{"title":"开放式车间调度问题的遗传算法","authors":"Yacine Benziani, I. Kacem, Pierre Laroche","doi":"10.1109/CoDIT.2018.8394932","DOIUrl":null,"url":null,"abstract":"In this paper, we present a genetic algorithm for the open shop scheduling problem. We use a simple and efficient chromosome representation based on the job's occurrence and the fitness function reflect the length of the schedule. The solutions obtained after performing the different operators of the genetic algorithm are always feasible. Heuristic approaches are also developed to generate the initial population and to improve the obtained solutions. The algorithm was implemented and computational results show interesting result.","PeriodicalId":128011,"journal":{"name":"2018 5th International Conference on Control, Decision and Information Technologies (CoDIT)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Genetic Algorithm for Open Shop Scheduling Problem\",\"authors\":\"Yacine Benziani, I. Kacem, Pierre Laroche\",\"doi\":\"10.1109/CoDIT.2018.8394932\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a genetic algorithm for the open shop scheduling problem. We use a simple and efficient chromosome representation based on the job's occurrence and the fitness function reflect the length of the schedule. The solutions obtained after performing the different operators of the genetic algorithm are always feasible. Heuristic approaches are also developed to generate the initial population and to improve the obtained solutions. The algorithm was implemented and computational results show interesting result.\",\"PeriodicalId\":128011,\"journal\":{\"name\":\"2018 5th International Conference on Control, Decision and Information Technologies (CoDIT)\",\"volume\":\"112 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 5th International Conference on Control, Decision and Information Technologies (CoDIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CoDIT.2018.8394932\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 5th International Conference on Control, Decision and Information Technologies (CoDIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CoDIT.2018.8394932","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Genetic Algorithm for Open Shop Scheduling Problem
In this paper, we present a genetic algorithm for the open shop scheduling problem. We use a simple and efficient chromosome representation based on the job's occurrence and the fitness function reflect the length of the schedule. The solutions obtained after performing the different operators of the genetic algorithm are always feasible. Heuristic approaches are also developed to generate the initial population and to improve the obtained solutions. The algorithm was implemented and computational results show interesting result.