J.M. Garcia, S. Lozano, K. Smith, T. Kwok, G. Villa
{"title":"Coordinated scheduling of production and delivery from multiple plants and with time windows using genetic algorithms","authors":"J.M. Garcia, S. Lozano, K. Smith, T. Kwok, G. Villa","doi":"10.1109/ICONIP.2002.1202802","DOIUrl":null,"url":null,"abstract":"This paper deals with the problem of selecting and scheduling a set of orders to be manufactured and immediately delivered to the customer site. We provide m plants for production and V vehicles for distribution. Furthermore, another constraints to be considered are the limited production capacity at plants and time windows within which orders must be served. A genetic algorithm to solve the problem is developed and tested empirically with randomly generated problems. In order to benchmark the GA, a graph-based exact method is proposed. However, such exact method is not efficient and, therefore, can only be used for small problems. Results attest that our GA produces good-quality solutions.","PeriodicalId":146553,"journal":{"name":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICONIP.2002.1202802","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 28
Abstract
This paper deals with the problem of selecting and scheduling a set of orders to be manufactured and immediately delivered to the customer site. We provide m plants for production and V vehicles for distribution. Furthermore, another constraints to be considered are the limited production capacity at plants and time windows within which orders must be served. A genetic algorithm to solve the problem is developed and tested empirically with randomly generated problems. In order to benchmark the GA, a graph-based exact method is proposed. However, such exact method is not efficient and, therefore, can only be used for small problems. Results attest that our GA produces good-quality solutions.