{"title":"Modified Coronavirus Herd Immunity optimization with an ACSAAD Algorithm for Capacitated Vehicle Routing Problems Vehicle Routing Problems","authors":"Yuqing Gao, Ruey-Maw Chen","doi":"10.1109/is3c57901.2023.00047","DOIUrl":null,"url":null,"abstract":"The capacitated vehicle routing problems (CVRPs) are well-known as NP-Hard, which aims to find the optimal route planning with the least cost without violating the constraints. A modified coronavirus herd immunity optimization with an associative customers savings algorithm, named MCASA, is designed to solve CVRPs. First, the individual solution update is modified to make the exploration more flexible. Second, a new saving algorithm, named ACSAAD, is suggested to adjust the customer visit order. Finally, a population state update mechanism is designed to prevent the CHIO from entering the exploitation stage quickly. Three different scale instances on the CVRPs dataset of CVPLIB were tested. The simulation results show that the MCASA can find the optimal solution for the tested instances, with ARPD no more than 0.2, indicating that the MCASA can effectively and efficiently solve CVRPs.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/is3c57901.2023.00047","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The capacitated vehicle routing problems (CVRPs) are well-known as NP-Hard, which aims to find the optimal route planning with the least cost without violating the constraints. A modified coronavirus herd immunity optimization with an associative customers savings algorithm, named MCASA, is designed to solve CVRPs. First, the individual solution update is modified to make the exploration more flexible. Second, a new saving algorithm, named ACSAAD, is suggested to adjust the customer visit order. Finally, a population state update mechanism is designed to prevent the CHIO from entering the exploitation stage quickly. Three different scale instances on the CVRPs dataset of CVPLIB were tested. The simulation results show that the MCASA can find the optimal solution for the tested instances, with ARPD no more than 0.2, indicating that the MCASA can effectively and efficiently solve CVRPs.
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