\u5236\u7d04\u4ed8\u304d\u306e\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306b\u95a2\u3059\u308b\u5b9a\u5f0f\u5316\u3092\u7701\u3044\u3066\u3044\u305f\u306e\u3067\u3001\u3053\u3053\u3067\u7c21\u5358\u306b\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n<p>\u307e\u305a\u306f\u5909\u6570\u306b\u3064\u3044\u3066\u3067\u3059\u3002<\/p>\n<ul>\n<li>$x_{v}^k$: $k$\u756a\u76ee\u306e\u30af\u30e9\u30b9\u30bf\u30fc\u306b$v$\u756a\u76ee\u306e\u9867\u5ba2\u304c\u542b\u307e\u308c\u308b\u3068\u304d\u306f1\u3092\u3001\u305d\u3046\u3067\u306a\u3044\u3068\u304d\u306f0\u3092\u3068\u308b\u4e8c\u5024\u5909\u6570<\/li>\n<li>$y_n^k$: \u4e0d\u7b49\u5f0f\u5236\u7d04\u3092\u8003\u3048\u308b\u305f\u3081\u306e\u64ec\u4f3c\u5909\u6570<\/li>\n<\/ul>\n<p>\u6700\u9069\u5316\u306b\u5fc5\u8981\u306a\u5165\u529b\u30c7\u30fc\u30bf\u306f\u4ee5\u4e0b\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/p>\n<ul>\n<li>$w_{v}$: \u9867\u5ba2\u306e\u8981\u6c42\u91cf\u3092\u8868\u3059\u5b9a\u6570<\/li>\n<li>$D_{uv}$: \u9867\u5ba2\u3069\u3046\u3057\u306e\u8ddd\u96e2\u3092\u8868\u3059\u884c\u5217<\/li>\n<li>$m$: \u30af\u30e9\u30b9\u30bf\u30fc\u306e\u6570<\/li>\n<li>$W$: \u30af\u30e9\u30b9\u30bf\u30fc\u306e\u5bb9\u91cf\u306e\u6700\u5927\u5024<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u7d9a\u3044\u3066\u5b9a\u5f0f\u5316\u3067\u3059\u3002\u4ee5\u4e0b\u306e3\u3064\u306e\u9805\u304b\u3089\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<ol style=\"list-style-type:decimal;\">\n<li>\u30c8\u30e9\u30c3\u30af\u306e\u5bb9\u91cf\u306b\u95a2\u3059\u308b\u4e0d\u7b49\u5f0f\u5236\u7d04$$ H_A = X\\sum_{k=1}^m\\left(1-\\sum_{n=1}^Wy_n^k\\right)^2 + A\\sum_{k=1}^m\\left(\\sum_{n=1}^Wny_n^k- \\sum_{v=1}^n w_{v} x_{v}^k\\right)^2 $$\u7b2c\u4e00\u9805\u304c\u30c8\u30e9\u30c3\u30af\u306e\u5bb9\u91cf\u3092\u4e00\u3064\u306b\u5b9a\u3081\u308bone-hot\u5236\u7d04\u3001\u7b2c\u4e8c\u9805\u304c\u30c8\u30e9\u30c3\u30af\u306e\u5bb9\u91cf\u3068\u8377\u7269\u306e\u91cd\u91cf\u306e\u7dcf\u548c\u3092\u7b49\u3057\u304f\u3059\u308b\u5236\u7d04\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/li>\n<li>one-hot\u5236\u7d04$$ H_B = B\\sum_{v=1}^n \\left(1- \\sum_{k=1}^{m}x_{v}^k\\right)^2 $$\u3053\u306e\u5f0f\u306f\u3001\u9867\u5ba2\u304c\u4e00\u3064\u306e\u30af\u30e9\u30b9\u30bf\u30fc\u306b\u3060\u3051\u542b\u307e\u308c\u308b\u3088\u3046\u306b\u3059\u308b\u5236\u7d04\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/li>\n<li>\u8ddd\u96e2\u306b\u95a2\u3059\u308b\u30b3\u30b9\u30c8\u95a2\u6570$$ H_C = C\\sum_{k=1}^{m}\\left(\\sum_{(u,v)\\in E}D_{uv}x_u^kx_v^k\\right)$$\u30af\u30e9\u30b9\u30bf\u30fc\u5185\u306e\u9867\u5ba2\u3069\u3046\u3057\u306e\u8ddd\u96e2\u306e\u7dcf\u548c\u3092\u8a08\u7b97\u3057\u3066\u3044\u307e\u3059\u3002\u3053\u308c\u304c\u6700\u5c0f\u306b\u306a\u308b\u3088\u3046\u306a\u89e3\u3092\u6c42\u3081\u308b\u3053\u3068\u304cCVRP\u306e\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u90e8\u5206\u306e\u76ee\u7684\u3067\u3059\u3002\u306a\u304a\u3001$X,A,B,C$\u306f\u5236\u7d04\u306e\u91cd\u307f\u3092\u6c7a\u5b9a\u3059\u308b\u305f\u3081\u306e\u5b9a\u6570\u3067\u3059\u3002<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u3044\u3056\u5b9f\u8df5\uff01\u5b9f\u969b\u306b\u3053\u306e\u5b9a\u5f0f\u5316\u3092<a href=\"https:\/\/pyqubo.readthedocs.io\/en\/latest\/\">pyqubo<\/a>\u3067\u8a18\u8ff0\u3057\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n<p>\u4eca\u56de\u89e3\u304f\u30c7\u30fc\u30bf\u306f<a href=\"http:\/\/vrp.galgos.inf.puc-rio.br\/index.php\/en\/\">\u3053\u3061\u3089<\/a>\u306e\u30b5\u30a4\u30c8\u304b\u3089\u898b\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<p>\u307e\u305a\u306f\u3001\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"err\">!<\/span><span class=\"n\">pip<\/span> <span class=\"n\">install<\/span> <span class=\"n\">numpy<\/span>\n<span class=\"err\">!<\/span><span class=\"n\">pip<\/span> <span class=\"n\">install<\/span> <span class=\"n\">matplotlib<\/span>\n<span class=\"err\">!<\/span><span class=\"n\">pip<\/span> <span class=\"n\">install<\/span> <span class=\"n\">dwave<\/span><span class=\"o\">-<\/span><span class=\"n\">ocean<\/span><span class=\"o\">-<\/span><span class=\"n\">sdk<\/span>\n<span class=\"err\">!<\/span><span class=\"n\">pip<\/span> <span class=\"n\">install<\/span> <span class=\"n\">openjij<\/span>\n<span class=\"err\">!<\/span><span class=\"n\">pip<\/span> <span class=\"n\">install<\/span> <span class=\"n\">ortools<\/span>\n<span class=\"err\">!<\/span><span class=\"n\">pip<\/span> <span class=\"n\">install<\/span> <span class=\"n\">vrplib<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"kn\">import<\/span> <span class=\"nn\">itertools<\/span>\n\n<span class=\"kn\">import<\/span> <span class=\"nn\">numpy<\/span> <span class=\"k\">as<\/span> <span class=\"nn\">np<\/span>\n<span class=\"kn\">import<\/span> <span class=\"nn\">matplotlib.pyplot<\/span> <span class=\"k\">as<\/span> <span class=\"nn\">plt<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">pyqubo<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">Array<\/span><span class=\"p\">,<\/span> <span class=\"n\">Constraint<\/span><span class=\"p\">,<\/span> <span class=\"n\">Placeholder<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001<a href=\"http:\/\/vrp.galgos.inf.puc-rio.br\/index.php\/en\/\">CVRPLIB<\/a>\u306eCMT1\u30a4\u30f3\u30bf\u30b9\u30f3\u30b9\u3092\u5b9f\u9a13\u306e\u5bfe\u8c61\u3068\u3057\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"kn\">import<\/span> <span class=\"nn\">vrplib<\/span>\n\n<span class=\"n\">instance_name<\/span> <span class=\"o\">=<\/span> <span class=\"s2\">\"CMT1\"<\/span>\n<span class=\"n\">path_instance<\/span> <span class=\"o\">=<\/span> <span class=\"s2\">\"cvrp.vrp\"<\/span>\n<span class=\"n\">path_solution<\/span> <span class=\"o\">=<\/span> <span class=\"s2\">\"cvrp.sol\"<\/span>\n\n<span class=\"n\">vrplib<\/span><span class=\"o\">.<\/span><span class=\"n\">download_instance<\/span><span class=\"p\">(<\/span><span class=\"n\">instance_name<\/span><span class=\"p\">,<\/span> <span class=\"n\">path_instance<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">vrplib<\/span><span class=\"o\">.<\/span><span class=\"n\">download_solution<\/span><span class=\"p\">(<\/span><span class=\"n\">instance_name<\/span><span class=\"p\">,<\/span> <span class=\"n\">path_solution<\/span><span class=\"p\">)<\/span>\n\n<span class=\"n\">vrp_instance<\/span> <span class=\"o\">=<\/span> <span class=\"n\">vrplib<\/span><span class=\"o\">.<\/span><span class=\"n\">read_instance<\/span><span class=\"p\">(<\/span><span class=\"n\">path_instance<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">vrp_solution<\/span> <span class=\"o\">=<\/span> <span class=\"n\">vrplib<\/span><span class=\"o\">.<\/span><span class=\"n\">read_solution<\/span><span class=\"p\">(<\/span><span class=\"n\">path_solution<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"n\">customers<\/span> <span class=\"o\">=<\/span> <span class=\"n\">vrp_instance<\/span><span class=\"p\">[<\/span><span class=\"s2\">\"node_coord\"<\/span><span class=\"p\">]<\/span>\n<span class=\"n\">customers_nodepot<\/span> <span class=\"o\">=<\/span> <span class=\"n\">customers<\/span><span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">:]<\/span>  <span class=\"c1\"># 0: depot<\/span>\n<span class=\"n\">cus_num<\/span> <span class=\"o\">=<\/span> <span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">customers_nodepot<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"c1\"># \u8ddd\u96e2\u884c\u5217\u306e\u751f\u6210<\/span>\n<span class=\"n\">D_uv<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">zeros<\/span><span class=\"p\">((<\/span><span class=\"n\">cus_num<\/span><span class=\"p\">,<\/span> <span class=\"n\">cus_num<\/span><span class=\"p\">))<\/span>\n<span class=\"k\">for<\/span> <span class=\"n\">u<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">cus_num<\/span><span class=\"p\">):<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\">v<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">u<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span><span class=\"p\">,<\/span> <span class=\"n\">cus_num<\/span><span class=\"p\">):<\/span>\n        <span class=\"n\">distance_uv<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">linalg<\/span><span class=\"o\">.<\/span><span class=\"n\">norm<\/span><span class=\"p\">(<\/span><span class=\"n\">customers_nodepot<\/span><span class=\"p\">[<\/span><span class=\"n\">u<\/span><span class=\"p\">]<\/span> <span class=\"o\">-<\/span> <span class=\"n\">customers_nodepot<\/span><span class=\"p\">[<\/span><span class=\"n\">v<\/span><span class=\"p\">])<\/span>\n        <span class=\"n\">D_uv<\/span><span class=\"p\">[<\/span><span class=\"n\">u<\/span><span class=\"p\">][<\/span><span class=\"n\">v<\/span><span class=\"p\">]<\/span> <span class=\"o\">=<\/span> <span class=\"n\">D_uv<\/span><span class=\"p\">[<\/span><span class=\"n\">v<\/span><span class=\"p\">][<\/span><span class=\"n\">u<\/span><span class=\"p\">]<\/span> <span class=\"o\">=<\/span> <span class=\"n\">distance_uv<\/span>\n\n<span class=\"n\">D_uv_normed<\/span> <span class=\"o\">=<\/span> <span class=\"n\">D_uv<\/span> <span class=\"o\">\/<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">max<\/span><span class=\"p\">(<\/span><span class=\"n\">D_uv<\/span><span class=\"p\">)<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">D_uv_normed<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>[[0.         0.14444578 0.22432224 ... 0.14444578 0.30830144 0.28268839]\n [0.14444578 0.         0.17863522 ... 0.28889157 0.24551063 0.16223248]\n [0.22432224 0.17863522 0.         ... 0.33235458 0.42298608 0.31874053]\n ...\n [0.14444578 0.28889157 0.33235458 ... 0.         0.41418987 0.41861108]\n [0.30830144 0.24551063 0.42298608 ... 0.41418987 0.         0.14061873]\n [0.28268839 0.16223248 0.31874053 ... 0.41861108 0.14061873 0.        ]]\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"n\">demands<\/span> <span class=\"o\">=<\/span> <span class=\"n\">vrp_instance<\/span><span class=\"p\">[<\/span><span class=\"s2\">\"demand\"<\/span><span class=\"p\">][<\/span><span class=\"mi\">1<\/span><span class=\"p\">:]<\/span>  <span class=\"c1\"># 0: depot<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">demands<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>[ 7 30 16  9 21 15 19 23 11  5 19 29 23 21 10 15  3 41  9 28  8  8 16 10\n 28  7 15 14  6 19 11 12 23 26 17  6  9 15 14  7 27 13 11 16 10  5 25 17\n 18 10]\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"c1\"># \u8eca\u4e21\u306e\u8a2d\u5b9a<\/span>\n<span class=\"n\">car_num<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">5<\/span>  <span class=\"c1\"># \u8eca\u306e\u53f0\u6570<\/span>\n<span class=\"n\">capa<\/span> <span class=\"o\">=<\/span> <span class=\"n\">vrp_instance<\/span><span class=\"p\">[<\/span><span class=\"s2\">\"capacity\"<\/span><span class=\"p\">]<\/span>  <span class=\"c1\"># \u30c8\u30e9\u30c3\u30af\u306e\u5bb9\u91cf<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u6b21\u306bpyqubo\u306b\u6570\u5f0f\u3092\u66f8\u3044\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"k\">def<\/span> <span class=\"nf\">make_pyqubo_model1<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">,<\/span> <span class=\"n\">cus_num<\/span><span class=\"p\">,<\/span> <span class=\"n\">capa<\/span><span class=\"p\">,<\/span> <span class=\"n\">D_uv<\/span><span class=\"p\">,<\/span> <span class=\"n\">demands<\/span><span class=\"p\">):<\/span>\n    <span class=\"n\">x<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Array<\/span><span class=\"o\">.<\/span><span class=\"n\">create<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"x\"<\/span><span class=\"p\">,<\/span> <span class=\"n\">shape<\/span><span class=\"o\">=<\/span><span class=\"p\">(<\/span><span class=\"n\">cus_num<\/span><span class=\"p\">,<\/span> <span class=\"n\">car_num<\/span><span class=\"p\">),<\/span> <span class=\"n\">vartype<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"BINARY\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">y<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Array<\/span><span class=\"o\">.<\/span><span class=\"n\">create<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"y\"<\/span><span class=\"p\">,<\/span> <span class=\"n\">shape<\/span><span class=\"o\">=<\/span><span class=\"p\">(<\/span><span class=\"n\">capa<\/span><span class=\"p\">,<\/span> <span class=\"n\">car_num<\/span><span class=\"p\">),<\/span> <span class=\"n\">vartype<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"BINARY\"<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"n\">H_A_1<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">(<\/span>\n        <span class=\"p\">[<\/span>\n            <span class=\"p\">(<\/span><span class=\"mi\">1<\/span> <span class=\"o\">-<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">([<\/span><span class=\"n\">y<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span> <span class=\"o\">-<\/span> <span class=\"mi\">1<\/span><span class=\"p\">,<\/span> <span class=\"n\">k<\/span><span class=\"p\">]<\/span> <span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"mi\">1<\/span><span class=\"p\">,<\/span> <span class=\"n\">capa<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span><span class=\"p\">)]))<\/span> <span class=\"o\">**<\/span> <span class=\"mi\">2<\/span>\n            <span class=\"k\">for<\/span> <span class=\"n\">k<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">)<\/span>\n        <span class=\"p\">]<\/span>\n    <span class=\"p\">)<\/span>\n    <span class=\"n\">H_A_2<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">(<\/span>\n        <span class=\"p\">[<\/span>\n            <span class=\"p\">(<\/span>\n                <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">([<\/span><span class=\"n\">i<\/span> <span class=\"o\">*<\/span> <span class=\"n\">y<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span> <span class=\"o\">-<\/span> <span class=\"mi\">1<\/span><span class=\"p\">,<\/span> <span class=\"n\">k<\/span><span class=\"p\">]<\/span> <span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"mi\">1<\/span><span class=\"p\">,<\/span> <span class=\"n\">capa<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span><span class=\"p\">)])<\/span>\n                <span class=\"o\">-<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">([<\/span><span class=\"n\">demands<\/span><span class=\"p\">[<\/span><span class=\"n\">v<\/span><span class=\"p\">]<\/span> <span class=\"o\">*<\/span> <span class=\"n\">x<\/span><span class=\"p\">[<\/span><span class=\"n\">v<\/span><span class=\"p\">,<\/span> <span class=\"n\">k<\/span><span class=\"p\">]<\/span> <span class=\"k\">for<\/span> <span class=\"n\">v<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">cus_num<\/span><span class=\"p\">)])<\/span>\n            <span class=\"p\">)<\/span>\n            <span class=\"o\">**<\/span> <span class=\"mi\">2<\/span>\n            <span class=\"k\">for<\/span> <span class=\"n\">k<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">)<\/span>\n        <span class=\"p\">]<\/span>\n    <span class=\"p\">)<\/span>\n    <span class=\"n\">H_B<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">(<\/span>\n        <span class=\"p\">[(<\/span><span class=\"mi\">1<\/span> <span class=\"o\">-<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">([<\/span><span class=\"n\">x<\/span><span class=\"p\">[<\/span><span class=\"n\">v<\/span><span class=\"p\">,<\/span> <span class=\"n\">k<\/span><span class=\"p\">]<\/span> <span class=\"k\">for<\/span> <span class=\"n\">k<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">)]))<\/span> <span class=\"o\">**<\/span> <span class=\"mi\">2<\/span> <span class=\"k\">for<\/span> <span class=\"n\">v<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">cus_num<\/span><span class=\"p\">)]<\/span>\n    <span class=\"p\">)<\/span>\n    <span class=\"n\">H_C<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">(<\/span>\n        <span class=\"p\">[<\/span>\n            <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">(<\/span>\n                <span class=\"p\">[<\/span>\n                    <span class=\"n\">D_uv<\/span><span class=\"p\">[<\/span><span class=\"n\">u<\/span><span class=\"p\">,<\/span> <span class=\"n\">v<\/span><span class=\"p\">]<\/span> <span class=\"o\">*<\/span> <span class=\"n\">x<\/span><span class=\"p\">[<\/span><span class=\"n\">u<\/span><span class=\"p\">,<\/span> <span class=\"n\">k<\/span><span class=\"p\">]<\/span> <span class=\"o\">*<\/span> <span class=\"n\">x<\/span><span class=\"p\">[<\/span><span class=\"n\">v<\/span><span class=\"p\">,<\/span> <span class=\"n\">k<\/span><span class=\"p\">]<\/span>\n                    <span class=\"k\">for<\/span> <span class=\"n\">u<\/span><span class=\"p\">,<\/span> <span class=\"n\">v<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">itertools<\/span><span class=\"o\">.