{"title":"Shadoks Knapsack 多边形打包方法","authors":"Guilherme D. da Fonseca, Yan Gerard","doi":"arxiv-2403.20123","DOIUrl":null,"url":null,"abstract":"We describe the heuristics used by the Shadoks team in the CG:SHOP 2024\nChallenge. Each instance consists of a convex polygon called container and a\nmultiset of items, where each item is a simple polygon and has an associated\nvalue. The goal is to pack some of the items inside the container using\ntranslations, in order to maximize the sum of their values. Our strategy\nconsists of obtaining good initial solutions and improving them with local\nsearch. To obtain the initial solutions we used integer programming and a\ncarefully designed greedy approach.","PeriodicalId":501570,"journal":{"name":"arXiv - CS - Computational Geometry","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Shadoks Approach to Knapsack Polygonal Packing\",\"authors\":\"Guilherme D. da Fonseca, Yan Gerard\",\"doi\":\"arxiv-2403.20123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe the heuristics used by the Shadoks team in the CG:SHOP 2024\\nChallenge. Each instance consists of a convex polygon called container and a\\nmultiset of items, where each item is a simple polygon and has an associated\\nvalue. The goal is to pack some of the items inside the container using\\ntranslations, in order to maximize the sum of their values. Our strategy\\nconsists of obtaining good initial solutions and improving them with local\\nsearch. To obtain the initial solutions we used integer programming and a\\ncarefully designed greedy approach.\",\"PeriodicalId\":501570,\"journal\":{\"name\":\"arXiv - CS - Computational Geometry\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Computational Geometry\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2403.20123\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Computational Geometry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2403.20123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We describe the heuristics used by the Shadoks team in the CG:SHOP 2024
Challenge. Each instance consists of a convex polygon called container and a
multiset of items, where each item is a simple polygon and has an associated
value. The goal is to pack some of the items inside the container using
translations, in order to maximize the sum of their values. Our strategy
consists of obtaining good initial solutions and improving them with local
search. To obtain the initial solutions we used integer programming and a
carefully designed greedy approach.
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