{"title":"具有不确定出行费用的有能力车辆路径问题的蚁群系统","authors":"N. E. Toklu, R. Montemanni, L. Gambardella","doi":"10.1109/SIS.2013.6615156","DOIUrl":null,"url":null,"abstract":"In this study, we consider a capacitated vehicle routing problem where the objective function is to minimize the total travel cost.We also consider that the travel costs between the locations are subject to uncertainty, therefore they are expressed as intervals, rather than fixed numbers. The motivation of this study is to solve this problem by using a metaheuristic approach. We base our approach on a variant of ant colony optimization metaheuristic, called ant colony system, which was originally implemented for solving the deterministic version of the problem (i.e. the classical version of the problem without the uncertainty), previously reported in the literature. We modify the algorithm to incorporate a robust optimization methodology, so that the uncertainty on traveling costs can be handled.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"An ant colony system for the capacitated vehicle routing problem with uncertain travel costs\",\"authors\":\"N. E. Toklu, R. Montemanni, L. Gambardella\",\"doi\":\"10.1109/SIS.2013.6615156\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, we consider a capacitated vehicle routing problem where the objective function is to minimize the total travel cost.We also consider that the travel costs between the locations are subject to uncertainty, therefore they are expressed as intervals, rather than fixed numbers. The motivation of this study is to solve this problem by using a metaheuristic approach. We base our approach on a variant of ant colony optimization metaheuristic, called ant colony system, which was originally implemented for solving the deterministic version of the problem (i.e. the classical version of the problem without the uncertainty), previously reported in the literature. We modify the algorithm to incorporate a robust optimization methodology, so that the uncertainty on traveling costs can be handled.\",\"PeriodicalId\":444765,\"journal\":{\"name\":\"2013 IEEE Symposium on Swarm Intelligence (SIS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-04-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE Symposium on Swarm Intelligence (SIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIS.2013.6615156\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Symposium on Swarm Intelligence (SIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIS.2013.6615156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An ant colony system for the capacitated vehicle routing problem with uncertain travel costs
In this study, we consider a capacitated vehicle routing problem where the objective function is to minimize the total travel cost.We also consider that the travel costs between the locations are subject to uncertainty, therefore they are expressed as intervals, rather than fixed numbers. The motivation of this study is to solve this problem by using a metaheuristic approach. We base our approach on a variant of ant colony optimization metaheuristic, called ant colony system, which was originally implemented for solving the deterministic version of the problem (i.e. the classical version of the problem without the uncertainty), previously reported in the literature. We modify the algorithm to incorporate a robust optimization methodology, so that the uncertainty on traveling costs can be handled.