{"title":"Team scheduling by genetic search","authors":"Tiehua Zhang, William A. GruveP, Michael H. Smith","doi":"10.1109/IPMM.1999.791495","DOIUrl":null,"url":null,"abstract":"We consider a photographic studio that must schedule multiple teams of photographers to a large number of elementary and secondary schools. The photographers' schedules are to be optimized so that time constraints are satisfied and each team is able to at least visit two schools daily. A multiple travelling salesman model is used where the total distance traveled and time consumed can be evaluated in a single cost function to achieve overall optimality. A genetic algorithm has been applied to solve the problem. The results show that this approach rapidly provides an effective means for solving the problem.","PeriodicalId":194215,"journal":{"name":"Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM'99 (Cat. No.99EX296)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"53","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM'99 (Cat. No.99EX296)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPMM.1999.791495","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 53
Abstract
We consider a photographic studio that must schedule multiple teams of photographers to a large number of elementary and secondary schools. The photographers' schedules are to be optimized so that time constraints are satisfied and each team is able to at least visit two schools daily. A multiple travelling salesman model is used where the total distance traveled and time consumed can be evaluated in a single cost function to achieve overall optimality. A genetic algorithm has been applied to solve the problem. The results show that this approach rapidly provides an effective means for solving the problem.