<\/span><span class=\"n\">combinations<\/span><span class=\"p\">(<\/span><span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">cus_num<\/span><span class=\"p\">),<\/span> <span class=\"mi\">2<\/span><span class=\"p\">)<\/span>\n                <span class=\"p\">]<\/span>\n            <span class=\"p\">)<\/span>\n            <span class=\"k\">for<\/span> <span class=\"n\">k<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">)<\/span>\n        <span class=\"p\">]<\/span>\n    <span class=\"p\">)<\/span>\n\n    <span class=\"n\">H<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Placeholder<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"X\"<\/span><span class=\"p\">)<\/span> <span class=\"o\">*<\/span> <span class=\"n\">Constraint<\/span><span class=\"p\">(<\/span><span class=\"n\">H_A_1<\/span><span class=\"p\">,<\/span> <span class=\"s2\">\"H_A_1\"<\/span><span class=\"p\">)<\/span> <span class=\"o\">+<\/span> <span class=\"n\">Placeholder<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"A\"<\/span><span class=\"p\">)<\/span> <span class=\"o\">*<\/span> <span class=\"n\">Constraint<\/span><span class=\"p\">(<\/span>\n        <span class=\"n\">H_A_2<\/span><span class=\"p\">,<\/span> <span class=\"s2\">\"H_A_2\"<\/span>\n    <span class=\"p\">)<\/span>\n    <span class=\"n\">H<\/span> <span class=\"o\">=<\/span> <span class=\"n\">H<\/span> <span class=\"o\">+<\/span> <span class=\"n\">Placeholder<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"B\"<\/span><span class=\"p\">)<\/span> <span class=\"o\">*<\/span> <span class=\"n\">Constraint<\/span><span class=\"p\">(<\/span><span class=\"n\">H_B<\/span><span class=\"p\">,<\/span> <span class=\"s2\">\"H_B\"<\/span><span class=\"p\">)<\/span> <span class=\"o\">+<\/span> <span class=\"n\">Placeholder<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"C\"<\/span><span class=\"p\">)<\/span> <span class=\"o\">*<\/span> <span class=\"n\">H_C<\/span>\n\n    <span class=\"k\">return<\/span> <span class=\"n\">H<\/span><span class=\"o\">.<\/span><span class=\"n\">compile<\/span><span class=\"p\">()<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"n\">model1<\/span> <span class=\"o\">=<\/span> <span class=\"n\">make_pyqubo_model1<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">,<\/span> <span class=\"n\">cus_num<\/span><span class=\"p\">,<\/span> <span class=\"n\">capa<\/span><span class=\"p\">,<\/span> <span class=\"n\">D_uv<\/span><span class=\"p\">,<\/span> <span class=\"n\">demands<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"n\">feed_dict1<\/span> <span class=\"o\">=<\/span> <span class=\"nb\">dict<\/span><span class=\"p\">(<\/span><span class=\"n\">X<\/span><span class=\"o\">=<\/span><span class=\"mf\">2500.0<\/span><span class=\"p\">,<\/span> <span class=\"n\">A<\/span><span class=\"o\">=<\/span><span class=\"mf\">50.0<\/span><span class=\"p\">,<\/span> <span class=\"n\">B<\/span><span class=\"o\">=<\/span><span class=\"mf\">2500.0<\/span><span class=\"p\">,<\/span> <span class=\"n\">C<\/span><span class=\"o\">=<\/span><span class=\"mf\">1.0<\/span><span class=\"p\">)<\/span>  <span class=\"c1\"># \u3044\u308d\u3044\u308d\u4fc2\u6570\u3092\u8abf\u6574\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044<\/span>\n<span class=\"n\">bqm1<\/span> <span class=\"o\">=<\/span> <span class=\"n\">model1<\/span><span class=\"o\">.<\/span><span class=\"n\">to_bqm<\/span><span class=\"p\">(<\/span><span class=\"n\">feed_dict<\/span><span class=\"o\">=<\/span><span class=\"n\">feed_dict1<\/span><span class=\"p\">)<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"num_variables:\"<\/span><span class=\"p\">,<\/span> <span class=\"n\">bqm1<\/span><span class=\"o\">.<\/span><span class=\"n\">num_variables<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>num_variables: 1050\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"c1\"># SA\u306e\u5834\u5408<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">neal<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">SimulatedAnnealingSampler<\/span>\n\n<span class=\"n\">sampler<\/span> <span class=\"o\">=<\/span> <span class=\"n\">SimulatedAnnealingSampler<\/span><span class=\"p\">()<\/span>\n\n<span class=\"c1\"># D-Wave\u30de\u30b7\u30f3\u306e\u5834\u5408<\/span>\n<span class=\"c1\"># from dwave.system import DWaveSampler, EmbeddingComposite<\/span>\n<span class=\"c1\"># sampler_config = dict(solver=\"Advantage_system6.2\", token=\"YOUR_TOKEN\")<\/span>\n<span class=\"c1\"># sampler = EmbeddingComposite(DWaveSampler(**sampler_config))<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"n\">sampler_params1<\/span> <span class=\"o\">=<\/span> <span class=\"nb\">dict<\/span><span class=\"p\">(<\/span><span class=\"n\">num_reads<\/span><span class=\"o\">=<\/span><span class=\"mi\">10<\/span><span class=\"p\">,<\/span> <span class=\"n\">num_sweeps<\/span><span class=\"o\">=<\/span><span class=\"mi\">10000<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">sampleset1<\/span> <span class=\"o\">=<\/span> <span class=\"n\">sampler<\/span><span class=\"o\">.<\/span><span class=\"n\">sample<\/span><span class=\"p\">(<\/span><span class=\"n\">bqm1<\/span><span class=\"p\">,<\/span> <span class=\"o\">**<\/span><span class=\"n\">sampler_params1<\/span><span class=\"p\">)<\/span><span class=\"o\">.<\/span><span class=\"n\">aggregate<\/span><span class=\"p\">()<\/span>\n<span class=\"n\">sampleset1<\/span><span class=\"o\">.<\/span><span class=\"n\">to_pandas_dataframe<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">head<\/span><span class=\"p\">()<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt output_prompt\">Out[\u00a0]:<\/div>\n<div class=\"output_html rendered_html output_subarea output_execute_result\">\n<div id=\"df-382d01eb-012b-4dda-87aa-d87e2d20698b\">\n<div class=\"colab-df-container\">\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {<br \/>        vertical-align: middle;<br \/>    }<\/p>\n<p>    .dataframe tbody tr th {<br \/>        vertical-align: top;<br \/>    }<\/p>\n<p>    .dataframe thead th {<br \/>        text-align: right;<br \/>    }<br \/><\/style>\n<table border=\"1\" class=\"dataframe\">\n<thead>\n<tr style=\"text-align: right;\">\n<th><\/th>\n<th>x[0][0]<\/th>\n<th>x[0][1]<\/th>\n<th>x[0][2]<\/th>\n<th>x[0][3]<\/th>\n<th>x[0][4]<\/th>\n<th>x[10][0]<\/th>\n<th>x[10][1]<\/th>\n<th>x[10][2]<\/th>\n<th>x[10][3]<\/th>\n<th>x[10][4]<\/th>\n<th>&#8230;<\/th>\n<th>y[99][2]<\/th>\n<th>y[99][3]<\/th>\n<th>y[99][4]<\/th>\n<th>y[9][0]<\/th>\n<th>y[9][1]<\/th>\n<th>y[9][2]<\/th>\n<th>y[9][3]<\/th>\n<th>y[9][4]<\/th>\n<th>energy<\/th>\n<th>num_occurrences<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>0<\/th>\n<td>1<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>&#8230;<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>124523.128859<\/td>\n<td>1<\/td>\n<\/tr>\n<tr>\n<th>1<\/th>\n<td>0<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>&#8230;<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>175790.620470<\/td>\n<td>1<\/td>\n<\/tr>\n<tr>\n<th>2<\/th>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>&#8230;<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>191027.912279<\/td>\n<td>1<\/td>\n<\/tr>\n<tr>\n<th>3<\/th>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>&#8230;<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>145609.056330<\/td>\n<td>1<\/td>\n<\/tr>\n<tr>\n<th>4<\/th>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>1<\/td>\n<td>&#8230;<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>144379.351831<\/td>\n<td>1<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>5 rows \u00d7 1052 columns<\/p>\n<\/div>\n<p><button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-382d01eb-012b-4dda-87aa-d87e2d20698b')\" title=\"Convert this dataframe to an interactive table.\" style=\"display: none;\"><\/button><\/p>\n<p><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" height=\"24px\" viewbox=\"0 0 24 24\" width=\"24px\"><\/svg>\n<path d=\"M0 0h24v24H0V0z\" fill=\"none\"><\/path> <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"><\/path><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"><\/path>\n<style>\n    .colab-df-container {<br \/>      display:flex;<br \/>      flex-wrap:wrap;<br \/>      gap: 12px;<br \/>    }<\/p>\n<p>    .colab-df-convert {<br \/>      background-color: #E8F0FE;<br \/>      border: none;<br \/>      border-radius: 50%;<br \/>      cursor: pointer;<br \/>      display: none;<br \/>      fill: #1967D2;<br \/>      height: 32px;<br \/>      padding: 0 0 0 0;<br \/>      width: 32px;<br \/>    }<\/p>\n<p>    .colab-df-convert:hover {<br \/>      background-color: #E2EBFA;<br \/>      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);<br \/>      fill: #174EA6;<br \/>    }<\/p>\n<p>    [theme=dark] .colab-df-convert {<br \/>      background-color: #3B4455;<br \/>      fill: #D2E3FC;<br \/>    }<\/p>\n<p>    [theme=dark] .colab-df-convert:hover {<br \/>      background-color: #434B5C;<br \/>      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);<br \/>      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));<br \/>      fill: #FFFFFF;<br \/>    }<br \/>  <\/style>\n<p><script>\n        const buttonEl =\n          document.querySelector('#df-382d01eb-012b-4dda-87aa-d87e2d20698b button.colab-df-convert');\n        buttonEl.style.display =\n          google.colab.kernel.accessAllowed ? 'block' : 'none';<\/p>\n<p>        async function convertToInteractive(key) {\n          const element = document.querySelector('#df-382d01eb-012b-4dda-87aa-d87e2d20698b');\n          const dataTable =\n            await google.colab.kernel.invokeFunction('convertToInteractive',\n                                                     [key], {});\n          if (!dataTable) return;<\/p>\n<p>          const docLinkHtml = 'Like what you see? Visit the ' +\n            '<a target=\"_blank\" href=https:\/\/colab.research.google.com\/notebooks\/data_table.ipynb>data table notebook<\/a>'\n            + ' to learn more about interactive tables.';\n          element.innerHTML = '';\n          dataTable['output_type'] = 'display_data';\n          await google.colab.output.renderOutput(dataTable, element);\n          const docLink = document.createElement('div');\n          docLink.innerHTML = docLinkHtml;\n          element.appendChild(docLink);\n        }\n      <\/script><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>one-hot\u5236\u7d04\u3084\u5bb9\u91cf\u5236\u7d04\u3092\u6e80\u305f\u3057\u3066\u3044\u308b\u304b\u78ba\u8a8d\u3057\u307e\u3057\u3087\u3046\u3002False\u304c\u8868\u793a\u3055\u308c\u308b\u5834\u5408\u306f\u305d\u306e\u5236\u7d04\u306f\u6e80\u305f\u3055\u308c\u3066\u3044\u306a\u3044\u3068\u3044\u3046\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"c1\"># \u5236\u7d04\u3092\u6e80\u305f\u3057\u3066\u3044\u308b\u304b\u3069\u3046\u304b\u3092\u78ba\u8a8d\u3059\u308b<\/span>\n<span class=\"n\">decoded_samples1<\/span> <span class=\"o\">=<\/span> <span class=\"n\">model1<\/span><span class=\"o\">.<\/span><span class=\"n\">decode_sampleset<\/span><span class=\"p\">(<\/span><span class=\"n\">sampleset1<\/span><span class=\"p\">,<\/span> <span class=\"n\">feed_dict1<\/span><span class=\"p\">)<\/span>\n\n<span class=\"k\">for<\/span> <span class=\"n\">sample<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">decoded_samples1<\/span><span class=\"p\">:<\/span>\n    <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">sample<\/span><span class=\"o\">.<\/span><span class=\"n\">energy<\/span><span class=\"p\">)<\/span>\n    <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">sample<\/span><span class=\"o\">.<\/span><span class=\"n\">constraints<\/span><span class=\"p\">(<\/span><span class=\"n\">only_broken<\/span><span class=\"o\">=<\/span><span class=\"kc\">True<\/span><span class=\"p\">))<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>124523.12880269997\n{'H_A_2': (False, 75.0), 'H_B': (False, 16.0), 'H_A_1': (False, 27.0)}\n144379.35184135474\n{'H_A_2': (False, 47.0), 'H_B': (False, 25.0), 'H_A_1': (False, 25.0)}\n145609.0562772788\n{'H_A_2': (False, 64.0), 'H_B': (False, 28.0), 'H_A_1': (False, 22.0)}\n151658.67309271824\n{'H_A_2': (False, 33.0), 'H_B': (False, 26.0), 'H_A_1': (False, 27.0)}\n173256.15261312574\n{'H_A_2': (False, 51.0), 'H_B': (False, 35.0), 'H_A_1': (False, 25.0)}\n173511.7737898957\n{'H_A_2': (False, 69.0), 'H_B': (False, 33.0), 'H_A_1': (False, 27.0)}\n175790.62046944257\n{'H_A_2': (False, 61.0), 'H_B': (False, 31.0), 'H_A_1': (False, 30.0)}\n191027.9123360645\n{'H_A_2': (False, 74.0), 'H_B': (False, 30.0), 'H_A_1': (False, 37.0)}\n194297.09123693034\n{'H_A_2': (False, 46.0), 'H_B': (False, 30.0), 'H_A_1': (False, 39.0)}\n216092.85363079607\n{'H_A_2': (False, 80.0), 'H_B': (False, 45.0), 'H_A_1': (False, 30.0)}\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u4f55\u56de\u304b\u5236\u7d04\u9805\u306e\u4fc2\u6570\u3092\u5909\u3048\u3066\u5b9f\u884c\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002\u3042\u308b\u771f\u5b9f\u306b\u6c17\u3065\u304f\u306f\u305a\u3067\u3059\u3002<\/p>\n<p>\u305d\u3046\u3002\u3059\u3079\u3066\u306e\u5236\u7d04\u9805\u304c0\u306b\u306a\u308b\u3088\u3046\u306b\u4fc2\u6570\u3092\u8abf\u6574\u3059\u308b\u3053\u3068\u304c\u96e3\u3057\u3044\u3068\u3044\u3046\u3053\u3068\u306b\uff01<\/p>\n<p>\uff08\u304b\u306a\u308a\u306e\u6642\u9593\u8a66\u3057\u307e\u3057\u305f\u304c\u3001\u7b46\u8005\u306f\u4e8c\u3064\u306e\u5236\u7d04\u9805\u30920\u306b\u3059\u308b\u3053\u3068\u3057\u304b\u3067\u304d\u307e\u305b\u3093\u3067\u3057\u305f&#8230;\uff09<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u8003\u3048\u3089\u308c\u308b\u539f\u56e0\u3068\u3057\u3066\u306f\u4ee5\u4e0b\u306e\u3053\u3068\u304c\u6319\u3052\u3089\u308c\u307e\u3059\u3002<\/p>\n<ol>\n<li>3\u3064\u306e\u4fc2\u6570\u3092\u30d0\u30e9\u30f3\u30b9\u3088\u304f\u8abf\u6574\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3002<\/li>\n<li>\u9867\u5ba2\u306e\u8981\u6c42\u91cf\u3084\u9867\u5ba2\u540c\u58eb\u306e\u8ddd\u96e2\u306b\u95a2\u3057\u3066\u30aa\u30fc\u30c0\u30fc\u304c\u7570\u306a\u308b\u3002<\/li>\n<li>\u305d\u3082\u305d\u3082\u4e0d\u7b49\u5f0f\u5236\u7d04\u3092\u305d\u306e\u307e\u307e\u89e3\u304f\u3053\u3068\u306f\u96e3\u3057\u3044\u3002<\/li>\n<\/ol>\n<p>\u3053\u306e\u6bb5\u968e\u3067\u3001\u8ad6\u6587\u306b\u8f09\u3063\u3066\u3044\u305f\u5b9a\u5f0f\u5316\u3067\u89e3\u304f\u3053\u3068\u306f\u96e3\u3057\u3044\u3068\u3044\u3046\u3053\u3068\u306b\u306a\u308a\u3001\u65b0\u305f\u306a\u624b\u6cd5\u3092\u53d6\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3057\u305f\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h4><span