{"title":"A new method coupling simulation and a hybrid metaheuristic to solve a multiobjective hybrid flowshop scheduling problem","authors":"Xiaohui Li, H. Chehade, F. Yalaoui, L. Amodeo","doi":"10.2991/eusflat.2011.33","DOIUrl":null,"url":null,"abstract":"In this paper, we have studied a multiobjective hybrid flowshop scheduling problem where n independent jobs should be executed in a hybrid assembly line. The aim of our work is to optimize the makespan and the total tardiness of the whole production. A simulation based optimization algorithm is proposed here to solve this problem. It is a combination of the simulation software ARENA and the FLC-NSGA-II optimization method. The latter uses a fuzzy logic controller to adjust the crossover and the mutation probabilities, in order to enhance the research ability of the traditional NSGA-II algorithm. This method is first compared with the industrial solutions, and then with NSGA-II. The result shows the eciency and the feasibility of our proposed method.","PeriodicalId":403191,"journal":{"name":"EUSFLAT Conf.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EUSFLAT Conf.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2991/eusflat.2011.33","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In this paper, we have studied a multiobjective hybrid flowshop scheduling problem where n independent jobs should be executed in a hybrid assembly line. The aim of our work is to optimize the makespan and the total tardiness of the whole production. A simulation based optimization algorithm is proposed here to solve this problem. It is a combination of the simulation software ARENA and the FLC-NSGA-II optimization method. The latter uses a fuzzy logic controller to adjust the crossover and the mutation probabilities, in order to enhance the research ability of the traditional NSGA-II algorithm. This method is first compared with the industrial solutions, and then with NSGA-II. The result shows the eciency and the feasibility of our proposed method.