class=\"ez-toc-section\" id=\"Part2_%E5%AE%B9%E9%87%8F%E3%81%AE%E5%B9%B3%E5%9D%87%E5%80%A4%E3%81%AB%E5%AF%84%E3%81%9B%E3%82%8B%E6%89%8B%E6%B3%95\"><\/span>Part2 \u5bb9\u91cf\u306e\u5e73\u5747\u5024\u306b\u5bc4\u305b\u308b\u624b\u6cd5<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u6b21\u306b\u7528\u3044\u305f\u624b\u6cd5\u304c2\u3064\u76ee\u306e\u8ad6\u6587\u306b\u8f09\u3063\u3066\u3044\u308b\u5b9a\u5f0f\u5316\u3067\u3059\u3002<\/p>\n<p>Part1\u3068\u7570\u306a\u308b\u306e\u306f\u5bb9\u91cf\u5236\u7d04\u306b\u95a2\u3059\u308b\u90e8\u5206\u3067\u3059\u3002\u5177\u4f53\u7684\u306b\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<p>\u91cd\u307f\u306b\u95a2\u3059\u308b\u5236\u7d04<br \/>\n$$ H_D = D\\sum_{k=1}^m\\left(H_{D&#8217;} &#8211; \\sum_{v=1}^n w_v x_v^k \\right)^2$$<\/p>\n<p>$$ H_{D&#8217;} = \\frac{1}{m}\\sum_{k=1}^m\\left(\\sum_{v=1}^n w_v x_v^k \\right) $$<\/p>\n<p>\u3053\u308c\u306f\u3001\u3059\u3079\u3066\u306e\u8377\u7269\u306e\u7dcf\u548c\u3092\u8eca\u306e\u53f0\u6570\u3067\u5272\u3063\u305f\u3082\u306e\uff08\u8eca\u306b\u5272\u308a\u5f53\u3066\u308b\u3079\u304d\u5bb9\u91cf\u306e\u5e73\u5747\u5024\uff09\u306b\u8fd1\u3065\u3051\u308b\u5236\u7d04\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002\uff08\u3053\u306e\u5236\u7d04\u306f0\u306b\u306f\u306a\u3089\u306a\u3044\u3053\u3068\u306b\u6ce8\u610f\u304c\u5fc5\u8981\uff09<\/p>\n<p>\u3053\u306e$H_D$\u3092\u5148\u307b\u3069\u306e$H_A$\u3068\u7f6e\u304d\u63db\u3048\u308b\u5f62\u3067\u89e3\u3044\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"c1\"># \u9867\u5ba2\u306e\u8981\u6c42\u91cf\u306b\u95a2\u3057\u3066\u6b63\u898f\u5316\u3059\u308b<\/span>\n<span class=\"n\">demands_normed<\/span> <span class=\"o\">=<\/span> <span class=\"n\">demands<\/span> <span class=\"o\">\/<\/span> <span class=\"n\">capa<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">demands_normed<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>[0.04375 0.1875  0.1     0.05625 0.13125 0.09375 0.11875 0.14375 0.06875\n 0.03125 0.11875 0.18125 0.14375 0.13125 0.0625  0.09375 0.01875 0.25625\n 0.05625 0.175   0.05    0.05    0.1     0.0625  0.175   0.04375 0.09375\n 0.0875  0.0375  0.11875 0.06875 0.075   0.14375 0.1625  0.10625 0.0375\n 0.05625 0.09375 0.0875  0.04375 0.16875 0.08125 0.06875 0.1     0.0625\n 0.03125 0.15625 0.10625 0.1125  0.0625 ]\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"k\">def<\/span> <span class=\"nf\">make_pyqubo_model2<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">,<\/span> <span class=\"n\">cus_num<\/span><span class=\"p\">,<\/span> <span class=\"n\">capa<\/span><span class=\"p\">,<\/span> <span class=\"n\">D_uv_normed<\/span><span class=\"p\">,<\/span> <span class=\"n\">demands<\/span><span class=\"p\">):<\/span>\n    <span class=\"n\">x<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Array<\/span><span class=\"o\">.<\/span><span class=\"n\">create<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"x\"<\/span><span class=\"p\">,<\/span> <span class=\"n\">shape<\/span><span class=\"o\">=<\/span><span class=\"p\">(<\/span><span class=\"n\">cus_num<\/span><span class=\"p\">,<\/span> <span class=\"n\">car_num<\/span><span class=\"p\">),<\/span> <span class=\"n\">vartype<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"BINARY\"<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"n\">H_B<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">(<\/span>\n        <span class=\"p\">[(<\/span><span class=\"mi\">1<\/span> <span class=\"o\">-<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">([<\/span><span class=\"n\">x<\/span><span class=\"p\">[<\/span><span class=\"n\">v<\/span><span class=\"p\">,<\/span> <span class=\"n\">k<\/span><span class=\"p\">]<\/span> <span class=\"k\">for<\/span> <span class=\"n\">k<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">)]))<\/span> <span class=\"o\">**<\/span> <span class=\"mi\">2<\/span> <span class=\"k\">for<\/span> <span class=\"n\">v<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">cus_num<\/span><span class=\"p\">)]<\/span>\n    <span class=\"p\">)<\/span>  <span class=\"c1\"># one-hot<\/span>\n    <span class=\"n\">H_C<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">(<\/span>\n        <span class=\"p\">[<\/span>\n            <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">(<\/span>\n                <span class=\"p\">[<\/span>\n                    <span class=\"n\">D_uv_normed<\/span><span class=\"p\">[<\/span><span class=\"n\">u<\/span><span class=\"p\">,<\/span> <span class=\"n\">v<\/span><span class=\"p\">]<\/span> <span class=\"o\">*<\/span> <span class=\"n\">x<\/span><span class=\"p\">[<\/span><span class=\"n\">u<\/span><span class=\"p\">,<\/span> <span class=\"n\">k<\/span><span class=\"p\">]<\/span> <span class=\"o\">*<\/span> <span class=\"n\">x<\/span><span class=\"p\">[<\/span><span class=\"n\">v<\/span><span class=\"p\">,<\/span> <span class=\"n\">k<\/span><span class=\"p\">]<\/span>\n                    <span class=\"k\">for<\/span> <span class=\"n\">u<\/span><span class=\"p\">,<\/span> <span class=\"n\">v<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">itertools<\/span><span class=\"o\">.<\/span><span class=\"n\">combinations<\/span><span class=\"p\">(<\/span><span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">cus_num<\/span><span class=\"p\">),<\/span> <span class=\"mi\">2<\/span><span class=\"p\">)<\/span>\n                <span class=\"p\">]<\/span>\n            <span class=\"p\">)<\/span>\n            <span class=\"k\">for<\/span> <span class=\"n\">k<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">)<\/span>\n        <span class=\"p\">]<\/span>\n    <span class=\"p\">)<\/span>  <span class=\"c1\"># \u8ddd\u96e2<\/span>\n    <span class=\"n\">H_D<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">(<\/span>\n        <span class=\"p\">[<\/span>\n            <span class=\"p\">(<\/span>\n                <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">(<\/span><span class=\"n\">demands<\/span><span class=\"p\">)<\/span> <span class=\"o\">\/<\/span> <span class=\"n\">car_num<\/span>\n                <span class=\"o\">-<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">([<\/span><span class=\"n\">demands<\/span><span class=\"p\">[<\/span><span class=\"n\">v<\/span><span class=\"p\">]<\/span> <span class=\"o\">*<\/span> <span class=\"n\">x<\/span><span class=\"p\">[<\/span><span class=\"n\">v<\/span><span class=\"p\">,<\/span> <span class=\"n\">k<\/span><span class=\"p\">]<\/span> <span class=\"k\">for<\/span> <span class=\"n\">v<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">cus_num<\/span><span class=\"p\">)])<\/span>\n            <span class=\"p\">)<\/span>\n            <span class=\"o\">**<\/span> <span class=\"mi\">2<\/span>\n            <span class=\"k\">for<\/span> <span class=\"n\">k<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">)<\/span>\n        <span class=\"p\">]<\/span>\n    <span class=\"p\">)<\/span>  <span class=\"c1\"># balance<\/span>\n\n    <span class=\"n\">H<\/span> <span class=\"o\">=<\/span> <span class=\"p\">(<\/span>\n        <span class=\"n\">Placeholder<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"B\"<\/span><span class=\"p\">)<\/span> <span class=\"o\">*<\/span> <span class=\"n\">Constraint<\/span><span class=\"p\">(<\/span><span class=\"n\">H_B<\/span><span class=\"p\">,<\/span> <span class=\"s2\">\"H_B\"<\/span><span class=\"p\">)<\/span>\n        <span class=\"o\">+<\/span> <span class=\"n\">Placeholder<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"C\"<\/span><span class=\"p\">)<\/span> <span class=\"o\">*<\/span> <span class=\"n\">H_C<\/span>\n        <span class=\"o\">+<\/span> <span class=\"n\">Placeholder<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"D\"<\/span><span class=\"p\">)<\/span> <span class=\"o\">*<\/span> <span class=\"n\">H_D<\/span>\n    <span class=\"p\">)<\/span>\n    <span class=\"k\">return<\/span> <span class=\"n\">H<\/span><span class=\"o\">.<\/span><span class=\"n\">compile<\/span><span class=\"p\">()<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"n\">model2<\/span> <span class=\"o\">=<\/span> <span class=\"n\">make_pyqubo_model2<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">,<\/span> <span class=\"n\">cus_num<\/span><span class=\"p\">,<\/span> <span class=\"n\">capa<\/span><span class=\"p\">,<\/span> <span class=\"n\">D_uv_normed<\/span><span class=\"p\">,<\/span> <span class=\"n\">demands_normed<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"n\">feed_dict2<\/span> <span class=\"o\">=<\/span> <span class=\"nb\">dict<\/span><span class=\"p\">(<\/span><span class=\"n\">B<\/span><span class=\"o\">=<\/span><span class=\"mi\">3<\/span><span class=\"p\">,<\/span> <span class=\"n\">C<\/span><span class=\"o\">=<\/span><span class=\"mi\">1<\/span><span class=\"p\">,<\/span> <span class=\"n\">D<\/span><span class=\"o\">=<\/span><span class=\"mi\">100<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">bqm2<\/span> <span class=\"o\">=<\/span> <span class=\"n\">model2<\/span><span class=\"o\">.<\/span><span class=\"n\">to_bqm<\/span><span class=\"p\">(<\/span><span class=\"n\">feed_dict<\/span><span class=\"o\">=<\/span><span class=\"n\">feed_dict2<\/span><span class=\"p\">)<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"num_variables:\"<\/span><span class=\"p\">,<\/span> <span class=\"n\">bqm2<\/span><span class=\"o\">.<\/span><span class=\"n\">num_variables<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>num_variables: 250\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u4e0a\u306e\u30b3\u30fc\u30c9\u306e\u5b9f\u884c\u7d50\u679c\u304b\u3089\u88dc\u52a9\u5909\u6570\u304c\u306a\u3044\u305f\u3081\u3001\u5909\u6570\u306e\u7dcf\u6570\u304c\u4e00\u3064\u76ee\u306e\u624b\u6cd5\u3068\u6bd4\u8f03\u3057\u3066\u5c11\u306a\u304f\u306a\u3063\u3066\u3044\u308b\u3053\u3068\u304c\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>50\u306e\u30c7\u30fc\u30bf\u306b\u3064\u3044\u3066\u306fB=3,C=1,D=100\u306b\u3059\u308b\u3068\u5bb9\u91cf\u5236\u7d04\u3092\u6e80\u305f\u3057\u305f\u3088\u3044\u7d50\u679c\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"c1\"># SA\u306e\u5834\u5408<\/span>\n<span class=\"n\">sampler<\/span> <span class=\"o\">=<\/span> <span class=\"n\">SimulatedAnnealingSampler<\/span><span class=\"p\">()<\/span>\n\n<span class=\"c1\"># D-Wave\u30de\u30b7\u30f3\u306e\u5834\u5408 \u30b5\u30a4\u30ba50\u3060\u3068\u5927\u304d\u3044\u305f\u3081\u89e3\u3051\u306a\u3044<\/span>\n<span class=\"c1\"># from dwave.system import DWaveSampler, EmbeddingComposite<\/span>\n<span class=\"c1\"># sampler_config = dict(solver=\"Advantage_system6.2\", token=\"\")<\/span>\n<span class=\"c1\"># sampler = EmbeddingComposite(DWaveSampler(**sampler_config))<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"n\">sampler_params2<\/span> <span class=\"o\">=<\/span> <span class=\"nb\">dict<\/span><span class=\"p\">(<\/span><span class=\"n\">num_reads<\/span><span class=\"o\">=<\/span><span class=\"mi\">10<\/span><span class=\"p\">,<\/span> <span class=\"n\">num_sweeps<\/span><span class=\"o\">=<\/span><span class=\"mi\">10000<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">sampleset2<\/span> <span class=\"o\">=<\/span> <span class=\"n\">sampler<\/span><span class=\"o\">.<\/span><span class=\"n\">sample<\/span><span class=\"p\">(<\/span><span class=\"n\">bqm2<\/span><span class=\"p\">,<\/span> <span class=\"o\">**<\/span><span class=\"n\">sampler_params2<\/span><span class=\"p\">)<\/span><span class=\"o\">.<\/span><span class=\"n\">aggregate<\/span><span class=\"p\">()<\/span>\n<span class=\"n\">sampleset1<\/span><span class=\"o\">.<\/span><span class=\"n\">to_pandas_dataframe<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">head<\/span><span class=\"p\">()<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt output_prompt\">Out[\u00a0]:<\/div>\n<div class=\"output_html rendered_html output_subarea output_execute_result\">\n<div id=\"df-b21aefa3-02aa-4af3-8919-e21363581ad4\">\n<div class=\"colab-df-container\">\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {<br \/>        vertical-align: middle;<br \/>    }<\/p>\n<p>    .dataframe tbody tr th {<br \/>        vertical-align: top;<br \/>    }<\/p>\n<p>    .dataframe thead th {<br \/>        text-align: right;<br \/>    }<br \/><\/style>\n<table border=\"1\" class=\"dataframe\">\n<thead>\n<tr style=\"text-align: right;\">\n<th><\/th>\n<th>x[0][0]<\/th>\n<th>x[0][1]<\/th>\n<th>x[0][2]<\/th>\n<th>x[0][3]<\/th>\n<th>x[0][4]<\/th>\n<th>x[10][0]<\/th>\n<th>x[10][1]<\/th>\n<th>x[10][2]<\/th>\n<th>x[10][3]<\/th>\n<th>x[10][4]<\/th>\n<th>&#8230;<\/th>\n<th>y[99][2]<\/th>\n<th>y[99][3]<\/th>\n<th>y[99][4]<\/th>\n<th>y[9][0]<\/th>\n<th>y[9][1]<\/th>\n<th>y[9][2]<\/th>\n<th>y[9][3]<\/th>\n<th>y[9][4]<\/th>\n<th>energy<\/th>\n<th>num_occurrences<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>0<\/th>\n<td>1<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>&#8230;<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>124523.128859<\/td>\n<td>1<\/td>\n<\/tr>\n<tr>\n<th>1<\/th>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>1<\/td>\n<td>&#8230;<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>144379.351831<\/td>\n<td>1<\/td>\n<\/tr>\n<tr>\n<th>2<\/th>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>&#8230;<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>145609.056330<\/td>\n<td>1<\/td>\n<\/tr>\n<tr>\n<th>3<\/th>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>&#8230;<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>151658.673178<\/td>\n<td>1<\/td>\n<\/tr>\n<tr>\n<th>4<\/th>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>1<\/td>\n<td>&#8230;<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>173256.152676<\/td>\n<td>1<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>5 rows \u00d7 1052 columns<\/p>\n<\/div>\n<p><button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b21aefa3-02aa-4af3-8919-e21363581ad4')\" title=\"Convert this dataframe to an interactive table.\" style=\"display: none;\"><\/button><\/p>\n<p><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" height=\"24px\" viewbox=\"0 0 24 24\" width=\"24px\"><\/svg>\n<path d=\"M0 0h24v24H0V0z\" fill=\"none\"><\/path> <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"><\/path><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"><\/path>\n<style>\n    .colab-df-container {<br \/>      display:flex;<br \/>      flex-wrap:wrap;<br \/>      gap: 12px;<br \/>    }<\/p>\n<p>    .colab-df-convert {<br \/>      background-color: #E8F0FE;<br \/>      border: none;<br \/>      border-radius: 50%;<br \/>      cursor: pointer;<br \/>      display: none;<br \/>      fill: #1967D2;<br \/>      height: 32px;<br \/>      padding: 0 0 0 0;<br \/>      width: 32px;<br \/>    }<\/p>\n<p>    .colab-df-convert:hover {<br \/>      background-color: #E2EBFA;<br \/>      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);<br \/>      fill: #174EA6;<br \/>    }<\/p>\n<p>    [theme=dark] .colab-df-convert {<br \/>      background-color: #3B4455;<br \/>      fill: #D2E3FC;<br \/>    }<\/p>\n<p>    [theme=dark] .colab-df-convert:hover {<br \/>      background-color: #434B5C;<br \/>      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);<br \/>      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));<br \/>      fill: #FFFFFF;<br \/>    }<br \/>  <\/style>\n<p><script>\n        const buttonEl =\n          document.querySelector('#df-b21aefa3-02aa-4af3-8919-e21363581ad4 button.colab-df-convert');\n        buttonEl.style.display =\n          google.colab.kernel.accessAllowed ? 'block' : 'none';<\/p>\n<p>        async function convertToInteractive(key) {\n          const element = document.querySelector('#df-b21aefa3-02aa-4af3-8919-e21363581ad4');\n          const dataTable =\n            await google.colab.kernel.invokeFunction('convertToInteractive',\n                                                     [key], {});\n          if (!dataTable) return;<\/p>\n<p>          const docLinkHtml = 'Like what you see? Visit the ' +\n            '<a target=\"_blank\" href=https:\/\/colab.research.google.com\/notebooks\/data_table.ipynb>data table notebook<\/a>'\n            + ' to learn more about interactive tables.';\n          element.innerHTML = '';\n          dataTable['output_type'] = 'display_data';\n          await google.colab.output.renderOutput(dataTable, element);\n          const docLink = document.createElement('div');\n          docLink.innerHTML = docLinkHtml;\n          element.appendChild(docLink);\n        }\n      <\/script><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"n\">decoded_samples2<\/span> <span class=\"o\">=<\/span> <span class=\"n\">model2<\/span><span class=\"o\">.<\/span><span class=\"n\">decode_sampleset<\/span><span class=\"p\">(<\/span><span class=\"n\">sampleset2<\/span><span class=\"p\">,<\/span> <span class=\"n\">feed_dict2<\/span><span class=\"p\">)<\/span>\n<span class=\"k\">for<\/span> <span class=\"n\">sample<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">decoded_samples2<\/span><span class=\"p\">:<\/span>\n    <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">sample<\/span><span class=\"o\">.<\/span><span class=\"n\">energy<\/span><span class=\"p\">)<\/span>\n    <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">sample<\/span><span class=\"o\">.<\/span><span class=\"n\">constraints<\/span><span class=\"p\">(<\/span><span class=\"n\">only_broken<\/span><span class=\"o\">=<\/span><span class=\"kc\">True<\/span><span class=\"p\">))<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>42.75833681104177\n{}\n42.75833681104177\n{}\n42.75833681104177\n{}\n43.31782178360663\n{}\n44.5085063285926\n{}\n45.84433192465008\n{}\n46.07861765315647\n{}\n46.21355565055228\n{}\n46.919144259469476\n{}\n47.258181302545495\n{}\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u7b46\u8005\u304c\u5b9f\u884c\u3057\u305f\u969b\u306f\u4e00\u756a\u826f\u3044\u3082\u306e\u3067\u30a8\u30cd\u30eb\u30ae\u30fc\u304c\u7d0442.8\u3068\u3044\u3046\u7d50\u679c\u304c\u5f97\u3089\u308c\u307e\u3057\u305f\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"k\">def<\/span> <span class=\"nf\">sample_to_assignment<\/span><span class=\"p\">(<\/span><span class=\"n\">sample<\/span><span class=\"p\">,<\/span> <span class=\"n\">car_num<\/span><span class=\"p\">,<\/span> <span class=\"n\">cus_num<\/span><span class=\"p\">):<\/span>\n    <span class=\"n\">assignment<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\">m<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">cus_num<\/span><span class=\"p\">):<\/span>\n        <span class=\"k\">for<\/span> <span class=\"n\">n<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">):<\/span>\n            <span class=\"k\">if<\/span> <span class=\"n\">sample<\/span><span class=\"p\">[<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"x[<\/span><span class=\"si\">{<\/span><span class=\"n\">m<\/span><span class=\"si\">}<\/span><span class=\"s2\">][<\/span><span class=\"si\">{<\/span><span class=\"n\">n<\/span><span class=\"si\">}<\/span><span class=\"s2\">]\"<\/span><span class=\"p\">]<\/span> <span class=\"o\">==<\/span> <span class=\"mi\">1<\/span><span class=\"p\">:<\/span>\n                <span class=\"n\">assignment<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">((<\/span><span class=\"n\">m<\/span><span class=\"p\">,<\/span> <span class=\"n\">n<\/span><span class=\"p\">))<\/span>\n    <span class=\"k\">return<\/span> <span class=\"n\">assignment<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"n\">sample2<\/span> <span class=\"o\">=<\/span> <span class=\"n\">decoded_samples2<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">]<\/span>\n<span class=\"n\">assignment2<\/span> <span class=\"o\">=<\/span> <span class=\"n\">sample_to_assignment<\/span><span class=\"p\">(<\/span><span class=\"n\">sample2<\/span><span class=\"o\">.<\/span><span class=\"n\">sample<\/span><span class=\"p\">,<\/span> <span class=\"n\">car_num<\/span><span class=\"p\">,<\/span> <span class=\"n\">cus_num<\/span><span class=\"p\">)<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">assignment2<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>[(0, 4), (1, 4), (2, 4), (3, 0), (4, 3), (5, 2), (6, 2), (7, 2), (8, 1), (9, 1), (10, 1), (11, 3), (12, 0), (13, 2), (14, 3), (15, 1), (16, 3), (17, 0), (18, 0), (19, 4), (20, 1), (21, 4), (22, 2), (23, 2), (24, 0), (25, 2), (26, 2), (27, 4), (28, 4), (29, 1), (30, 4), (31, 4), (32, 3), (33, 1), (34, 4), (35, 4), (36, 3), (37, 1), (38, 1), (39, 0), (40, 0), (41, 0), (42, 2), (43, 3), (44, 3), (45, 3), (46, 3), (47, 2), (48, 1), (49, 1)]\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"c1\"># \u9867\u5ba2\u304c\u3069\u306e\u30af\u30e9\u30b9\u30bf\u30fc\u306b\u5c5e\u3059\u308b\u304b\u3092\u793a\u3059\u30ea\u30b9\u30c8\u3092\u4f5c\u6210<\/span>\n<span class=\"n\">belonged_cluster<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"n\">j<\/span> <span class=\"k\">for<\/span> <span class=\"n\">_<\/span><span class=\"p\">,<\/span> <span class=\"n\">j<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">assignment2<\/span><span class=\"p\">]<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">belonged_cluster<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>[4, 4, 4, 0, 3, 2, 2, 2, 1, 1, 1, 3, 0, 2, 3, 1, 3, 0, 0, 4, 1, 4, 2, 2, 0, 2, 2, 4, 4, 1, 4, 4, 3, 1, 4, 4, 3, 1, 1, 0, 0, 0, 2, 3, 3, 3, 3, 2, 1, 1]\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u5bb9\u91cf\u304c\u6307\u5b9a\u5024\u3092\u8d85\u3048\u3066\u3044\u306a\u3044\u304b\u3092\u6b21\u306e\u30b3\u30fc\u30c9\u3067\u78ba\u8a8d\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"k\">def<\/span> <span class=\"nf\">weight_check<\/span><span class=\"p\">(<\/span><span class=\"n\">belonged_cluster<\/span><span class=\"p\">,<\/span> <span class=\"n\">demands<\/span><span class=\"p\">,<\/span> <span class=\"n\">capa<\/span><span class=\"p\">):<\/span>\n    <span class=\"n\">weights<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">]<\/span> <span class=\"o\">*<\/span> <span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"nb\">set<\/span><span class=\"p\">(<\/span><span class=\"n\">belonged_cluster<\/span><span class=\"p\">))<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\">i<\/span><span class=\"p\">,<\/span> <span class=\"n\">cluster_i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">enumerate<\/span><span class=\"p\">(<\/span><span class=\"n\">belonged_cluster<\/span><span class=\"p\">):<\/span>\n        <span class=\"n\">weights<\/span><span class=\"p\">[<\/span><span class=\"n\">cluster_i<\/span><span class=\"p\">]<\/span> <span class=\"o\">+=<\/span> <span class=\"nb\">int<\/span><span class=\"p\">(<\/span><span class=\"n\">demands<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">])<\/span>\n\n    <span class=\"k\">for<\/span> <span class=\"n\">i<\/span><span class=\"p\">,<\/span> <span class=\"n\">weight_i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">enumerate<\/span><span class=\"p\">(<\/span><span class=\"n\">weights<\/span><span class=\"p\">):<\/span>\n        <span class=\"k\">if<\/span> <span class=\"n\">weight_i<\/span> <span class=\"o\">&gt;<\/span> <span class=\"n\">capa<\/span><span class=\"p\">:<\/span>\n            <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"\u30ad\u30e3\u30d1\u30aa\u30fc\u30d0\u30fc\u3067\u3059\u3002(\u8d85\u904e:<\/span><span class=\"si\">{<\/span><span class=\"n\">weight_i<\/span><span class=\"w\"> <\/span><span class=\"o\">-<\/span><span class=\"w\"> <\/span><span class=\"n\">capa<\/span><span class=\"si\">}<\/span><span class=\"s2\">)\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"\u5bb9\u91cf\u5236\u7d04\u3092\u6e80\u305f\u3057\u3066\u3044\u307e\u3059\u3002\"<\/span><span class=\"p\">)<\/span>\n\n\n<span class=\"n\">weight_check<\/span><span class=\"p\">(<\/span><span class=\"n\">belonged_cluster<\/span><span class=\"p\">,<\/span> <span class=\"n\">demands<\/span><span class=\"p\">,<\/span> <span class=\"n\">capa<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>\u5bb9\u91cf\u5236\u7d04\u3092\u6e80\u305f\u3057\u3066\u3044\u307e\u3059\u3002\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u5bb9\u91cf\u5236\u7d04\u304c\u6e80\u305f\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u304c\u78ba\u8a8d\u51fa\u6765\u305f\u3089\u3001\u5b9f\u969b\u306b\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u3055\u308c\u305f\u7d50\u679c\u3092\u63cf\u753b\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"c1\"># \u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306e\u7d50\u679c\u3092\u51fa\u529b<\/span>\n<span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">scatter<\/span><span class=\"p\">(<\/span><span class=\"n\">customers_nodepot<\/span><span class=\"p\">[:,<\/span> <span class=\"mi\">0<\/span><span class=\"p\">],<\/span> <span class=\"n\">customers_nodepot<\/span><span class=\"p\">[:,<\/span> <span class=\"mi\">1<\/span><span class=\"p\">],<\/span> <span class=\"n\">c<\/span><span class=\"o\">=<\/span><span class=\"n\">belonged_cluster<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">show<\/span><span class=\"p\">()<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_png output_subarea \"><img decoding=\"async\" src=\"data:image\/png;base64,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\" \/><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u8a18\u4e8b\u3068\u540c\u3058\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u3067\u5b9f\u884c\u3057\u305f\u5834\u5408\u30015\u3064\u306b\u304d\u308c\u3044\u306b\u8272\u5206\u3051\u3055\u308c\u305f\u7d50\u679c\u304c\u5f97\u3089\u308c\u308b\u3068\u601d\u3044\u307e\u3059\u3002<\/p>\n<p>\u304b\u306a\u308a\u3088\u3044\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306e\u7d50\u679c\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h3><span class=\"ez-toc-section\" id=\"%E3%83%AB%E3%83%BC%E3%83%86%E3%82%A3%E3%83%B3%E3%82%B0\"><\/span>\u30eb\u30fc\u30c6\u30a3\u30f3\u30b0<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u7d9a\u3044\u3066\u3053\u306e\u30c7\u30fc\u30bf\u3092\u3082\u3068\u306b\u30eb\u30fc\u30c6\u30a3\u30f3\u30b0\u3092\u5b9f\u884c\u3057\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n<p>\u5143\u8ad6\u6587\u3067\u306f\u3001\u30eb\u30fc\u30c6\u30a3\u30f3\u30b0\u3092\u91cf\u5b50\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u30de\u30b7\u30f3\u3092\u5229\u7528\u3057\u3066\u89e3\u3044\u3066\u3044\u307e\u3057\u305f\u304c\u3001\u4eca\u56de\u306f<a href=\"https:\/\/developers.google.com\/optimization\">Google\u306eOR-Tools<\/a>\u3092\u5229\u7528\u3057\u3066\u89e3\u3044\u3066\u3044\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"c1\"># depot\u304c\u5165\u3063\u3066\u3044\u308b\u72b6\u614b\u3067\u306e\u9867\u5ba2\u756a\u53f7\u306e\u30ea\u30b9\u30c8\u3092\u4f5c\u6210<\/span>\n<span class=\"n\">clusters<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[[<\/span><span class=\"mi\">0<\/span><span class=\"p\">]<\/span> <span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">)]<\/span>  <span class=\"c1\"># depot\u306e\u756a\u53f7\u306f\u521d\u3081\u304b\u3089\u8ffd\u52a0\u3057\u3066\u304a\u304f<\/span>\n<span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">cus_num<\/span><span class=\"p\">):<\/span>\n    <span class=\"n\">clusters<\/span><span class=\"p\">[<\/span><span class=\"n\">belonged_cluster<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">]]<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">i<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span><span class=\"p\">)<\/span>  <span class=\"c1\"># depot\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u72b6\u614b\u306b\u623b\u3059\u305f\u3081\u306b\u6570\u5b57\u3092+1\u3059\u308b<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">clusters<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>[[0, 4, 13, 18, 19, 25, 40, 41, 42], [0, 9, 10, 11, 16, 21, 30, 34, 38, 39, 49, 50], [0, 6, 7, 8, 14, 23, 24, 26, 27, 43, 48], [0, 5, 12, 15, 17, 33, 37, 44, 45, 46, 47], [0, 1, 2, 3, 20, 22, 28, 29, 31, 32, 35, 36]]\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"c1\"># \u540c\u4e00\u30af\u30e9\u30b9\u30bf\u30fc\u306b\u304a\u3051\u308b\u9867\u5ba2\u3069\u3046\u3057\u306e\u8ddd\u96e2\u3092\u8a08\u7b97\u3059\u308b<\/span>\n<span class=\"n\">distance_matrices<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n<span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">):<\/span>\n    <span class=\"n\">cluster_size<\/span> <span class=\"o\">=<\/span> <span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">clusters<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">])<\/span>\n    <span class=\"n\">distance_matrix<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">zeros<\/span><span class=\"p\">((<\/span><span class=\"n\">cluster_size<\/span><span class=\"p\">,<\/span> <span class=\"n\">cluster_size<\/span><span class=\"p\">))<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\">j<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">cluster_size<\/span><span class=\"p\">):<\/span>\n        <span class=\"k\">for<\/span> <span class=\"n\">k<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">j<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span><span class=\"p\">,<\/span> <span class=\"n\">cluster_size<\/span><span class=\"p\">):<\/span>\n            <span class=\"n\">distance_jk<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">linalg<\/span><span class=\"o\">.<\/span><span class=\"n\">norm<\/span><span class=\"p\">(<\/span>\n                <span class=\"n\">customers<\/span><span class=\"p\">[<\/span><span class=\"n\">clusters<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">][<\/span><span class=\"n\">j<\/span><span class=\"p\">]]<\/span> <span class=\"o\">-<\/span> <span class=\"n\">customers<\/span><span class=\"p\">[<\/span><span class=\"n\">clusters<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">][<\/span><span class=\"n\">k<\/span><span class=\"p\">]]<\/span>\n            <span class=\"p\">)<\/span>\n            <span class=\"n\">distance_matrix<\/span><span class=\"p\">[<\/span><span class=\"n\">j<\/span><span class=\"p\">][<\/span><span class=\"n\">k<\/span><span class=\"p\">]<\/span> <span class=\"o\">=<\/span> <span class=\"n\">distance_matrix<\/span><span class=\"p\">[<\/span><span class=\"n\">k<\/span><span class=\"p\">][<\/span><span class=\"n\">j<\/span><span class=\"p\">]<\/span> <span class=\"o\">=<\/span> <span class=\"n\">distance_jk<\/span>\n\n    <span class=\"n\">distance_matrices<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">distance_matrix<\/span><span class=\"o\">.<\/span><span class=\"n\">tolist<\/span><span class=\"p\">())<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"c1\"># ortools\u3092\u5229\u7528\u3057\u3066\u5de1\u56de\u30bb\u30fc\u30eb\u30b9\u30de\u30f3\u554f\u984c\u3092\u89e3\u304f<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">ortools.constraint_solver<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">routing_enums_pb2<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">ortools.constraint_solver<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">pywrapcp<\/span>\n\n\n<span class=\"k\">def<\/span> <span class=\"nf\">print_solution<\/span><span class=\"p\">(<\/span><span class=\"n\">manager<\/span><span class=\"p\">,<\/span> <span class=\"n\">routing<\/span><span class=\"p\">,<\/span> <span class=\"n\">solution<\/span><span class=\"p\">,<\/span> <span class=\"n\">j<\/span><span class=\"p\">):<\/span>\n    <span class=\"n\">index<\/span> <span class=\"o\">=<\/span> <span class=\"n\">routing<\/span><span class=\"o\">.<\/span><span class=\"n\">Start<\/span><span class=\"p\">(<\/span><span class=\"mi\">0<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plan_output<\/span> <span class=\"o\">=<\/span> <span class=\"sa\">f<\/span><span class=\"s2\">\"Route for vehicle <\/span><span class=\"si\">{<\/span><span class=\"n\">j<\/span><span class=\"si\">}<\/span><span class=\"s2\">:<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">\"<\/span>\n    <span class=\"n\">route_distance<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">0<\/span>\n    <span class=\"k\">while<\/span> <span class=\"ow\">not<\/span> <span class=\"n\">routing<\/span><span class=\"o\">.<\/span><span class=\"n\">IsEnd<\/span><span class=\"p\">(<\/span><span class=\"n\">index<\/span><span class=\"p\">):<\/span>\n        <span class=\"n\">plan_output<\/span> <span class=\"o\">+=<\/span> <span class=\"s2\">\" <\/span><span class=\"si\">{}<\/span><span class=\"s2\"> -&gt;\"<\/span><span class=\"o\">.<\/span><span class=\"n\">format<\/span><span class=\"p\">(<\/span><span class=\"n\">manager<\/span><span class=\"o\">.<\/span><span class=\"n\">IndexToNode<\/span><span class=\"p\">(<\/span><span class=\"n\">index<\/span><span class=\"p\">))<\/span>\n        <span class=\"n\">previous_index<\/span> <span class=\"o\">=<\/span> <span class=\"n\">index<\/span>\n        <span class=\"n\">index<\/span> <span class=\"o\">=<\/span> <span class=\"n\">solution<\/span><span class=\"o\">.<\/span><span class=\"n\">Value<\/span><span class=\"p\">(<\/span><span class=\"n\">routing<\/span><span class=\"o\">.<\/span><span class=\"n\">NextVar<\/span><span class=\"p\">(<\/span><span class=\"n\">index<\/span><span class=\"p\">))<\/span>\n        <span class=\"n\">route_distance<\/span> <span class=\"o\">+=<\/span> <span class=\"n\">routing<\/span><span class=\"o\">.<\/span><span class=\"n\">GetArcCostForVehicle<\/span><span class=\"p\">(<\/span><span class=\"n\">previous_index<\/span><span class=\"p\">,<\/span> <span class=\"n\">index<\/span><span class=\"p\">,<\/span> <span class=\"mi\">0<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plan_output<\/span> <span class=\"o\">+=<\/span> <span class=\"s2\">\" <\/span><span class=\"si\">{}<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">\"<\/span><span class=\"o\">.<\/span><span class=\"n\">format<\/span><span class=\"p\">(<\/span><span class=\"n\">manager<\/span><span class=\"o\">.<\/span><span class=\"n\">IndexToNode<\/span><span class=\"p\">(<\/span><span class=\"n\">index<\/span><span class=\"p\">))<\/span>\n    <span class=\"n\">plan_output<\/span> <span class=\"o\">+=<\/span> <span class=\"s2\">\"Route distance: <\/span><span class=\"si\">{}<\/span><span class=\"s2\">miles<\/span><span class=\"se\">\\n<\/span><span class=\"s2\">\"<\/span><span class=\"o\">.<\/span><span class=\"n\">format<\/span><span class=\"p\">(<\/span><span class=\"n\">route_distance<\/span><span class=\"p\">)<\/span>\n    <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">plan_output<\/span><span class=\"p\">)<\/span>\n    <span class=\"k\">return<\/span> <span class=\"n\">route_distance<\/span>\n\n\n<span class=\"k\">def<\/span> <span class=\"nf\">get_routes<\/span><span class=\"p\">(<\/span><span class=\"n\">solution<\/span><span class=\"p\">,<\/span> <span class=\"n\">routing<\/span><span class=\"p\">,<\/span> <span class=\"n\">manager<\/span><span class=\"p\">):<\/span>\n    <span class=\"n\">routes<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\">route_nbr<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">routing<\/span><span class=\"o\">.<\/span><span class=\"n\">vehicles<\/span><span class=\"p\">()):<\/span>\n        <span class=\"n\">index<\/span> <span class=\"o\">=<\/span> <span class=\"n\">routing<\/span><span class=\"o\">.<\/span><span class=\"n\">Start<\/span><span class=\"p\">(<\/span><span class=\"n\">route_nbr<\/span><span class=\"p\">)<\/span>\n        <span class=\"n\">route<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"n\">manager<\/span><span class=\"o\">.<\/span><span class=\"n\">IndexToNode<\/span><span class=\"p\">(<\/span><span class=\"n\">index<\/span><span class=\"p\">)]<\/span>\n        <span class=\"k\">while<\/span> <span class=\"ow\">not<\/span> <span class=\"n\">routing<\/span><span class=\"o\">.<\/span><span class=\"n\">IsEnd<\/span><span class=\"p\">(<\/span><span class=\"n\">index<\/span><span class=\"p\">):<\/span>\n            <span class=\"n\">index<\/span> <span class=\"o\">=<\/span> <span class=\"n\">solution<\/span><span class=\"o\">.<\/span><span class=\"n\">Value<\/span><span class=\"p\">(<\/span><span class=\"n\">routing<\/span><span class=\"o\">.<\/span><span class=\"n\">NextVar<\/span><span class=\"p\">(<\/span><span class=\"n\">index<\/span><span class=\"p\">))<\/span>\n            <span class=\"n\">route<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">manager<\/span><span class=\"o\">.<\/span><span class=\"n\">IndexToNode<\/span><span class=\"p\">(<\/span><span class=\"n\">index<\/span><span class=\"p\">))<\/span>\n        <span class=\"n\">routes<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">route<\/span><span class=\"p\">)<\/span>\n    <span class=\"k\">return<\/span> <span class=\"n\">routes<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"n\">solutions<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n<span class=\"n\">route_dis<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n\n<span class=\"k\">for<\/span> <span class=\"n\">j<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">clusters<\/span><span class=\"p\">)):<\/span>\n    <span class=\"n\">distance_matrix<\/span> <span class=\"o\">=<\/span> <span class=\"n\">distance_matrices<\/span><span class=\"p\">[<\/span><span class=\"n\">j<\/span><span class=\"p\">]<\/span>\n\n    <span class=\"n\">manager<\/span> <span class=\"o\">=<\/span> <span class=\"n\">pywrapcp<\/span><span class=\"o\">.<\/span><span class=\"n\">RoutingIndexManager<\/span><span class=\"p\">(<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">distance_matrix<\/span><span class=\"p\">),<\/span> <span class=\"mi\">1<\/span><span class=\"p\">,<\/span> <span class=\"mi\">0<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">routing<\/span> <span class=\"o\">=<\/span> <span class=\"n\">pywrapcp<\/span><span class=\"o\">.<\/span><span class=\"n\">RoutingModel<\/span><span class=\"p\">(<\/span><span class=\"n\">manager<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"k\">def<\/span> <span class=\"nf\">distance_callback<\/span><span class=\"p\">(<\/span><span class=\"n\">from_index<\/span><span class=\"p\">,<\/span> <span class=\"n\">to_index<\/span><span class=\"p\">):<\/span>\n        <span class=\"n\">from_node<\/span> <span class=\"o\">=<\/span> <span class=\"n\">manager<\/span><span class=\"o\">.<\/span><span class=\"n\">IndexToNode<\/span><span class=\"p\">(<\/span><span class=\"n\">from_index<\/span><span class=\"p\">)<\/span>\n        <span class=\"n\">to_node<\/span> <span class=\"o\">=<\/span> <span class=\"n\">manager<\/span><span class=\"o\">.<\/span><span class=\"n\">IndexToNode<\/span><span class=\"p\">(<\/span><span class=\"n\">to_index<\/span><span class=\"p\">)<\/span>\n        <span class=\"k\">return<\/span> <span class=\"nb\">int<\/span><span class=\"p\">(<\/span><span class=\"n\">distance_matrix<\/span><span class=\"p\">[<\/span><span class=\"n\">from_node<\/span><span class=\"p\">][<\/span><span class=\"n\">to_node<\/span><span class=\"p\">])<\/span>  <span class=\"c1\"># ortools\u306e\u4ed5\u69d8\u3067int\u3092\u8fd4\u3059<\/span>\n\n    <span class=\"n\">transit_callback_index<\/span> <span class=\"o\">=<\/span> <span class=\"n\">routing<\/span><span class=\"o\">.<\/span><span class=\"n\">RegisterTransitCallback<\/span><span class=\"p\">(<\/span><span class=\"n\">distance_callback<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">routing<\/span><span class=\"o\">.<\/span><span class=\"n\">SetArcCostEvaluatorOfAllVehicles<\/span><span class=\"p\">(<\/span><span class=\"n\">transit_callback_index<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"n\">search_parameters<\/span> <span class=\"o\">=<\/span> <span class=\"n\">pywrapcp<\/span><span class=\"o\">.<\/span><span class=\"n\">DefaultRoutingSearchParameters<\/span><span class=\"p\">()<\/span>\n    <span class=\"n\">search_parameters<\/span><span class=\"o\">.<\/span><span class=\"n\">first_solution_strategy<\/span> <span class=\"o\">=<\/span> <span class=\"p\">(<\/span>\n        <span class=\"n\">routing_enums_pb2<\/span><span class=\"o\">.<\/span><span class=\"n\">FirstSolutionStrategy<\/span><span class=\"o\">.<\/span><span class=\"n\">PATH_CHEAPEST_ARC<\/span>\n    <span class=\"p\">)<\/span>\n\n    <span class=\"n\">solution<\/span> <span class=\"o\">=<\/span> <span class=\"n\">routing<\/span><span class=\"o\">.<\/span><span class=\"n\">SolveWithParameters<\/span><span class=\"p\">(<\/span><span class=\"n\">search_parameters<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"k\">if<\/span> <span class=\"n\">solution<\/span> <span class=\"ow\">is<\/span> <span class=\"kc\">None<\/span><span class=\"p\">:<\/span>\n        <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"No solution found !\"<\/span><span class=\"p\">)<\/span>\n        <span class=\"k\">continue<\/span>\n\n    <span class=\"c1\"># \u89e3\u3092\u8868\u793a\u3059\u308b<\/span>\n    <span class=\"n\">route_dis<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">print_solution<\/span><span class=\"p\">(<\/span><span class=\"n\">manager<\/span><span class=\"p\">,<\/span> <span class=\"n\">routing<\/span><span class=\"p\">,<\/span> <span class=\"n\">solution<\/span><span class=\"p\">,<\/span> <span class=\"n\">j<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span><span class=\"p\">))<\/span>\n\n    <span class=\"n\">routes<\/span> <span class=\"o\">=<\/span> <span class=\"n\">get_routes<\/span><span class=\"p\">(<\/span><span class=\"n\">solution<\/span><span class=\"p\">,<\/span> <span class=\"n\">routing<\/span><span class=\"p\">,<\/span> <span class=\"n\">manager<\/span><span class=\"p\">)<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\">i<\/span><span class=\"p\">,<\/span> <span class=\"n\">route<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">enumerate<\/span><span class=\"p\">(<\/span><span class=\"n\">routes<\/span><span class=\"p\">):<\/span>\n        <span class=\"n\">solutions<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">route<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>Route for vehicle 1:\n 0 -&gt; 3 -&gt; 5 -&gt; 2 -&gt; 7 -&gt; 6 -&gt; 4 -&gt; 8 -&gt; 1 -&gt; 0\nRoute distance: 110miles\n\nRoute for vehicle 2:\n 0 -&gt; 3 -&gt; 4 -&gt; 11 -&gt; 5 -&gt; 7 -&gt; 6 -&gt; 9 -&gt; 2 -&gt; 10 -&gt; 1 -&gt; 8 -&gt; 0\nRoute distance: 106miles\n\nRoute for vehicle 3:\n 0 -&gt; 8 -&gt; 10 -&gt; 3 -&gt; 7 -&gt; 2 -&gt; 5 -&gt; 9 -&gt; 6 -&gt; 4 -&gt; 1 -&gt; 0\nRoute distance: 105miles\n\nRoute for vehicle 4:\n 0 -&gt; 9 -&gt; 2 -&gt; 1 -&gt; 5 -&gt; 8 -&gt; 3 -&gt; 7 -&gt; 6 -&gt; 4 -&gt; 10 -&gt; 0\nRoute distance: 87miles\n\nRoute for vehicle 5:\n 0 -&gt; 9 -&gt; 2 -&gt; 7 -&gt; 4 -&gt; 10 -&gt; 11 -&gt; 3 -&gt; 6 -&gt; 8 -&gt; 5 -&gt; 1 -&gt; 0\nRoute distance: 113miles\n\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u30eb\u30fc\u30c6\u30a3\u30f3\u30b0\u306e\u7b54\u3048\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u305f\u306e\u3067\u3001\u5b9f\u969b\u306b\u3069\u3093\u306a\u30eb\u30fc\u30c8\u306b\u306a\u3063\u305f\u306e\u304b\u3092\u63cf\u753b\u3057\u3066\u307f\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"kn\">import<\/span> <span class=\"nn\">networkx<\/span> <span class=\"k\">as<\/span> <span class=\"nn\">nx<\/span>\n\n\n<span class=\"c1\"># \u89e3\u3092\u8996\u899a\u7684\u306b\u63cf\u753b\u3059\u308b\u95a2\u6570<\/span>\n<span class=\"k\">def<\/span> <span class=\"nf\">print_routes<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">,<\/span> <span class=\"n\">solutions<\/span><span class=\"p\">,<\/span> <span class=\"n\">belonged_cluster<\/span><span class=\"p\">,<\/span> <span class=\"n\">customers<\/span><span class=\"p\">):<\/span>\n    <span class=\"c1\"># \u5143\u306e\u30c7\u30fc\u30bf\u756a\u53f7\u3067\u306e\u547c\u3073\u51fa\u3057<\/span>\n    <span class=\"n\">solutions2<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[[]<\/span> <span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">)]<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">solutions<\/span><span class=\"p\">)):<\/span>\n        <span class=\"k\">for<\/span> <span class=\"n\">j<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">solutions<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">])):<\/span>\n            <span class=\"n\">solutions2<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">clusters<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">][<\/span><span class=\"n\">solutions<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">][<\/span><span class=\"n\">j<\/span><span class=\"p\">]])<\/span>\n\n    <span class=\"c1\"># \u5404\u9867\u5ba2\u306b\u3064\u3044\u3066\u8272\u3092\u6307\u5b9a\u3059\u308b<\/span>\n    <span class=\"n\">cm<\/span> <span class=\"o\">=<\/span> <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">get_cmap<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"tab20\"<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">node_list<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[(<\/span><span class=\"mi\">0<\/span><span class=\"p\">,<\/span> <span class=\"p\">{<\/span><span class=\"s2\">\"color\"<\/span><span class=\"p\">:<\/span> <span class=\"n\">cm<\/span><span class=\"p\">(<\/span><span class=\"mi\">0<\/span><span class=\"p\">)})]<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">belonged_cluster<\/span><span class=\"p\">)):<\/span>\n        <span class=\"n\">node_list<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">((<\/span><span class=\"n\">i<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span><span class=\"p\">,<\/span> <span class=\"p\">{<\/span><span class=\"s2\">\"color\"<\/span><span class=\"p\">:<\/span> <span class=\"n\">cm<\/span><span class=\"p\">(<\/span><span class=\"n\">belonged_cluster<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">]<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span><span class=\"p\">)}))<\/span>\n\n    <span class=\"c1\"># depot\u304a\u3088\u3073\u9867\u5ba2\u306e\u756a\u53f7\u3068\u4f4d\u7f6e\u306b\u95a2\u3059\u308b\u8f9e\u66f8\u3092\u4f5c\u6210<\/span>\n    <span class=\"n\">pos<\/span> <span class=\"o\">=<\/span> <span class=\"p\">{}<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">customers<\/span><span class=\"p\">)):<\/span>\n        <span class=\"n\">pos<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">]<\/span> <span class=\"o\">=<\/span> <span class=\"p\">(<\/span><span class=\"n\">customers<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">][<\/span><span class=\"mi\">0<\/span><span class=\"p\">],<\/span> <span class=\"n\">customers<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">][<\/span><span class=\"mi\">1<\/span><span class=\"p\">])<\/span>\n\n    <span class=\"c1\"># \u30eb\u30fc\u30c6\u30a3\u30f3\u30b0\u3067\u5f97\u3089\u308c\u305f\u89e3\u3092\u3082\u3068\u306b\u5b9f\u969b\u306b\u901a\u308b\u8fba\u306e\u30ea\u30b9\u30c8\u3092\u4f5c\u6210<\/span>\n    <span class=\"n\">edge_list<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">solutions2<\/span><span class=\"p\">)):<\/span>\n        <span class=\"k\">for<\/span> <span class=\"n\">j<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">solutions2<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">])<\/span> <span class=\"o\">-<\/span> <span class=\"mi\">1<\/span><span class=\"p\">):<\/span>\n            <span class=\"n\">edge_list<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">((<\/span><span class=\"n\">solutions2<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">][<\/span><span class=\"n\">j<\/span><span class=\"p\">],<\/span> <span class=\"n\">solutions2<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">][<\/span><span class=\"n\">j<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span><span class=\"p\">]))<\/span>\n\n    <span class=\"n\">g<\/span> <span class=\"o\">=<\/span> <span class=\"n\">nx<\/span><span class=\"o\">.<\/span><span class=\"n\">DiGraph<\/span><span class=\"p\">()<\/span>\n    <span class=\"n\">g<\/span><span class=\"o\">.<\/span><span class=\"n\">add_nodes_from<\/span><span class=\"p\">(<\/span><span class=\"n\">node_list<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">g<\/span><span class=\"o\">.<\/span><span class=\"n\">add_edges_from<\/span><span class=\"p\">(<\/span><span class=\"n\">edge_list<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"n\">node_color<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"n\">node<\/span><span class=\"p\">[<\/span><span class=\"s2\">\"color\"<\/span><span class=\"p\">]<\/span> <span class=\"k\">for<\/span> <span class=\"n\">node<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">g<\/span><span class=\"o\">.<\/span><span class=\"n\">nodes<\/span><span class=\"o\">.<\/span><span class=\"n\">values<\/span><span class=\"p\">()]<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">figure<\/span><span class=\"p\">(<\/span><span class=\"n\">figsize<\/span><span class=\"o\">=<\/span><span class=\"p\">(<\/span><span class=\"mi\">8<\/span><span class=\"p\">,<\/span> <span class=\"mi\">8<\/span><span class=\"p\">))<\/span>  <span class=\"c1\"># \u898b\u3084\u3059\u3044\u3088\u3046\u306b\u30b5\u30a4\u30ba\u3092\u5909\u66f4\u3057\u3066\u304f\u3060\u3055\u3044<\/span>\n    <span class=\"n\">nx<\/span><span class=\"o\">.<\/span><span class=\"n\">draw_networkx<\/span><span class=\"p\">(<\/span><span class=\"n\">g<\/span><span class=\"p\">,<\/span> <span class=\"n\">pos<\/span><span class=\"p\">,<\/span> <span class=\"n\">with_labels<\/span><span class=\"o\">=<\/span><span class=\"kc\">True<\/span><span class=\"p\">,<\/span> <span class=\"n\">node_color<\/span><span class=\"o\">=<\/span><span class=\"n\">node_color<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">show<\/span><span class=\"p\">()<\/span>\n\n\n<span class=\"n\">print_routes<\/span><span class=\"p\">(<\/span><span class=\"n\">car_num<\/span><span class=\"p\">,<\/span> <span class=\"n\">solutions<\/span><span class=\"p\">,<\/span> <span class=\"n\">belonged_cluster<\/span><span class=\"p\">,<\/span> <span class=\"n\">customers<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_png output_subarea \"><img decoding=\"async\" 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NChUq8PbbbxMbG8s\/\/\/zDkydPmD59OuXKlTN2iG\/k0qVLKe9jgp64BSwI+SAgIIBZs2axYMECkpOTee+99\/j444+pVq2asUPLlilTpjB9+nQmT55M7dq18ff3Z\/HixQwePNjYoRUpOp2O6tWr4+3tzfr1640SgyzLHA3fw\/W4S2\/cVzWbWrRw9stwxfrevXuxtLSkadOm6T6enJzM4cOH2bJlC1u2bOHRo0fY2trSsWNHunXrRseOHYv0fK68Jssyx48fZ+7cuaxbtw4TExMGDBjA6NGj8fHxMXZ4BhMWFoarqyurVq3inXfeMXY4eUrMARSEAuLy5cvMmDGDVatWYWNjw+jRoxk3blyaW6oFXUxMDAEBAXh5eQEwdOhQVq5cyblz5wpNEltYLFiwgJEjR3L79m0qVaqU9Ql5QJZlzkUf53zUCf33OZgTKCEhI+Nj70t9h6YG2\/FBlmWuXLmSkgyeOXMGpVJJ8+bN6datG926dSswpUcKuri4OFasWMHcuXO5fPkylSpVYvTo0XzwwQc4OjoaOzyD279\/P23btuXGjRtF\/v1KJICCYESyLLNv3z5mzJjB7t27KVOmDB9++CFDhgzB1tbW2OEZRHx8PPXr18fc3JyTJ09iYWFh7JCKjKSkJMqWLUuvXr34448\/jBpLqCqY\/WH\/EaWOSEnsMvLicXsTR1q7dsLN3D1PYwsKCmLbtm1s2bKFvXv3olKpqFGjRkoy2KhRo0I5Vy0v3bx5M2VRR1xcHF27dmX06NG0a9euSP9bzZw5k88++4zY2FhMTEyyPqEQEwlgRpKi9TXQYgIhNhA0SSBJYO4A9p5gXwZcqqYpgyEI2aHRaFizZg0\/\/\/wzFy5coE6dOkyePJk+ffpgampq7PAM7tKlSzRq1IgRI0Ywe\/ZsY4dTpHz\/\/ff88MMPPH782CjbZ71KlmUCkx5xNeY8gUmP0t3PVymZ4GFRFi+7unhYlMvXfV5BP6K1Z88etmzZwrZt2wgLC6NEiRJ06dKFbt260b59e6ysrPI1plcl61Q8TLjLM9VTQlXBJOoSkGUZS6UVruYlKWFWinJWlTBXGv6DlEajYcuWLcydO5f9+\/fj6urKsGHDGD58OGXLFo\/dfQYNGsTVq1c5c+aMsUPJcyIBfF1MIDzYD6E3AFlf7FZ+bfuiF8dMrcCjMZRtASbmRglXKFzi4uJYtGgRM2fO5PHjx\/j5+TF58mTatm2b738I89ucOXMYN24cW7ZsoWvXrsYOp8gIDw+nTJkyfPLJJ3z99dfGDieFLMtEayKJUkeglbUoJSUOpk7YmzgWmN91rVbLyZMn2bx5M1u3buXmzZtYWFjQvn17unXrRpcuXfJtCka8Jpbz0Se5FXcVraxBgQIdqf\/2vDimREllGy98HBqnu2VeTj19+pRFixbx559\/EhgYSNOmTRk9ejS9evXC3Lx4\/W3z8fHBx8eHRYsWGTuUPCcSwBd0Gri\/Dx4eQr\/1Vdo9K9MngbktePUFJzGnREhfcHAwv\/\/+O\/PmzSM2NpZ+\/foxadKkYlVqQJZl3n77bY4dO8alS5dE7TYDGjduHKtWreLRo0dGHb0q7G7fvs3WrVvZsmULR48eRafT0ahRo5RbxV5eXgZPXmVZ5nbcNY5G7EUra7I9h1JCQiEpaeLUmuo2tTOMKyIigsmTJ\/PZZ5+lmvcoyzJHjhzhjz\/+YP369ZiZmeHv78+oUaOoU6eOIZ5aoaNWq7GxsWHGjBmMHz\/e2OHkOZEAgv727oWl+u2uclXUVNKfV7UbePpm2VooPm7evMkvv\/zCP\/\/8g5mZGcOHD2fixIl4enoaOzSjCA8Pp3bt2lSuXJm9e\/cW2FI2hc39+\/epXLkyc+fOZeTIkcYOp0gIDw9n+\/btbNmyhZ07dxIXF0f58uVTksHmzZu\/8XQNQ62irmLtTUuXt1BIqefmqVQq2rVrx9GjRxkyZAiLFi0iNjaWZcuW8ccff3D16lWqVq3K6NGjee+993BwcHijOAq7a9eu4e3tzcGDB2nZsqWxw8lzIgHUquH8IogOIHfJ32uq94TSBXufQyFvybLMsWPHmDFjBlu2bKFkyZJMnDiRESNGFPs3WIBDhw7RunVrpk6dyhdffGHscIqMvn37cuHCBW7evCkSawNTqVQcPHgwZVXxkydPsLe3p1OnTnTr1o0OHTrk6rV9NHwv12IvGCTGajY1afG8iDbo34f8\/f1ZtWoVOp0OU1NT3nvvPdasWUNCQgLdu3dn9OjRtGnTpsDckje2FStWMHDgQCIiIorkCufXiQTwzg54dITXk78flh\/ni6WH8SrnwtWFQ1OOT1txnC0n7nLvaSSxCcl4utrRuVFFPh\/QBFcHK\/38wEbjwKZwle4Q3pxWq2XLli389NNPnDx5kurVqzNp0iQGDhxY7ObRZOWrr75i2rRpHDp0KMO6bkLOnDlzhoYNG7J+\/Xp69uxp7HCKLFmWuXjxIps3b2bLli1cuHABExMTWrZsmbI1Xfny5bPs5378LfaEbkn3saDbwWybtYfHVwOJDo3FzNKUUpXc8BveklrtamTYZ2uXTlSx0Zdf+uqrr\/juu+9SPW5jY8OHH37I8OHD8fDwyMGzLh4+\/fRTVqxYwePHj40dSr4o3glg9GM4My\/N4SehMVQdvBAJKFfSPlUC2OvbDbjaW1GtjDO2lmbceBzOwu2XKOFgxcX5g7C2tADbktBwjD4ZFIq8xMRE\/vnnH3755Rfu3LlDixYtmDx5Mp06dSrS5RLehEajoVWrVgQEBHDx4sVi8Wk7P7Rs2RK1Ws3x48eNHUqxERAQkDJvcP\/+\/ajVamrWrJlyq7h+\/fpp3geStImsClyESpeUbp9XDtzgwNJjVPApi30JO5KT1FzYcYW7Zx4w8IeeNB\/QON3zTCUz+pUewp+\/L+Sjjz5K83i5cuW4d++eeF\/KQMeOHTExMWHr1q3GDiVfFO8E8PxfEHGP1xd89PthM6FRCWh1OsJiElMlgOlZf+QmvaduYuVn3ejX+vmns1rvQomMP6kJhV94eDh\/\/PEHv\/\/+O+Hh4fTs2ZPJkyfTsGFDY4dWKDx69Ig6derQtm1b1q5dK25DGcC2bdvo2rUrR48eFSOrRhATE8Pu3bvZsmUL\/\/33HxEREZQqVYquXbvSrVs32rRpg6WlJeeijnMu6niOimbrtDqmdZ2NWqXh232T020jIVHNojYtS\/npv5eklOkAGo2+JM\/evXtp27btGz7Tosnd3Z1Bgwbxww8\/GDuUfJGTfK1oFbxLCIeIO2kOH778mHWHb3Jh\/iDGzdmTra7KuTkAEBWnen5EgoDjIgHMD5okCL\/9vF5jEKgT9SOvFg5gV1pfr9GhrEFHYx88eMCvv\/7KX3\/9hSzLDBo0iI8++kjsLJBDZcuWZfHixfTq1YuFCxcyfPhwY4dU6HXq1Ilq1aoxY8YMkQAagZ2dHb1796Z3795oNBqOHz\/Oli1b2Lx5MwsWLMDKygq\/t\/zoOL0ZslnO5pwrlAocSznw6HJAhm1kZO4l32DOH7+jVmlITEwkIiIi5Ss6OlpshweEJz8jKCmAMFUI0ZpItLIWNBItRjSkWvOKxGvisDaxMXaYBUrRSgCfXSVl9e5zWq2OcXP3MLRjbWqWL5HhqbIsEx6TiEar405gJJ8uOohSIdGqdpkXLSDyHqgT9LUCBcNLCIdHh+HpeX0Jn9frNcY8ef4zlsHSCTybgEejLAt3JyQkkJSUlO6b5NmzZ5kxYwbr1q3D0dGRyZMnM2bMGKMX3y3MevbsyciRI5kwYQJNmzZN2T5OyB2FQsGkSZMYNmwYt27domrVqsYOqdgyMTGhRYsWtGjRghkzZnDr1i22bNnCyVvHkM2yV2ZMlZCMOklNYmwSl\/Ze49qhW9TrUivTc5J1Krq+35syVlnPQyxOZFnmXvxNLsWcISw5BAAJBfIrdwBbDvQlwSSc5U\/mU86qMnXsG1LCvJSxQi5QitYt4MvL4dk1Xk0A524+x+dLDnNn6QhcHaxo9fHydG8BB0fEUeqdOSnfe7ja8suINvRtWT31NeoOAWfj7M9ZZMk6CDihX7yDnLZId2asXMH7Hf3IYDri4uJo0KABOp2OGzduoFAokGWZHTt2MGPGDA4ePEjFihX56KOP+OCDD0S9NQNJTEykQYMGSJLE6dOnsbS0NHZIhVpSUhLlypWje\/fu\/Pnnn8YOR3jN5egznIw8lK3bv8s\/X8+RFacAkBQSdd\/yZuD\/9cLaPuP3HgmJ+g5N8XEQJcleiNVEczBsJ0FJj7PcpvCFF+1q2dWngUMzTBRFb4em4nsLOCaQV5O\/8JhEvvr7CF8OfL6aNxNOtpbs+bEfSckaLtwNYcPRW8QlJr\/WSoK4IJEAGpJWDVdXPt+lJRcSwuD0XPDqA6XqpnpIlmXee+89bt26hSzLbNy4kdjYWH7++WeuXbtGw4YNWbt2LT169BAlNgzM0tKSVatW0aBBAz7++GOj72lb2FlYWDB+\/HimTp3Kd999R4kSGd\/NEPJfWPKzbLdtO7g5Ph1rER0Sw7ntl9DpZLTJWoNeo6gLTnrC9pD1aGQ1QLbnXb5odznmHE8SH9GlZB8sldZ5FmdBV7SWDWlSr776YslhnGwtGfd2\/SxPNTNV0s6nHF0aV+JL\/6bMHefHkF92sO3k3ZeNJEWaawhvQKeFKysg9OYbdCLrv66tgeDLqR6ZNm0aGzduRJZlJEmif\/\/+DBo0iPLly3Po0CFOnjxJ7969RfKXR7y9vZk5cybz5s1j48aNxg6n0Bs5ciQmJibMmTMn68ZCvkrWJWc7CSlZsQTVm1Wmca96jFk8GFW8irlDl5LZzTgZOcPVxcXNM9VTtoWsRSOrc7TgJjWZSHUYW4JXo9IW33\/XojUC+MqKwztPIliw\/SKzRrUlKDw25XhSsha1RsfD4CjsrMxxskv\/1lQTLw9KOdmwfN81ujR+MeInU9RyZqN6dATC0k\/+PvhpG3\/vuZrhqU9WjqG0i23qg9fWgJ07WLmwbds2vvzyy5SHZFlGrVazYsUK+vfvb5DwhayNGDGCvXv3MmTIEOrVq0eZMmWyPklIl5OTE0OGDGHu3Ln873\/\/w9q6+I5cFDRvstjdp2NNln++gZD7oZSsmPHIrgKxoj5Zl8zuZ5t4ciuQrbN2Z1lTcemk1Zxcfy5NP24VXPl232Si1REcCd9DuxLFcx\/zopUAmtnqF2kAgeGx6HQy4+fuZfzcvWmaln93PhN61GfW6HYZdpek1hAdr3p5QNaBuVhFZBBxIXA\/4xXZI7rUpZ1PuVTHZBlG\/raLcm72aZM\/fQu4tpaTmjr06NEjzSdqhULBmjVrRAKYjyRJYuHChdSuXZuBAwdy4MABTEyK1ttOfvrwww+ZM2cOS5cuZcyYMcYOR3jOSmmDAgW6bO83\/1KySl\/KJTE245EoCQVWSvG352TEQRK08YQFRpAUr6Jxr3qpair+MWxpmpqKJmYmvDu9d6p+LG0tgOcrrBNuUjG+KuWtq+TrcykIitY7sb0nJISCrMO7nCsbv0lbOf+LpYeJTUhm9uh2VHR3ID4xGUmSsLJIPRl0\/ZGbRMYmUb\/Ka7t\/2IrN7g3iftqk\/FW+NUrjWyP1v\/XRqwEkJKkZ2CaDUjyyDqIfc+XMvZT6WK\/S6XTs3Lkz5ZawkD8cHR1ZsWIFLVu25Pvvv+ebb74xdkiFVrly5ejTpw+\/\/vorI0eOFNMXCggXM7csk7+YsDjsXFIncVq1llMbzmFqYUqpym4Zniujw9W8eO9EFaOO4sbz\/ZVrtq5OzdapF2i2fq8J07rOZu\/iI6kSQKWJgkY9fDLt+2TkIcpZVS52fxeKWAJYBoLOAuBib8XbTdNm9LM2nAFIeezi3RDa\/W8V77SsTrUyTigkibO3g1m27xrlStozoecrewBLSrEdnCGoYtKs1s6OFfuvI0kwIKMEEAAFwzp4MfRTHSqVisTExJSvpKQk7O3ti92LvCBo1qwZ33zzDd988w2tW7dOvSl7cjxEPdAv4koI088NVSjBygXsPMChHJiJ250vTJ48mfr167Nhwwb69Olj7HAEwM3cPcs2yz9fT1KcisoNy+PgZkdMaBynN18g+N4zen\/eBQvrzLeWLO6lS27EXsp0tW9mNRV1Wh2qhOSUkb\/XxWiiCEoKoLRl8ZqiUrQSQLeacGuLvoZcNnm42tKreVX2X3zE33uuoNbqKFvCjrHdffh8QBOcX8wRlBT6VabKordsPN+FXMnxKWqNljWHbtKkhgflSjpk0lIH4beRNElYWFhiYWEhtiQrID777DP27duHv78\/Fy9exNkkAR4fg2dX9KO3kkJ\/nx8ZkPQTq14cd6sFZZrqE8Jirl69erRu3ZoZM2bQu3dv8YGmAHAyc8HZrAThyaFk9MG2fpfaHFt9hsPLThAXlYCFtTllvD3o8b+O1G6fea1MBxMnXM2K9+DD7fhraZK\/7NRUTE5UM7HmlyQnqrGyt6RB1zr0+LRTqoRbQsHd+BsiASzUTCygVD0IPMPrW8G9cPCXgam+d7G34s+JHbLuW9Yhl24kpuEaQsyT53\/csz8CuOvsA8JjEhnYNps7scQGgpMo11OQKJVKli1bhm\/DepxY+hldajulLvadqv6j\/PL3Q9ZByGUIvgieTaGSHyjN8jv8AmXSpEl07tyZI0eO0KJFC2OHIwA1betxMHxHho836FqHBl3r5KpvbzufYp3oJ2rjSdDGpzm+7oetaWoq9vv27ZTH7UvY4TeiJZ5epZFlmWuHbnFo2Qme3HzKRytHoDTRT6GQ0RGiCsyX51KQFK0EEKBCW\/0fCq0qy6bZJ5HoWAMbp7IoFAqcnJxwcnLC0dERZ2dnXF1d+eqrryhXrpwBr1mExTzJWbFn9Ld\/TU0UaQtzp0vSbyEnEsACx8PFlhtLx2CJfrFWtn8PXrQLOA7ht8BnKFjY502QhUDHjh2pUaMGP\/\/8s0gAC4jKNjW4GnuO8OTQNyhPkpqEhIOpE9VsM98ppKjLqAZiVjUVe3zSMVX7Bl3r4Fbelc0\/7+T8jiupEvIodQRaWYtSKj7zaoteTRNzW6j+tgE7lMDMGnPvHnh6eqLRaHj27Bk3b97kxIkTbNu2jSVLlvDo0SMDXrOIUyfmqHlcYjKbT9zhrfrlX96Sz4wk5fgaQj5QxcLZP7FRqlAqcjuaIUNiBJz9U99fMSVJEpMmTWLr1q3cuJHLIuqCQSkkBW1cOiMZ+D5RG5fOxSopSU9GtfpyU1Ox7ZDmSAqJm0fvpDouI6PWvb75Q9FW9BJAgJJ1oGzLLJtlTdLP+aszCIW5NXv27MHUNPUcQKVSSfv27cWn8Dy06djt56t\/xZ6yhZYsw9VVoIpOd9TvzpMI+v2wGY\/+c7Hq8jPVBi9g6r9HSUhSp9OXTt\/P1dU5mkZQ1AwYMIBSpUrxyy+\/GDsU4TlHMxfaunYxWH+tXDriYp7x6mAhNZ+ONXl0OYCQ+6EZtjGzMMXG0Yr46LSDBIZO3gu6opkAAlR6C8q3ff5Nbn6oEphaQr3h+uLCQOXKlZk2bVqqVlqtFjMzMwIDi9\/8gVwzy1k9q+X7r2NjaUY338rZO0GWc3wNIY8FnobI++kmfwHPYmg47m9O3ghkbHcfZo1qh2\/10nz9z1H6T9ucfn+yDiLvQdCZPA684DI3N2fChAn8+++\/BAcHGzsc4bkK1lVp79odJcpcJRQSEgoUtHXpQhUb8aEXwFKZvT3as1NTMSkuibiIBGydUlcWUKDAVFG85hYX3QRQkqBiO6g37JW5Qtl4MUrP\/0lK1oYmH4Nd6lp0H374IT4+PiiVShQKBX5+fpw5c4aqVavyww8\/kJRUfLeVyTZ7z5f\/zlkIjUpg7\/mH9GhaOU2txozJYJt1WQYhn2jVcHdnhg\/\/u\/cqUXEq\/vu+D5\/282V45zosmdyZ99p7s+XEXSIzeTPnzs4crfovakaMGIGZmRm\/\/\/67sUMRXlHBugp9Sg9KKQ+TnUTwRRsXMzf6lB5EJZvszHcuHlzMUo+CxoTFpWnzek1FtUpNUlza947\/ft+HLMvUaFk11XEnM1cU2fy7VFQUvUUgr3OsAL4fQvAlfcmJ+BD98Vd\/0M9LT2i0Miala4KHLziUTbc7pVLJP\/\/8Q506dbC3t2fNmjUAfPfdd3zzzTcsXryYX375hbfffrtYr9rKlL1nSr3GrKw+eAONVpfD27+SSAALkmdXMt1DOyZBP+\/GzTH1J\/JSTjYoFBJmJpm8KWsS4dlV\/bSPYsjBwYFhw4Yxb948pkyZgo2NGPkuKOxNHelWsj+PEu9xLeY8T5L088Sl5\/\/Bi53M9aPipSw88bKtSzmrSsUuEcmKudICWxN7YjXRQPZqKoY9iWBa59nU71aHkhVcAbh+5DZXD9zEq2VVard\/WVFCQpGtWo5FjSRnNlvyuZiYGOzt7YmOjsbOzi4\/4sobsgyJkfoSIbFPQZsEKMDCjpXbDvPRN7\/w8Ekw5uaZF+QE2Lp1K46OjjRr1izl2K1bt\/jwww\/ZsWMHbdu2Zfbs2Xh5iSH8NNSJcPgHkLVZNvUd\/w\/3n0YRtGosSmU23hQlBbh6Qa0BBghUMIhzi\/S3fzNYGbnzzH06fraGbr6V+Pa95jjbWXL8eiDDZ+5kcIeazByV8XaNIIFTRfAZkiehFwaPHz+mQoUK\/Prrr4wfP97Y4QgZSNTGE6oKISw5hERtIiBjobTExcwNVzM3rExE8p6Z81EnOBt1DBmZM1svcmz1GYJuPU1VU7H1+01SaiomxCSy+uvN3L\/4iOiQGHRamRLlnGnYvS7th7VEaZp6YU3PUu8Wid1WcpKvFa8EMBMXLlzAx8eHAwcO0KpVqzfqa\/v27UycOJH79+8zevRovv32W1GM+HXX1kHwhRyXg8mWesPBsbzh+xVyTpbh4DegzXx13ffLjzFt5QkSVS9v534+oAnfD8rG4iqlObT6Wj\/to5gaOHAgx48f586dO2KvZaFIStDGsyxgnsFK7LwgIeFk5kpv9\/cN2q+x5CRfE+PMz9WuXRtXV1d27979xn116tSJq1evMn36dJYuXUrlypWZP38+Wm3WI17FRsV2+q31DElSgEt1\/dZhQsGQGJll8gdQzs2eFjU9WfBhB9Z\/1YPBHWoxbeVx5mw6l\/U1tCpIinrzWAuxSZMm8fDhQ9avX2\/sUAQhT1gpralr3zjrhjkkI9PEqY3B+y0MRAL4nEKhoH379gZJAAHMzMyYNGkSt2\/fpmvXrowaNYp69epx+PBhg\/Rf6Fk4QNWuBuxQAoUpVO9RrEeCChx12ur9r1t14DrDZ+1k0UcdGdapDj2bV2Xxx514v31N\/rf4IOEx2ajpqE4wQLCFV926dWnbti0zZszItAaaIBRmPg6+OJg6Gaxci4SEt60P7haeBumvsBEJ4Cv8\/Pw4f\/48oaEZ1xDKqZIlS7JkyRJOnTqFhYUFLVu25J133uHx48cGu0ah5V4fSjc0QEfP942t\/a6+ELhQgGSdjPyx9Tx1K7rh4Zr6dkU330okJKm5cDfEINcp6iZPnsy5c+c4ePCgsUMRBIMLCgpixo8z2P79QcwU5m+cBEpIlDT3oJGjIWoGF04iAXxF+\/btkWWZffv2Gbzvhg0bcvz4cf7++28OHz5MtWrV+Pbbb0lMLMY7VkgSVOuuX3WtP5CLPhT6Yt11B+sXAwgFi6l1lk1CIhPQ6tImcGqtfn6oRpuNeaKm2asTVpT5+flRs2ZNfv75Z2OHIghvTK1Wc\/jwYaZMmYK3tzelS5dmypQprFi0iu4lB2CltH6jJLC0RVk6uvXCRFF858yKBPAV7u7ueHt7G+w28OsUCgXvvfcet2\/fZvz48UybNo1q1aqxdu3a4nvbRlJAtW5Q61194e1sv6Cft3Mory\/zI5K\/gsnSEZSZF1et4uHIhXsh3H4Sker4ygPXUSgkapV3zfwaSjOwEIusXmwPt337dq5du2bscAQh1+7cuYOLiwstW7bk559\/TvX7PHz4cBzNnOlTejBVbLyB7O\/gISGhRElTp7Z0cuuNqSK7tWWLJpEAvsbPz4\/du3fnaUJma2vL9OnTuXbtGrVr16Zv3760adOGy5cv59k1C7wSNaDJJKjkB+b6W4E6WUaHBC++Xq2N5VgBavnry39YOBgjYiE7JAXYliazxH5yn0ZotTqaf7iM75Yd448t5+n02Ro2HbvD4Ldq4e6S2W19Cew8xbzP5\/r160fp0qXFKKBQqLm6uuLk5IRCoUCjSV3ofdSoUQCYK8xp5dKB7iUHUMGqSkoSKKX6T5Fy3Ewyo5Zdfd7xGIK3nY+o04soA5PGrl276NChA9euXaNGjRpZn2Cga06cOJHbt28zYsQIvvvuO5ydnfPl2gWSrOP2uYPMmjqJrz4cRkkXB30iYW6nTybsPfUjS0LhEHQWrme+OvX0zSC++ecoF+6FEB6TSPmSDrzf3ptP3mmMSVb1H2v0AXcfAwZcuM2YMYPPP\/+chw8f4u5e\/IrbCkXDgwcPqFWrFnFx+l0\/JEmibt26nDuXfmWARG0CIaogQlXBxGqi0ck6TBSmOJo642rmRglz92Jxu1fUAXwDCQkJODk5MX36dCZOnJhv11Wr1cyZM4dvvvkGpVLJd999x4gRI4ptTa9\/\/\/2X9957r1j8zhV52mQ4PE1frsXQlObQ4nP9PFABgOjoaDw9PRk9ejTTp083djiCkGOyLDN16lS++eYbTE1N0Wq1yLLMvHnzGDFihLHDK9BEHcA3YGVlRfPmzfNsHmBGTE1N+fDDD7lz5w69evVi3Lhx1K1bl\/379+drHAXFpUuXKF++vEj+igKlGVRsnzd9V\/ITyd9r7O3tGTFiBPPnzyc2NtbY4QhCjmi1WsaOHcs333zDtGnT2LdvHyYmJpiZmdGvXz9jh1ekiAQwHX5+fhw8eBCVKg9GLLJQokQJFi5cyJkzZ7Czs6Nt27b06tWLhw8f5nssxnTp0iVq165t7DAEQ\/H0BfsyqedxvglJoS\/47WH4wrBFwfjx44mPj2fRokXGDkUQsk2lUjFgwADmz5\/PggULmDJlCs2bN2fv3r38+++\/2NvbGzvEIkUkgOnw8\/MjMTGR48ePGy2GevXqcfToUZYvX86pU6eoVq0aX331FfHxWRfWLQouX74sEsCiRFJAzQFgZvPmSaCkADNb8O5nuISyiPH09KR\/\/\/7MnDkTtVpt7HAEIUtxcXF06dKFTZs2sXbtWoYNG5byWPPmzenTp48RoyuaxLtnOmrWrImbm1u+3wZ+nSRJDBgwgJs3bzJp0iR++uknqlWrxqpVq4p02Zjg4GCePXtGrVq1jB2KYCCXLl2iW9936ffLQTC3J1c1H0F\/nrk91B8BFmI0IDMff\/wxAQEBrF271tihCEKmwsLCaNOmDadOnWLnzp307NnT2CEVCyIBTIeht4V7UzY2Nnz\/\/fdcv36dBg0a0L9\/f1q2bMmFCxeMHVqeuHTpEoAYASzkQkJCmDlzJt7e3tSpU4etW7dy6NRlaDxevwsMZH8E70W70g2g8QSxCjwbateujZ+fn9geTijQHj9+TLNmzXj06BGHDh2idevWxg6p2BAJYAbyYlu4N1WhQgU2bNjAnj17iIiIoF69eowYMaJAxWgIly9fxsbGhvLlyxs7FCEXIiIi6NKlC+7u7kyaNClVEdfx48eDiQXU6An1hoNLNVJGA19PBlO+l8Clun7Ur3oPMDHPl+dRFEyePJmLFy\/mye5GgvCmrl+\/TpMmTUhOTubYsWPUrVvX2CEVK6IMTAaePn2Ku7s7K1asoH\/\/\/sYOJw2NRsO8efP46quvkGWZb7\/9ltGjR2NqWvhXRPr7+3P\/\/n2jzsEUci8gIIBq1aqRkJCQ5rGrV6\/i5eWV+mBSNETeh9hAiA8FnQYUJmDtqq\/76FhB3O7NJVmW8fHxwc3NjZ07dxo7HEFIceLECTp37oyHhwe7du2iVKlSxg6pSBBlYAygVKlS1KpVq8DcBn6diYkJ48aN486dO\/Tv35+PPvqIOnXqsGfPHmOH9sbECuDCzdPTk8OHD2Nrm3oHDw8Pj\/SLq1vYQ6m6UKUL1B0E9Ybp\/7dKF\/1xkfzl2ovt4Xbt2lW8dxoSCpQdO3bQrl07vLy8OHz4sEj+jEQkgJnIj23h3pSLiwvz5s3j3LlzuLi44Ofnx9tvv829e\/eMHVquqFQqbt68KRaAFHKOjo6Ym5ujVCpTvt5++22x\/ZIR9O3bF09PT3755RdjhyIILF++nG7dutG2bVt2796Ng4ODsUMqtkQCmAk\/Pz+CgoK4fv26sUPJUp06dTh48CCrV6\/m\/Pnz1KhRg88++yxlG53C4saNG2g0GjECWIgFBQXRrl07HBwcOH78OB4eHmi1Wrp06WLs0IolU1NTJk6cyIoVK3jy5ImxwxGKsdmzZ+Pv74+\/vz8bNmzA0tLS2CEVayIBzESzZs0wNzcvsLeBXydJEn379uXmzZtMmTKFmTNnUrVqVZYtW1agRzFf9WIFcM2aNY0ciZAb4eHhtG\/fHrVazd69e2nYsCEnTpxg5syZtG3b1tjhFVvDhg3D2tqa2bNnGzsUoRiSZZnPP\/+ciRMnMnnyZP76669iu81pQSISwExYWlrSokWLQpMAvmBlZcU333zDzZs3adq0Ke+++y5Nmzbl7Nmzxg4tS5cvX6ZChQpp5o8JBV9MTAwdOnQgNDSUPXv2ULZsWUA\/n3bixIniDd+IbG1tGTFiBH\/++SfR0dHGDkcoRrRaLSNGjGDatGn89NNP\/PTTT2IqSAEhEsAs+Pn5cejQIZKSkowdSo6VLVuWNWvWcODAAeLj42nYsCFDhgwhJCTE2KFlSCwAKZwSExPp3r07t2\/fZteuXVSrVs3YIQmvmTBhAklJSSxcuNDYoQjFRFJSEn379uWvv\/5iyZIlTJ482dghCa8QCWAWXmwLd+zYMWOHkmutWrXi3LlzzJ07l02bNlGlShV++eUXkpOTjR1aKrIsc+nSJbEApJBRq9W88847nDp1iv\/++0\/U8iqg3N3dGThwILNmzSpwr32hYFJpkwhMfMztuGvcirvKg\/g7xKijsjWlKCYmho4dO7J9+3Y2btzIBx98kPcBCzki6gBmQZZlSpUqxfvvv8+PP\/5o7HDeWEREBF9\/\/TXz5s2jUqVKzJw5k44dOxo1ptmzZ5OcnIyHhwcDBgxgw4YN9OjRw6gxCdmj0+l49913Wbt2LVu2bKFDhw7GDknIxNWrV6lZsyb\/\/PMP7777rrHDEQqgJG0it+Oucj32EtGayHTbmEpmVLSuhpdtHVzM3dI8HhISQseOHbl\/\/z7btm2jWbNmeR228FxO8jWRAGbDe++9x5UrV4rU1mtXr15lwoQJ7N+\/n86dOzNz5kwqV65slFhcXFwIDw9P+d7e3h4fHx8GDx6Mv7+\/UWISsibLMmPGjOHPP\/9k1apVYrP2QqJTp048efKELVu2MHv2bCIiIvj777+NHZZgZDpZx9WYc5yOOoJW1mbZXkKBjI4ylhVo4fwW1iY2ANy\/fx8\/Pz8SEhLYuXOnuKOTz0QhaAPz8\/Pj4sWLBXruXE55e3uzd+9e1q9fz7Vr1\/Dy8uKTTz4hJiYm32N56623Ui0QiI6O5sCBA+zatSvfYxGy7\/PPP2fevHksWLBAJH+FSPfu3bly5QoVKlRg1qxZrF+\/3tghCUaWoI1nc\/AKTkQezFbyByCjAyAg8QGrAxfzMOEuly9fpmnTpkiSxLFjx0TyV8CJBDAb2rVrB8DevXuNHIlhSZJEz549uX79Ol999RVz586lSpUqLF26FJ1Ol29xdOnSBY1Gk\/K9QqGgVKlSzJo1K99iEHLmxx9\/5P\/+7\/\/45ZdfGDJkiLHDEbLh0aNHtG7dmpEjRwKkzOMyNxd7KxdnCZo4Nj1dTqgqOFfny8io5WR2hWxk7PcjcHd359ixY2Iv90JA3ALOpjp16lC7du0ifaskICCA\/\/3vf6xcuZKGDRvy22+\/0ahRozy\/bkREBC4uLil\/kJRKJUeOHMHX1zfPry3k3J9\/\/snIkSP58ssvmTp1qrHDEbJp9+7ddOjQIc0E\/tKlS4sC0cWUVtayMehfzp+9wPH1Z7l98h7hTyKwdrSmfJ0ydP\/4LdwquKY65+ndENZ+t5V7Zx+iNFVSs3V1en\/RBVsnG2RZxs+hBxWcjDOdSBC3gPNEYdgW7k15enqyYsUKjhw5glqtpnHjxrz\/\/vs8ffo0T6\/r5ORE\/fr1U76fPn26SP4KqJUrVzJq1CjGjRvHt99+a+xwhBzw8\/Njy5YtWFtbo1QqU46L3RiKrwvRJwlXh7Jz\/gEu7LxCtSaV6PtVN5r3a8Td0\/eZ1nU2gbdejgxGPo3il3fmE\/oonO6TOtB+WEuuHLjB7HcXolFrUCgUnEo4iEanNuKzErJLJIDZ5OfnR3BwMFevXjV2KHmuWbNmnDlzhgULFrB9+3aqVKnCjz\/+iEqlyrNrvhhp9PX15aOPPsqz6wi5999\/\/\/Hee+\/x7rvvMmvWLFHMtRDq0qULZ8+epVy5cik\/P1Ggu3iKUUdxPuoEAO2GNmfa0Sm88013mvVrRKdxbfl4zSi0Gh275h1IOWfHHwdQJSTz4fLhtBnUjI5j2jBsjj9PbjzlxLqzyMjEaKK4HFPwNx0QRAKYbc2aNcPCwqLQ7QqSW0qlkmHDhnHnzh2GDh3KF198gbe3N1u3bjXMKKhWDdEBEHYLwm4x7v2eeNeoxrp161AoxK9lQXPo0CF69+5Nly5dWLx4sfgZFWLVqlXj3LlzKXObX134lahNICjxMY8T7hOQ+IAodUSRvutRnF2PvZTy\/yvWK4eJWeoPAm7lXXGv4kbwvWcpxy7suELNNtVxKu2Ycqx6s8q4lXfh3PbLKceuxJzL9mISwXjER79ssrCwoGXLluzZs4ePP\/7Y2OHkGwcHB2bOnMnw4cOZOHEi3bp146233mLWrFk53+0hOR6enoegcxD\/DHj5h6UKcGV2D3i8FjT1oZQPmIpbUwXB2bNn6dq1K02bNmXlypVixKgIsLe3Z8eOHQwYMACnMg4cDd\/Lg4TbJGjj07Q1kUwpae6Bl11tylhWRCGJ5L+w08k6bsReQibj5F6WZWLC4nCvrK\/zFxkcTWx4HGVreqRpW652Ga4evJnyfZIukccJ9yhvXcXwwQsGI17JOVCYt4V7U9WrV2fnzp1s2rSJO3fuULNmTT766COioqKyPlmngXt74Mj\/wZ0dEB8C6b7xyBAXDLe3wZFp8OAA6MSnSGO6fv06HTp0oEaNGmzatAkLCwtjhyQYSJKcwAe\/9aXOmEpcj72YbvIHoJHVBCY9ZNezTax4soCAxAf5HKlgaJHqMJLlzKf0nN50gajgaOp10W\/NGfNMP1JsXyLtPu12JWyJj0pArdJXc1Cg4KlKLCwq6EQCmAN+fn4kJSVx9OhRY4diFJIk0b17d65fv853333HggULqFKlCosWLUKrzSBRiw+FU7\/Dg\/0ga0k\/8UuHTgP3dsOZPyAx\/Wr0Qt568OAB7du3x93dne3bt2NjY2PskAQDuR9\/i9WBi1OSucxGgl59PEEbx\/aQdRwK2yVu8RVioarMa9oG33vGyq83UcGnLL696gGQnKRP7l6\/VQxgaq4\/plbpF3\/o0PFMlbeLB4U3JxLAHPDy8qJUqVLFvkCxubk5n376Kbdv36ZDhw4MGzaMhg0bpt0vOS4Ezs6D+LDcXywuWJ8EJrxBH0KOPX36lHbt2mFpacnu3btxcnIydkiCgdyMvcye0C2oZXWWid\/rXrS\/GXeZXSEbRRJYSMVqolFk8Oc\/OjSWOYP\/wtLWguF\/+KNQ6tuZWeiTPE2yJs05L0b+TM1NU47FqKMMHLVgaGIyTw5IkoSfnx979uwxdigFgru7O\/\/88w+jRo1i\/PjxNGvWjAEDBvDjjz\/iUcIRzi\/izNUH\/L37MgcuPeZhSDTOtpY0ru7O94NaUMXjZVKxcPtFlu29xs2AcKLiVbg729CqVhm+frcp5UoB5xaB70QwEbcg81p4eDjt27dHpVJx9OhRSpYsaeyQBAN5nPCAv\/cv4sT6c5nWfNPpdJzccJ6LO68QcD2I+KgEXDydqN+lDu2Ht8DU3JSApAccDttFa9dORn5WQk7pSL\/Qf2JMInM+WExiTBIfrxmFg5t9ymN2JfQ15aKfxaY5L+ZZLNYOVikjgZldQyg4xAhgDvn5+XHp0iWCg3NXNb0o8vX15dSpU\/z111\/s27ePqlWrcn3jdOTkeH5cfYL1R2\/Rtm5ZZo9qx\/DOtTl8JQCfUUu4+iA0pY8Ld0MoX9KeT\/o2Yt54P\/zberHjzH0ajP2boNBoUMXo5wYKeSo2NpZOnToREhLCnj17KFeunLFDEgxEpU3iYNh2ds0\/mGXNt+RENf9MXkNsRDzNBzSmz5fdKFfbk62zdvP7B4tTVgbfjr\/Gw4S7xnxaQi6YSqZpRn\/VKjVzhy4l5EEoYxYPSln88YJjSXtsna15dCXt3L6Hlx7jUcM91TETyTRNO6FgESOAOfTqtnD+\/v5GjqbgUCgUDBo0iF69erH6j++p4ay\/JfBRrwasmNINM9OXhWffaVmdmsMXM331SZZ92hWAP8a\/labPt5tUof6Ypfyz9yqf9vPVrx4uVQ8cxRZDeSEpKYnu3btz8+ZNDhw4QPXq1Y0dkmBAp6OOkKRLpN3Q5gyZ3T\/VXK56XWrxXYeZ7Jp3gMGz+mNiqmTyutFUrFcupU3z\/o1w9nBk68w93Dx2l+rN9Ls9HArbiYfHSEwU4s9JYeFo6pwqAdRpdSwcu5z7Fx4xasH7VPApm+55dTvU5MT6c0QEReHk7gDAzWN3CHkQRtshzV9pKeFs5ppuH0LBIUYAc6hEiRLUrVu32NQDzCk7OzuG+VWD50Vmm3h5pEr+ACp7OOFVzoUbjzOf11eupP72Q1Tci9VqCnh8LOMThFxTq9X069ePEydOsG3bNnx8fIwdkmBAKm0St2KvICNnq+abiZlJquTvhTp+3gAE331ZGy5Jl8j9hFt5F7xgcC7mqad1rPthG5f3Xse7ZVUSohI5tfF8qq8XOoxug5mlKTMH\/MmBpcfY+cd+FoxZRumqJfHt3SClnQS4moupIwWd+MiWC35+fixduhRZlsVuCK9LjISIO5k2kWWZkMgEvMq6pHksPCYRrVbH42cxTF2mT\/ba1n3xaVQHoddBFQvmaUsRCLmj0+kYPHgw\/\/33H1u2bKF58+ZZnyQUKnfir6Ml4wUbr9d8y0h0qH7+l42TVcoxCYlrMReoYuNlmGCFPGejtMXR1JlIdTgAAdeDALi87waX991I075RD\/0HQid3Bz5eNZJ1329j40\/bMTE1wbt1NXp\/3iXV\/D8ZmbKWFfPhmQhvQiSAueDn58ePP\/7IlStXqFWrlrHDKVgi72fZZPm+awSGxTL1\/WZpHivdbw4qtf4PlbOdJb+NaUf7eq\/e8pUh6iG41TRQwMWbLMuMHz+e5cuXs2rVKjp27GjskIQ8EJj0GAkpw1W\/L2q+df3QL9N+dv95CAtbC7xavSwCLyPzLPkpap0aU4WY91UYSJKEt50PR8L1Cxo\/XjUy2+e6VynJ+H+GZtw3Es5mJcQIYCEgbgHnQtOmTVPKYwiviQ2ETHYKuPk4nDG\/78G3Rmneb582idsxrS\/bf+jDLyPaUKaEHfFJr20qLin01xAM4ssvv2Tu3LksWLCAvn37GjscIY88Uz3NMPlLr+ZbenbM3c\/NY3fo8UlHrOzS7tITnvwsnbOEgqqydQ0sFVZIGPYuloyMj31jg\/Yp5A2RAOaCubk5rVq1EglgehLCQU5\/+X9wRBydv1iLvbU56758G6Uy7a9f6zpl6diwIh\/1bsjaL9\/m23+PMWfTuZcNZFl\/DeGNzZgxgx9++IGff\/6ZoUMz\/kQvFG5aWUuCNi7dxzKq+fa6s9susuWXXTTt24CW\/r7ptonRiILthYmpwoxWLh1zXAsyMxIS5a2qiC3gCgmRAOaSn58fhw8fJjEx0dihFCxadbqHo+OT6PjZGqLiktj5f31xd8l6Dl9Fd0fqVnJj+f5rrxyVxfZwBrBw4UI++eQTvvjii2K1t3VxpMugWPOrNd\/GLR2Squbbq64fuc3Sj1fj3boaA37omeF1tBl88BMKrjJWFfCyrWuQviQkrJQ2NHdub5D+hLwnEsBcelEo98iRI8YOpWBRmqU5lJSsoeuX67gdGMm27\/tQI53FHxlJVKmJjn91z0oJlGLq6ptYvXo1I0aMYOzYsUydOtXY4Qh5TCmlfb1kVfPthQcXHvPnyH8oU9ODYXP9UZoo020HYJLOdYSCr4lTG6pYv9kCHn3yZ03Xku9gqbTK+gShQBAJYC7VqFEDd3d3cRv4dTYlUs0B1Gp1vPP9Jk5cD2LtF2\/jW6N0mlM0Wh2RsUlpjp++GcSVB6HUr\/LKZGJJAqsSeRJ6cbB9+3b8\/f0ZOHAgs2fPFqvYiwGFpMDW5OXo3qs134bN9c+w5tvTuyHMGfIXzh6OjFk8CDOLzBd4OJg6GzRuIX8oJAWtXDrSyLEFChS5mhNY2qIsPUq9i72pYx5EKOQV8ZEtl15sCycSwNfYlk41B\/DjP\/ez5cRdujauRERsIsv2Xk3V3L+dN3GJyXgOmMs7rarjVdYFawtTrjwIZcnuK9hbm\/PlwKYvT5B1YJc2iSx2ZB0kx4FOAwoTMLPJdPENwOHDh+nVqxedO3dmyZIlKBTi819x4WbuTpwmBhk5peZbrbbVU2q+vapRDx+S4pL47f3FJEQn4je8JVf330zVxrWsc6rEUUKBk1n2R\/aFgkWSJOrYN6KsZUWORxzgSdLDTFeNv3jMWmlLfYemVLXxFh8mCyGRAL6BF\/UAnz59SqlSpYwdTsHgVFGfiDxPAi\/eCwFg68m7bD2Zdsso\/3beWJmbMrRjbQ5cesS6w7dITFbj7mxD\/1bV+WJgE8qVdHh5gqQEh3L58EQKIFUMBJ6B8DsQGwS6V+ZbKkzB1h1cqoJ7\/TR1Es+dO0eXLl1o0qQJq1atwsREvPSLE0\/LctyN19d3y07Nt7ioBCKDogDY+OOONG0a96qXkgBKSJS28EQpZXx7WCgcHM1c6FyyD9HqSG7FXSU46QmhySFoZP17jYSEvakTbualqGBVFU\/L8iLxK8Qk+cWmjpmIiYnB3t6e6Oho7Ozs8iOuQiE0NJQSJUrw999\/89577xk7nILjykp4djXD1cC5JimglA\/U6GXYfgu65Di4tQ1CLj8\/kNlL9vmbcak6ULkzmFlz48YNWrRoQcWKFdmzZw+2tqKIdnGj0Wn4N2AuyXJynvT\/VokelLOqlCd9C8YlyzIaWU1iUhJe1byY+u1UBg0aZOywhAzkJF8T94DegKurKz4+PuI28OvKNjd88gf6EjBlmmbdrih5dg2O\/wIhV9Anfll9Xnve5ulFOPELIdcO0r59e0qWLMn27dtF8ldMmShMqGlf3+D9SkjYmzhSxrKCwfsWCgZJkjBVmLFnxx6eBDzhww8\/JC4u\/bJCQuEiEsA35Ofnx549e9DpRAmEFHYeUKYZGLjAKOVbg00xqi7\/5CRcXgaaJCCnv18yqBMpEbST99pWZ\/fu3Tg5OeVFlEIhUde+EfYmTgYt\/Csj09q1E4os5p8Khd\/SpUsB\/QjT559\/btxgBIMQr9o35Ofnx7Nnz7h8+XLWjYuTin5g7ZrlwoRskRT6+W3lW795X4VFyGW4udkAHUn88G59SikjDNCXUJgpJRMcA0qRrFJnPZCcTT72vriZuxumM6HACg0NZccO\/VxQWZb5\/fffOXnypJGjEt6USADfUJMmTbCyshK3gV+nNAWfoWDp9IZJoARWrlB3sH61a3GgioHrG9J96Iflx5HaT8d72KJUx3U6mflbL1BnxF\/YdP0Ftz6\/0fGzNZy4\/kQ\/3nN9Hahi8z52ocDatGkT3Vv35MTciyik3JX7eJWXbV3qOxSzKRnF1MqVK1Pd5VIoFHzwwQckJ+fNnFIhf4gE8A2JbeEyYW4L9UfqV6bmVglvqD8CzKwNF1dBd3Nz6hW+zz0JjWHaqhNYp1OPbfKC\/Yz6bRc1y7vy68i2fNy7IbefRNDy4xWcvhkE2mS4tTU\/ohcKoMWLF9OrVy+6d+\/OXzP+oXupAdiY2OU4CZSQUKDA17E1TZ3aihWgxcTixYt5db2oVqvl1q1b\/Prrr0aMSnhTxWRIJW\/5+fnxySefkJCQgJWVqIKeipk11HoXQi7BnR360S0kMrwH9aKEjIWDfhWrm3c+BlsAJIRB6PV0H5q04ACNq7mj1ekIi3m5BaFGq2Petgv0bl6Vfz\/tmnK8T4tqVHhvPsv3XaNhNXd4dgUSI8FSFGstLmRZ5scff2TKlCmMHj2a3377DaVSSQlK0cd9EOeijnE19gJaWZNpPy\/qvpWy8KS5c3scTMV80uIivSlOdnZ2uLu74+Iiaj8WZiIBNAA\/Pz8mTpzI4cOH6dChg7HDKXgkCUrWAbdaEHYLnl6A6Megik7dzsIB7MvoS704VzbM\/MHC5skp0kuQD19+zLrDN7kwfxDj5uxJ9ZhaoyVRpcHNMfUoaQkHKxQKCUvzFyOGEgSehkpv5V38QoGh0+mYNGkSM2fO5JtvvuGrr75KNWJnqjClsVMr6jr4cifuGg8S7hCqCkb9SqkYCQVOps64W5ahuk1tHM3Ebh\/FjYuLCzt37sTMzIxbt24xatQobt26RcmSxWhBXhElEkADqFatGh4eHuzevVskgJmRFOBaXf8FoE7Uf0mAqRWYWBg1vAIh7AavJ39arY5xc\/cwtGNtapZPuw2epbkpjaq5s3T3FXxrlKZ5TQ+i4lR8t+wYjjYWDO9c53lLGUJviASwGFCr1QwePJjly5czd+5cRo8enWFbc4U53nY+eNv5IMsyidp41LIahaTASmkjCjwXcwqFgrfe0r9n2NvrtxR88uSJSACLAJEAGoDYFi6XTC31X4KeNhkS0q7Wnb\/tAo9CYtj7Y\/8MT132aVfe+WET\/tNfzvOrUMqBY7P8qVDK4WXD+GegVesX6QhFUkJCAn369GHPnj2sWrWKvn37ZvtcSZKwMrHJw+iEwszDwwPQJ4D16xu+rqSQv4rhPba84efnx7Vr1wgMDDR2KEJhFR\/G66N\/4TGJfPX3Eb4c2ARXh4znl9pameFV1oUx3XzY8HUP\/hjvh0ar4+2vNxAWnfBKSxkSRUmYoioiIoL27dtz6NAh\/vvvvxwlf4KQFRcXF8zMzHjy5ImxQxEMQCSABtK2rX5F3J49e7JuLAjpSWfl7xdLDuNka8m4tzP+tK3R6mj3ySrsrc2ZM86PHs2qMqqrD3t\/7Me9p5HMWHMq9QlaUbqhKAoMDKRFixbcunWLAwcO0L59e2OHJBQxCoWC0qVLiwSwiBAJoIG4uLhQr149cRtYyD1F6rlWd55EsGD7Rcb3qEdQeCwPg6N4GBxFUrIWtUbHw+AoImISOXz5MVcfhtLNt3Kq8yt7OFG9jDPHrr02Kl1c6ikWI7du3aJJkybExsZy9OhRGjRoYOyQhCLKw8NDJIBFhPhLYEB+fn4sXLgQnU6HQiFyayGHLFOX1ggMj0Wnkxk\/dy\/j5+5N07z8u\/OZ0KM+jarpd2LQ6tKW1lFrdGi0r20jZylKeBQlZ8+epWPHjpQoUYJdu3alzNMShLwgEsCiQySABuTn58e0adO4dOkSdevWNXY4QmFjagXmds9rJYJ3OVc2ftMzTbMvlh4mNiGZ2aPbUdHdgWS1PsFbdeA6HRpUSGl3\/k4wt55EMLxTnZcnWzqBiXmePg0h\/+zdu5cePXpQs2ZNtm3bJvZ7FvKch4cHp0+fNnYYggGIBNCAfH19sba2Zvfu3dStWxdZlkWlfCFnnCpD8AWQdbjYW\/F20yppmszacAYg1WPtfcrx956rxCQk41evPE8j4vh98zkszUyY2PP5\/EFJoe9fKBLWrFmDv78\/7dq1Y+3atVhbF6PdcgSj8fDwIDAwUPx9KwLEfUoDMjU1pUGDBvz111907doVGxsbpk6dauywhMLEo5F+J5Qc2jy1F1Pfb86tJxF89Oc+Zm88S1Ov0hyd6U9Vz+fFe2Wdvn+h0Js3bx79+vWjb9++bN68WSR\/Qr7x8PAgKSmJiAhRTaCwEyOABqDVavnf\/\/7HqlWrUsrA3L17F51Oh4mJ+CcWcsDeE+w8IDYow0Tw4C8D0xyzNDflS\/+mfOnfNP1+JQXYeYJtKUNGK+QzWZaZOnUq33zzDRMmTODXX38V842FfPVqLUBnZ7EzTGEm3jkMZOXKlalqAOp0+j\/e9erVM1ZIQmFVo1ce9Zt2PqFQeOh0OsaNG8c333zDtGnTmDlzpkj+hHz3agIoFG7i3cMAlEolu3btwtbWNs0bskgAhRyzKQkV\/QzbZ6WOYJ12GzmhcEhOTmbAgAHMmzePhQsXMmXKFDH\/SjAKNzc3lEqlSACLAJEAGoi3tzfbtm1DqXxZy61UqVK4uLgYMSqh0CrbAjwaG6avMk31X0KhIMsy\/v7+rF69GoC4uDi6dOnCxo0bWbt2LUOHDjVyhEJxplQqcXd3FwlgESASQANq0aIFq1atSvm+Vq1aRoxGKNQkCap2ez4SKJHjl6qkAEnBpfhSHAqy0fcnFAqHDx9m+fLlDBgwgBUrVtCmTRtOnjzJzp076dlT3MYXjE\/UAiwaRAJoYD179uSnn34CEBNkhTcjSVC+NTQcCzbPb99KWbxkXzxu7QYNx9Lqg69p1bo1HTp04OTJk3kbr2AQixcvxsTEJGUk8O7duxw6dIjWrVsbOzRBAEQCWFSIJap5YPLHH1HNSUuruuXhzHxIitSv6FSagU0p\/SpP1+pg42bsUIXCwM4dGo2HqAcQcBIi7oAmKW07EwtwrqK\/dexQDiSJNm3asGHDBnbv3s2uXbto0aIFX3zxBe3atRNzyPKILMs8VT0hMPERz1RPiVKHo5W1KCUlDqbOlDAvRWnLspQy90jzM4iJiWHNmjVoNJqUY8nJyZiamub30xCEDHl4eHDlyhVjhyG8IZEAGpI2GR4dgYATdC0bD5GX05bySIyE0OtwbxfYl4UKbfR\/tAUhM5IEjhX0X7IMSVGQEAo6rX5vX2tXMLdPc6u3bdu2bNy4EVnWbxN37Ngx\/Pz8aNSoEQcPHsTCwsIIT6ZokmWZW3FXuBh9mmhNJBIKZFK\/\/uO0sQQmPeZ89AnsTRypbd+QajY1UxLB1atXo1KpUp0THx9P27ZtefjwIZaWlvn2fAQhIx4eHgQEBIhi0IWcSAANJeoRXF2t\/8PM8z1Z063j9sp+rdGP4cISKOUDVbqAqXhzF7JBksDSUf+VhXr16qUkf6CvWQn6kiJiVMlwYtRRHAjbQbDq5W2x15O\/149HayI5HL6L23FXae3SCTtTB2bMmJHSzsTEBI1Gg6WlJa1bt071cxQEY\/Lw8CA+Pp6YmBjs7e2NHY6QSyIBNISnF+Da2uff5ORNWn55ftQjqDcMLMSLSTCcWrVqoVQqUxI\/AH9\/fxYtWpRqxbqQe89UT9kWvAaNrM7V+SGqINYF\/U09mnPnzh0AqlSpQvfu3enUqRNNmjTBzMzMkCELwht5tRagSAALL5EAvqmQq3BtzRt2IuvnCZ5bAA1Gg5nY1kkwDEtLS6pVq8a1a9cwNTXFxsaGu3fvits2BhKe\/IytwavRyhrkHH34e0lGRiOrOSsdZs7fv9GpeRfKly9v4EgFwXBeTQC9vLyMHI2QWyIBfBNJUXBtDWduPeXv3Vc4cOkxD0Oicba1pHF1d74f1IIqHk7pnqrWaKk94i9uPA5nxvDWTOrTSN\/fzU1QK+1WX4KQW126dCEhIYENGzagUqlo3rw5H330EXPmzDF2aIWaRqdhz7MtGSZ\/SfEq9iw4xIOLj3l4KYCE6ETem9GXJr3rp2krI6OVNTi3scHT3TM\/wheEXCtVqhSSJBEQEGDsUIQ3IMrA5JYsw\/X1IGv5cfVJ1h+9Rdu6ZZk9qh3DO9fm8JUAfEYt4eqD0HRP\/33TOR4\/i3mtTx08uwohYnWVYDj\/93\/\/x71796hTpw6NGjXit99+Y+7cufz777\/GDq1QOx99gmhNZIYjf3GR8fz3216C7z7Do3rWezDLyERrIjkXfdzQoQqCQZmamlKyZMlU258KhY9IAHMrJgAi7oKs46NeDXi0bDS\/jWnP0E61+WJgU478OhCNVsf01Wlrrz2LjGfqsmP8750Mdnq4t0efYAqCAUiSlOqW74gRI3j\/\/fcZMWIEly5dMmJkhZdKm8Sl6DOZtrF3tePH018y7dhn9JrSOdt9X44+i0qbTpkfQShARC3Awk8kgLkVcCKl6G4TLw\/MTFNPqK\/s4YRXORduPA5Lc+qniw9S1dMJ\/7YZzJ1ICIWoh4aOWBAAfUI4b948qlatSs+ePYmMjDR2SIXO7fhr6NBm2sbU3AR7V9sc961Dy624q7kNTRDyhUgACz+RAObGi1u16ZZ5ed5ElgmJTMDFzirV8dM3g\/h7z1VmjWqX8e5ckkLcBhbylKWlJevXryciIoL33nsPnS7j32UhrbtxN\/K2\/\/i87V8Q3pRIAAs\/kQDmRnwo6DSZNlm+7xqBYbG806payjFZlhk3dw\/vtKyOb43SGZ8s6\/S3mAUhD1WoUIHly5ezbds2\/u\/\/\/s\/Y4RQaOllHePKzPL1GeHIoukw+YAqCsYkEsPATCWBuxD3N9OGbj8MZ8\/sefGuU5v32NVOOL911hSsPQvlxaKtsXCP4DYMUhKx16tSJr7\/+mi+\/\/JLdu3cbO5xCIUYThTaL279vSoeWaLW4NS8UXB4eHkRFRREXF2fsUIRcEglgbqS3D+tzwRFxdP5iLfbW5qz78m2USv0\/cUy8iil\/HWJyn0Z4lrDL+ho6jX6bL0HIY1999RUdOnSgf\/\/+PHz40NjhFHjJOlXWjQxxHTl\/riMIufGiFqBYCVx4iQQwV9L\/Z4uOT6LjZ2uIikti5\/\/1xd3l5QTwn9eeIlmj5Z1W1XkYHMXD4CiehMUCEBmbxMPgKJLVryV8olivkA8UCgXLli3Dzs6O3r17k5QkVqBmRiJ\/XpcK8fYsFGCvFoMWCidRCDo3zNOu7EtK1tD1y3XcDoxk74\/9qFHWJdXjj5\/FEBmbhNfQRWnOnbbyBNNWnuDCvEHUqeSmP2hqlbLKWBDympOTExs2bKBJkyaMGzeOhQsXGjukAstKaVOkriMIueHu7g6IBLAwEwlgbtilXsCh1ep45\/tNnLgexOZve6W7wGN8j\/q83bRKqmPPohIYMWsnH\/jVpHuTypQv9WJPRQnsxG4AQv6qW7cu8+bNY9CgQTRq1IihQ4caO6QCydrEBguFJUm6xDy7hoXCEmsTkQAKBZeFhQUuLi4iASzERAKYG+Z2+i+VfiePj\/\/cz5YTd+nauBIRsYks25u6hpd\/O298KpfEp3LJVMcfBkcB4FXOJU1yiH2ZPAtfEDLywQcfcOLECcaOHUudOnWoXz\/ttmUClLTw4FHC3Sz3\/z3w9zESY5KIer7rz5V914l6Gg1A6\/ebYGlnmeYcCQk3c3fDBy0IBiZWAhduIgHMrdIN4f4+QObivRAAtp68y9aTd9M09W\/nnfP+3X3eMEBByJ3ffvuNCxcu0Lt3b86dO4ezs7OxQypwqtnU5GHCnSzb7Vl4mIjAl6t5L+y8yoWd+g+IDXvUTTcBlJGpblvbcMEKQh4RCWDhJhLA3CrdAB7sB1nm4C8Dc9VFuZIOyHs+TX1QUoBzVbBwePMYBSEXzM3NWbduHT4+PgwcOJD\/\/vsPpVKZ9YnFiKdleayVtsRr4yCTUcBpR6fkuG9rpQ2eluXfIDpByB8eHh6cPJl2u1OhcBCrDHLL3A7Kt86DjiWo0ikP+hWE7CtTpgyrVq1iz549fPvtt8YOp8BRSAqaO7cns+Qvt5o5t0chFoAJhYAYASzcxLvMmyjXGqzdDLtat3JHsHLJup0g5LF27drx\/fff891337Ft2zZjh1PglLWqSGXrGgYrCyMhUcm6BuWsKhmkP0HIax4eHoSFhYnSUYWUSADfhEIJdT8AUxvDJIHuDcCzyZv3IwgG8r\/\/\/Y\/u3bvj7+\/P3btp57cWd82d\/ShhXuqNk0AJiRLmpWjh7GegyAQh74li0IWbSADflIUDNBgJlk6Qqz8Cz8\/xbArV3xbFn4UCRaFQ8Pfff+Pq6kqvXr1ISEgwdkgFiqnClM5ufShtUVZ\/IJd3hEtblKGzWx9MFaaGC04Q8pgoBl24iQTQECwdodF4KNsckLI1GijLIMsymFlDnQ+gahdR+FkokOzt7dmwYQN3795lxIgR+t9bIYWpwozykV6s\/nozslbO9mighIRSMqGZUzs6ufXBVGGWx5EKgmGVLq2veSsSwMJJrAI2FKWpfv6eez14cgqCzoI2Wf+YpEA\/0ieDrAMgRmvOJ3M2M2HaX9RwqWq0sAUhO2rWrMmiRYsYMGAAvr6+jB492tghFRg6nY4Rw0cQEvKMd0oN4YH6NldjL5CojQdSb+mmQ\/\/6t1Ra421bl2o2NbESBZ+NRq1LJkQVRFhyCFHqCLSyFoWkxMHECVdzN0qYu2MmEvMM2djY4ODgIBLAQkokgIZmXQKqdtUng3HBEBMISZH6xE9pBtYlwa40lgortg1ZiO73P8S2W0Kh0L9\/f06ePMnEiROpW7cuvr6+xg6pQFiwYAFHjx7lwIEDOFo744gvdewbEa2OJDQ5OCWxUEpKHEydcDVzw97USaz0NaLI5HCuxp7nVtxVtLLm+ajt8w\/pz8nIKCUTqljXwNuuHk5mYnFeesRK4MJLkrNxPycmJgZ7e3uio6Oxs7PLj7iKhWnTpvHdd98REBCAi4t4cxEKvuTkZNq0acODBw84f\/48bm5uxg7JqAIDA6lRowZ9+vRh0aK0+3wLBYtW1nI+6gQXovW167LayQVIuaVfx74R9Rx8UUpi3ORVHTt2xMLCgo0bNxo7FIGc5WviI6gRDR8+HNCPIAhCYWBmZsaaNWvQarX069cPjUZj7JCMaty4cVhaWjJjxgxjhyJkIVGbwKanyzkffQL5+X\/Z8aLtheiTbAj6l4Tnt\/YFPTECWHiJBNCIXFxcePfdd5k7dy7JycnGDkcQssXd3Z01a9Zw5MgRPvvsM2OHYzQbNmxg48aN\/P777zg6Oho7HCETKm0SW4NXEZ787I36iVSHs+XpShK1YjX8CyIBLLxEAmhkEyZMICgoiHXr1hk7FEHIthYtWjBjxgxmzJjB+vXrjR1OvouKimLs2LF07dqV3r17GzscIROyLHMgbDtR6og0o34PLwWw8qtNfOv3C+NrfM6UptNYMGYZIfdD0+8LmRhNFPtD\/xOr4Z\/z8PAgJCREDGIUQiIBNDIvLy\/at2\/PzJkzxRuKUKhMnDiRvn378sEHH3Dz5k1jh5OvPv30U2JjY5k7dy6SqN1ZoN2Nv8GjxHvp3vLdNf8gF3ZeoVqTSvT9qhvN+zXi7un7TOs6m8Bbwen2JyPzJOkht+Ku5nXohYKHhweyLPP06VNjhyLkkEgAC4CJEydy9uxZTpw4YexQBCHbJEli8eLFeHp60rNnT+Li4owdUr44cuQIf\/75J9OnT8fT09PY4QiZ0Mk6TkQcyPDxdkObM+3oFN75pjvN+jWi07i2fLxmFFqNjl3zMj4P4GTkQbRy8Z4DC6IYdGEmEsACoEOHDlSpUoVZs2YZOxRByBEbGxs2bNhAQEAAQ4YMSTuKLesgPhSeXoC7u+DWVri9HQKOQ\/Rj0KqNE3guJSUlMWzYMHx9fRk1apSxwxGy8DDhDom6jOfrVaxXDhOz1Kt63cq74l7FjeB7mc8XVOmSuB9\/2yBxFlaJ2gSUbjIdRrfmjuIKxyP2cz7qBI8S7onFMoWAWM9eACgUCiZMmMC4ceN49OgRZcuWNXZIgpBt1apVY+nSpfTu3ZvGjRvz4YcfgjoRgs7pE72kSH3DlLp3Esha\/f9VmIJ7ffBsrK+hWcBNmzaN+\/fvs379ehQK8fm5oLsVdxUJKdsrfkE\/ZzAmLA73ypmXOJKQuBV3hco2Nd40zEJFlmUCEh9wNfY8AYkPAOj2UQdUijiuxVxItcLa3aIM3rY+lLWqKOpeFkDiJ1JAvPfee9jZ2TF37lxjhyIIOdarVy8mT57M5MmTuXZwDRyfAXf+e5n8gX40UNa9TP4AdGoIPAUnZsGdHQV6RPDatWtMnz6dTz\/9FC8vL2OHI2RBlmVCVEE5Sv4ATm+6QFRwNPW61M68f2SeqZ4Wq7nbMeootgavYsez9TxJfJhyXKGUQJLRoUv17\/00KYDdoZvY\/HQFUeoII0QsZEYUgi5APvnkExYuXEhAQAA2NmJ7KKFw0SSr2DV7FJ3r5rY4tARWzuAzBCwcDBnaG9NqtTRr1oyoqCguXryIubm5sUMSshCviWXZk\/k5Oif43jOm95iDe2U3Jq0ZhUKZ9RhJv9JDsTct+mWAHibcZW\/oVnSyNsdJtfT8v9YunahkUz2PIhRAFIIutMaOHUtsbCz\/\/POPsUMRhJyRdZjcXE+nXCd\/ADIkRsCZeZAUbbDQDGHevHmcPHmShQsXiuSvkMhprb7o0FjmDP4LS1sLhv\/hn63kDyBJm5ib8AqVB\/F32P1sE1pZk+PkD\/SjpTp07Avbxp2463kQoZAbYgSwgOnbty+XLl3ixo0bYo6RUHg8OMCZ7X\/z9+4rHLj0mIch0TjbWtK4ujvfD2pBFQ+nlKZS++kZdtPOpxx7fhoANiWhwWhQKPMj+kwFBARQo0YNBg4cyPz5ORtREownVBXChqfZ+zCdGJPIr\/3\/JCIoio\/XjMpy\/t+rHG97UM6hIu7u7ri5uWFqaprbkAukyORw1gUt5f6lR5xYf47bJ+8R\/iQCa0drytcpQ\/eP38Ktgmuqcw78fYxD\/54gLCAca0dr6nepTbeP3sLcygwJiZ6l3sXFvHhvI5lXcpKviUUgBczEiRNp2rQpO3fupFOnTsYORxCyFhcM9\/fy4+qTHLv2hD4tqlGrfAmCI+OYs\/k8PqOWcPK39\/Aur\/8j8e\/\/uqTp4uztYGZvPItfvfL6eYKxQfD4CJRrlc9PJjVZlhkzZgy2trb8+OOPRo1FSOv27dv89NNPeHt706RJE+rUqYOZmRkAFgqLbPWhVqmZO3QpIQ9CmbhseI6SP4DxoyakFI6WJIkSJUrg7u6e4Vfp0qVxdXUtFB\/wdbKOA2HbkZHZNf8g9849pF6nWpSuVpKY0DgO\/nOMaV1n88mGsZSuWhKADdO3s\/vPg\/h0rEmbQU15eucZB\/4+xtPbIYz\/ZygA+8P+o5f7+ygl43\/AK87ECGABI8syDRs2xNHRkd27dxs7HEHI2rlFEPWA41cfU79KKcxMX76p33kSQc3hi+ndohrLPu2aYRdDf9nOX7su83j5aDxcn7\/HSEpo\/imYGW8+7Nq1a+nbty8bNmygR48eRotDSN+mTZtS\/VzMzMyoW7cuXl5eNPZtjLJ9PGo544VFOq2O+SP\/4erBm4xa8D41W+dsfpoSJV0tB\/D0aTBBQUEZfoWEhKDT6V6ep1RSqlSpTBNFd3d3nJyc8rTQuFqtJjg4OMN6lrfjrnEgbDsA9849pGxNj1Rlc0IehPJdh5n4dKzJ4Fn9iX4Ww5Sm02jQtQ6Dfu2X0u7A38dY\/c1mRi\/8gFrt9Kummzm1w8uubp49t+JKjAAWYpIkMXHiRPz9\/bl27ZpYbVgQ6LSQEAoxgaCKBWQwMQebUmDrrv\/\/xVV8KETeA6CJl0eahyt7OOFVzoUbj8My7EKVrGH90Vu0rFXmZfIH+pHAoHNQrqXBw86OyMhIxo0bR48ePUTyV0A1a9Ys1ffJycmcOnWKU6dO8ffff7MxYBlBqieQwby1dT9s4\/Le69RqW52EqERObTyf6vFGPXwyvb6zuRtubiVxcytJnTp1Mmyn0Wh49uxZhgnisWPHCAoKIjQ09RZ0ZmZmWSaJ7u7u2NnZ5SpRXLp0KcOHD2fkyJFMnz4de3v7VI9fjTmfUkanYr1yac5\/vWbi\/fOP0Gl01O+aegV1g651WP3NZs5su5iSAF6JOUcN2zpiJx0jEglgAdSnTx8mT57Mb7\/9xp9\/\/mnscIqvuBB4cgqengPt830uX9SykmX0f1QkcK4Cnr7gXPmVWnfFxNPz+ucs69J9WJZlQiIT8CrrkmEX20\/fIypOxcA2r3\/YkSHwtNESwMmTJ5OYmMicOXOMcn0hfbIsc+vWLQ4ePMjBgwdRKpVotS9LC0mShL29Pf\/99x9ONrYEqQIy7CvgehAAl\/fd4PK+G2kezyoBrGKdvRqAJiYmKclaZpKTkwkOTn80MTAwkBs3bhAUFERkZGSq86ysrLKVKFpbW6c6786dOygUChYuXMj69euZP38+PXv2BPRz\/0KT098O74XXayZqkvU7o5hZpJ4HaWap\/\/7xlcCUY9GaSEKTgylhXirTawh5RySABZCZmRljxozh+++\/Z9q0aTg7Oxs7pOJFo9LXpAs8lTa5SZPoyBB+B8JvgZ0HePUFa1eKjaiHGSZ\/AMv3XSMwLJap7zfLuM3+65ibKundomraBxMjQJ0AplYGCDb7Dh48yOLFi5k\/f36Wf7SFvPV6wnfw4EFCQkJQKpU0aNCAOnXqcPHiRbRaLUqlEi8vL3bs2IG7uztqnZrjEftRy8np9v3xqpG5jstEMqGyjWHv0JiZmVGmTBnKlCmTabvExESePn1KYGBgusni+fPnCQwMJD4+9W4cdnZ2qRLC8+fPI8syOp2OsLAwevXqRefOnZk\/fz5x9pEZXP2lFzUTu37oB5CyGOTe2UdU9a2U0u7OaX3B6KiQ1Kv7Q1RBIgE0IpEAFlDDhw\/n+++\/Z8GCBUyZMsXY4RQfcSFwYQmoYvTfZ5LcvPS8TWwQnJwN1XuAe708C7HAkGX9c87AzcfhjPl9D741SvN++5rptomJV\/HfqXt0algRB5sMJu3HBIFzpfQfywOJiYkMHz6c5s2bM2zYsHy7rqAnyzI3b95MSfYOHTpESEgIJiYmNGjQgEGDBtGqVSuaNm2KjY0N69evp3fv3kiSRMeOHVm1alXKSJepwpT6Dk04EXnQ4HHWtffFTGFm8H6zw9LSkgoVKlChQoVM28XGxmZ42\/nhw4fcv38\/pZD1i\/\/977\/\/KFOmDFvvrUUhKdCR\/ntg8L1nrPx6ExV8yuLbS\/9+V8bbg\/J1yrDrzwM4lLSjim9Fgu8+Y8UXG1GaKlEnvdw7WUIiTBViiH8OIZdEAlhAubq64u\/vz5w5c5g0aVKRKy1QIMUFw9k\/QZNMRnOGMvUiWby+Tj9v0KOhQcMrcHTql7fGXxMcEUfnL9Zib23Oui\/fRplBTbX1R26RlKxhYNtMbqUlxxgi2mz77rvvePToEVu2bCkUKzULu9cTvoMHD\/Ls2bOUhG\/w4MG0atWKJk2apFsgv1WrVjg4OPDBBx\/w888\/o1SmXlnqbVePe\/G3CE0OzlUNu9dJSDiZulLHvuC\/vm1tbalatSpVq6Yzug5UqFCBBw\/0o3MvbqXb2dnRsmVLlJYSuqT0k7\/MaiYOn\/cui8Yt559P1gKgUCpoO6Q5d07dT1ktDfragHHaWEM9VSEXRAJYgE2YMIFFixaxbt06+vfvb+xwijZNEpz\/63nyl\/6b3vk7wXzzz1GOXntCUrKGCqUcGN6pDuN71E\/b+OZGsHIBp8w\/oRdqGRQQiI5PouNna4iKS+LITH\/cXWwz7GL5\/mvYW5vTpVEmI3z5uNXW5cuXmTFjBl9++SXVqlXLt+sWJ7Isc+PGjVQjfC8SvoYNGzJkyJBME77XOTs7ExYWlibxe0EhKWhXoisbg5aRpEt8oyRQQsJcYUH7Et2KxN62ISH6ETgTExN69OjB4MGDad++PUqlkl0hG9M9JzEmkTkfLCYxJomP14zCwS31whHHkvZMXjuakAehxITGUaK8C\/autvyv0XeUKJ96ekxx2kavIBIJYAHm7e1Nu3btmDlzJv369ROrpfLS7W2QHEdGI3+7zz6g61frqFvRjS8HNsHG0ox7QVE8CcvoE6wE19aA74dFd5WwwgSQePXfLClZQ9cv13E7MJK9P\/ajRiaLP56Gx3Hg0mM+8KuJuVkmb0Um2avn9qa0Wi1Dhw6latWqfPrpp\/lyzeLg9YTv4MGDhIaGpiR8Q4cOTUn4Xl+kkF0ZJX8v2JrY073UALYGryJBG5+rJFBCwkJhSdeS\/YrM1m+fffYZ9vb2DBgwACcnp1SPmSnMU1YAv5CTmolu5V1xe57wBd0JIfpZLL69X\/2wLGGuzJ\/XtpA+kQAWcBMnTqRLly6cPHkSX19fY4dTNMU80ZcbyejheBXv\/bSNzg0rsu6rHigU2UnEZf08wkeHoWJ7w8VakCiUYF0C4vWjCFqtjne+38SJ60Fs\/rYXvjVKZ3r6qoPX0elkBrbJYiWlbf5MEp8zZw5nz57l2LFjKcWEhZyTZZnr16+njO69mvA1atSIYcOGvXHClxv2po70dv+AYxH7uBt\/I01yk5EX7cpbVaaZc3sslfm7ICkvff755xk+5mxWgjvxL7dt02l1LBy7nPsXHjFqwftU8CmbrWvodDo2Tv8PM0tTWgxonHJcAlzMSuQ6duHNiQSwgOvYsSOVK1dm1qxZIgHMKwEnMi1lsmL\/dUIi4\/lhUAsUCon4xGQszU2zkQjK8OQklG\/9fLSsCLIvo6+RKOv4+M\/9bDlxl66NKxERm8iyvVdTNfVv553q++X7ruPubEOr2pn8IVGag0Xej7Y8evSIzz\/\/nNGjR+fr60ylTSIsOYRYTTQ6WYeJwhQnUxcczVwKzS4JryZ8L5K+0NBQTE1NadiwIcOHD6dVq1b4+vrma8KXHgulJW1du1DFxotL0acJTHoMgILUix0kFMjPvy9p7kFt+waUtapolJiNxdW8ZKoEObs1E1d\/uxmNSoNHDXe0ai1ntlzk4aUA3v+5L06lX76WZWRczMR2cMZURP8qFR0KhYIJEyYwYcIEHj9+nGV5ACGHNCoIvpTpat+9Fx5iZ2VOYHgcb3+zgdtPIrC2MOXddt7MHNUWi8xuX6oTIOwWlCiiBb3dakLQGQAu3tOPBG49eZetJ++mafpqAngrIJxzd4L5qFeDjBNpSQEl60AeT32QZZlRo0bh4ODAtGnT8vRaAMk6FXfirnMt9gKR6vB020go8LQsh5etD56W5QrU9A+dTpcq4Tt8+HCBTfgy4mlZHk\/L8kSrIwlMekSoKoRIdRhaWYNSMsHR1BlXs5K4W5bBwdQp6w6LIDdzdywVViTqEoDs10z09CrN\/r+OcHrzBSSFRLnankxcPixVWRgAM8mM0hbZG0UU8obYCq4QiIuLw8PDgxEjRoj9SA0t8gGcW5Bpk9ojFnM3KAqAIR1q0apWGQ5efszvm87Rr1V1Vn7ePeOTJQWUaQaVOxow6AJE1sGxnyEp65phudJofJ7fAl65ciUDBgxg8+bNdOvWLc+uI8syt+KucjxiX6bbk73w4tajk6kLbVw742yk22U6nY5r166ljO4dOnSIsLAwTE1NadSoEa1atUpJ+Kysis7tUQHORR3nXNRxg6yefpWERC27+jR2amXQfgWxFVyRY2Njw7Bhw1iwYAFfffVVgf1UXSjFBvL6QobXxSWqSUhSM7JLXX4bo5\/P17N5VZLVWv787yJT329OZY8MRglkHURnvBNBoScpoJIfXF1t4I4V4Fo9z5O\/8PBwJkyYQJ8+ffI0+UvWJbP32RYCkh5k+5wXf3Qj1eGsD\/qHxo4tqWXfIK9CTPFqwvci6QsPD8fU1JTGjRszatQoWrZsKRK+YsDLti5XYs6h0iUZtF+lZEJNu3SqJwj5qvCvYy8mxo4dS0xMDH\/\/\/bexQylakqKzvMVoaa7\/nNT\/tY3iBzxfvHDiRmCac1JfI49GxwoKt9rgUs2w2+CZmEG1TEZWDeTjjz9GrVbz22+\/5dk1knXJbA1exZOkh7k6X37+34nIg5yJPGrY4NAnfJcvX+a3336jZ8+elChRglq1ajFp0iRCQ0MZPXo0+\/btIyoqisOHDzN16lTatm0rkr9iwEJpSQtnP4P328y5HdYmWZf4EfKWGAEsJMqWLUvPnj357bffGDlypChQayiyDv0IYMbcnWy49jAMN8fUI68lHPTfR8Zm8ek4W7uJFGKSBDV6wek\/QBVtgOcrgXc\/MM+4fqAh7N27l7\/\/\/puFCxdSsmTJPLmGLMvsC93KlauX2TprN4+vBhIdGouZpSmlKrnhN7wltdrpP0jodDpObjjPxZ1XCLgeRHxUAi6eTtTvUof2w1tgam7K+egT2Js6UMXGO4srZ0yn03H16tVUI3wRERGYmZnRuHFjxowZQ6tWrWjcuDGWlpaG+qcQCqkK1lXxSqrLtdgLBumvirUXVayL6JzoQkYkgIXIxIkTadasGbt27aJjxyI6pyy\/mZiT1a4f9aqUZM\/5hwSGxVLV8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\" \/><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>OR-Tools\u306e\u25cb\u25cbmile\u3068\u3044\u3046\u8868\u8a18\u306f\u5c0f\u6570\u70b9\u4ee5\u4e0b\u304c\u5207\u308a\u6368\u3066\u3089\u308c\u3066\u3044\u308b\u306e\u3067\u3001\u6b63\u3057\u3044\u8ddd\u96e2\u3092\u8a08\u7b97\u3057\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"c1\"># \u5404\u30eb\u30fc\u30c8\u306e\u8ddd\u96e2\u3092\u8a08\u7b97\u3059\u308b<\/span>\n<span class=\"n\">distance_sum<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n<span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">solutions<\/span><span class=\"p\">)):<\/span>\n    <span class=\"nb\">sum<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">0<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\">j<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">solutions<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">])<\/span> <span class=\"o\">-<\/span> <span class=\"mi\">1<\/span><span class=\"p\">):<\/span>\n        <span class=\"nb\">sum<\/span> <span class=\"o\">+=<\/span> <span class=\"n\">distance_matrices<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">][<\/span><span class=\"n\">solutions<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">][<\/span><span class=\"n\">j<\/span><span class=\"p\">]][<\/span><span class=\"n\">solutions<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">][<\/span><span class=\"n\">j<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span><span class=\"p\">]]<\/span>\n    <span class=\"n\">distance_sum<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"nb\">sum<\/span><span class=\"p\">)<\/span>\n\n<span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">distance_sum<\/span><span class=\"p\">)):<\/span>\n    <span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"sa\">f<\/span><span class=\"s2\">\"<\/span><span class=\"si\">{<\/span><span class=\"n\">i<\/span><span class=\"o\">+<\/span><span class=\"mi\">1<\/span><span class=\"si\">}<\/span><span class=\"s2\">\u756a\u76ee\u306e\u30eb\u30fc\u30c8\u306e\u8ddd\u96e2\u306e\u7dcf\u548c: <\/span><span class=\"si\">{<\/span><span class=\"n\">distance_sum<\/span><span class=\"p\">[<\/span><span class=\"n\">i<\/span><span class=\"p\">]<\/span><span class=\"si\">}<\/span><span class=\"s2\">\"<\/span><span class=\"p\">)<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">(<\/span><span class=\"n\">distance_sum<\/span><span class=\"p\">))<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>1\u756a\u76ee\u306e\u30eb\u30fc\u30c8\u306e\u8ddd\u96e2\u306e\u7dcf\u548c: 113.02515612536826\n2\u756a\u76ee\u306e\u30eb\u30fc\u30c8\u306e\u8ddd\u96e2\u306e\u7dcf\u548c: 110.42427768703959\n3\u756a\u76ee\u306e\u30eb\u30fc\u30c8\u306e\u8ddd\u96e2\u306e\u7dcf\u548c: 108.17151038656631\n4\u756a\u76ee\u306e\u30eb\u30fc\u30c8\u306e\u8ddd\u96e2\u306e\u7dcf\u548c: 90.23091471949962\n5\u756a\u76ee\u306e\u30eb\u30fc\u30c8\u306e\u8ddd\u96e2\u306e\u7dcf\u548c: 115.51879142816513\n537.370650346639\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span><\/span><span class=\"n\">bks<\/span> <span class=\"o\">=<\/span> <span class=\"n\">vrp_solution<\/span><span class=\"p\">[<\/span><span class=\"s2\">\"cost\"<\/span><span class=\"p\">]<\/span>  <span class=\"c1\"># \u53b3\u5bc6\u89e3<\/span>\n<span class=\"n\">ans<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">(<\/span><span class=\"n\">distance_sum<\/span><span class=\"p\">)<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">((<\/span><span class=\"n\">ans<\/span> <span class=\"o\">-<\/span> <span class=\"n\">bks<\/span><span class=\"p\">)<\/span> <span class=\"o\">\/<\/span> <span class=\"n\">bks<\/span> <span class=\"o\">*<\/span> <span class=\"mi\">100<\/span><span class=\"p\">,<\/span> <span class=\"s2\">\"%\"<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt\"><\/div>\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>2.4322117429178913 %\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u53b3\u5bc6\u89e3(BKS)\u3068\u306e\u504f\u5dee\u306f+\u7d042.4%\u3068\u3044\u3046\u7d50\u679c\u306b\u306a\u308a\u307e\u3057\u305f\u3002<\/p>\n<p>\u5143\u8ad6\u6587\u306e\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9\u306a\u624b\u6cd5\u3067\u306f\u3001\u53b3\u5bc6\u89e3\u3068\u306e\u504f\u5dee\u304c5.98%\u3067\u3042\u3063\u305f\u306e\u3067\u3001\u304b\u306a\u308a\u826f\u3044\u7d50\u679c\u3092\u5f97\u3089\u308c\u305f\u3068\u3044\u3048\u308b\u3067\u3057\u3087\u3046\uff01<\/p>\n<p>\u3057\u304b\u3057\u3001\u3088\u308a\u30b5\u30a4\u30ba\u306e\u5927\u304d\u3044\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u89e3\u3044\u3066\u307f\u3088\u3046\u3068\u3059\u308b\u3068\u3001\u5bb9\u91cf\u5236\u7d04\u3092\u6e80\u305f\u3057\u3066\u3082\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u304c\u3046\u307e\u304f\u3044\u304b\u305a\u3001\u53b3\u5bc6\u89e3\u306e\u504f\u5dee\u3082\u5143\u8ad6\u6587\u306e\u624b\u6cd5\u3088\u308a\u60aa\u3044\u7d50\u679c\u3068\u306a\u308a\u307e\u3057\u305f\u3002\uff08\u8a73\u7d30\u306f\u7701\u304d\u307e\u3059\u3002\uff09<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2><span class=\"ez-toc-section\" id=\"%E3%81%BE%E3%81%A8%E3%82%81\"><\/span>\u307e\u3068\u3081<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u4eca\u56de\u5b9f\u969b\u306b\u8ad6\u6587\u3067\u6271\u308f\u308c\u3066\u3044\u305fCMT1\u3068\u3044\u3046\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306fSA\u3001\u30eb\u30fc\u30c6\u30a3\u30f3\u30b0\u306fGoogle\u306eOR-Tools\u3092\u4f7f\u3063\u3066\u89e3\u304d\u307e\u3057\u305f\u3002<\/p>\n<p>CMT1\u306b\u5bfe\u3057\u3066\u306f\u53b3\u5bc6\u89e3\u3092\u5f97\u308b\u3053\u3068\u306f\u3067\u304d\u307e\u305b\u3093\u3067\u3057\u305f\u304c\u3001\u53b3\u5bc6\u89e3\u306b\u304b\u306a\u308a\u8fd1\u3044\u89e3\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002\u3057\u304b\u3057\u3001\u30b5\u30a4\u30ba\u306e\u5927\u304d\u3044\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u5bfe\u3057\u3066\u306f\u5236\u7d04\u3092\u6e80\u305f\u3059\u826f\u3044\u89e3\u3092\u5f97\u308b\u3053\u3068\u304c\u96e3\u3057\u304b\u3063\u305f\u3067\u3059\u3002<\/p>\n<p>\u4eca\u5f8c\u306e\u5c55\u671b\u3068\u3057\u3066\u306f\u3001SA\u306b\u3088\u308b\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u5f8c\u306b\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u3092\u6539\u5584\u3059\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u5c0e\u5165\u3059\u308b\u3068\u3044\u3063\u305f\u3053\u3068\u304c\u8003\u3048\u3089\u308c\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2><span class=\"ez-toc-section\" id=\"%E3%81%82%E3%81%A8%E3%81%8C%E3%81%8D\"><\/span>\u3042\u3068\u304c\u304d<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u4eca\u56de\u5b9f\u969b\u306b\u8ad6\u6587\u306e\u554f\u984c\u3092\u89e3\u304f\u306b\u3042\u305f\u3063\u3066\u3044\u304f\u3064\u304b\u58c1\u306b\u5f53\u305f\u308a\u307e\u3057\u305f\u3002<\/p>\n<ol>\n<li>\u8ad6\u6587\u901a\u308a\u306b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u5b9f\u88c5\u3059\u308b\u3082\u5bb9\u91cf\u304c\u304d\u3064\u304d\u3064\u3067\u3001\u8377\u7269\u306e\u5165\u308c\u66ff\u308f\u308a\u304c\u8d77\u3053\u3089\u305a\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u304c\u3046\u307e\u304f\u3044\u304b\u306a\u3044\u3002<\/li>\n<li>\u81ea\u5206\u3067\u91cf\u5b50\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u30de\u30b7\u30f3\u3092\u5229\u7528\u3057\u3066\u89e3\u3053\u3046\u3068\u3059\u308b\u3082\u5236\u7d04\u3092\u6e80\u305f\u3057\u3066\u304f\u308c\u306a\u3044\u3002<\/li>\n<li>\u8ad6\u6587\u306e\u5b9a\u5f0f\u5316\u901a\u308a\u306e\u5b9f\u88c5\u3092\u3057\u3066\u3082\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u304c\u3046\u307e\u304f\u3044\u304b\u306a\u3044\u3002<\/li>\n<\/ol>\n<p>\u3068\u3044\u3063\u305f\u611f\u3058\u3067\u3059\u3002\u4f55\u56de\u304b\u5fc3\u304c\u6298\u308c\u307e\u3057\u305f\u304c\u3001\u5b9f\u969b\u306b\u304b\u306a\u308a\u826f\u3044\u7d50\u679c\u304c\u5f97\u3089\u308c\u3001\u305d\u308c\u3092\u63cf\u753b\u3057\u305f\u6642\u306b\u306f\u9054\u6210\u611f\u304c\u3042\u308a\u307e\u3057\u305f\u3002<\/p>\n<p>\u4eca\u5f8c\u3082\u540c\u69d8\u306b\u518d\u73fe\u5b9f\u9a13\u3092\u901a\u3057\u3066\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u306e\u6280\u8853\u3092\u78e8\u3044\u3066\u3044\u304d\u305f\u3044\u3067\u3059\u3002<\/p>\n<p>\u307e\u305f\u3001\u4eca\u56de2\u672c\u76ee\u3068\u3057\u3066\u7d39\u4ecb\u3057\u305f\u8ad6\u6587\u3084\u3001\u5bb9\u91cf\u5236\u7d04\u3067\u7528\u3044\u305f\u4e0d\u7b49\u5f0f\u5236\u7d04\u306b\u95a2\u3059\u308b\u8ad6\u6587\u3092\u8aad\u3093\u3067\u3001CVRP\u3068\u3044\u3046\u554f\u984c\u306b\u5bfe\u3057\u3066\u77e5\u8b58\u3092\u5897\u3084\u3057\u3066\u3044\u304d\u305f\u3044\u3067\u3059\u3002\u5b9f\u793e\u4f1a\u306b\u5373\u3057\u305f\u554f\u984c\u3092\u89e3\u3051\u308b\u3088\u3046\u306b\u9811\u5f35\u308a\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff01<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>\u6982\u8981 \u8a18\u4e8b\u300c\u91cf\u5b50\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u30de\u30b7\u30f3\u306b\u3088\u308b\u914d\u9001\u8a08\u753b\u300d\u3067\u6271\u3063\u305f\u8ad6\u6587\u3067\u306f\u3001CVRP(\u5bb9\u91cf\u5236\u7d04\u6709\u308a\u306e\u914d\u9001\u8a08\u753b\u554f\u984c)\u3092\u91cf\u5b50\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u30de\u30b7\u30f3\u3068\u53e4\u5178\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u3092\u7528\u3044\u305f\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9\u306a\u624b\u6cd5\u3067\u89e3\u304d\u3001\u305d\u306e\u6027\u80fd\u3092\u53e4\u5178\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u3068\u6bd4\u8f03\u3057\u3066\u3044 [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-6607","post","type-post","status-publish","format-standard","hentry","category-hands-on"],"yoast_head":"<!-- This site is optimized with the Yoast SEO 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