Imad Belassiria, M. Mazouzi, Said El Fezazi, Z. El Maskaoui
{"title":"An efficient approach for workload balancing of assembly line systems with assignment restrictions","authors":"Imad Belassiria, M. Mazouzi, Said El Fezazi, Z. El Maskaoui","doi":"10.1109/LOGISTIQUA.2017.7962865","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a hybrid genetic algorithm to solve assembly line balancing problem type E. There are two objectives to be achieved: Maximizing line efficiency balancing the workstation simultaneously. The model provide more realistic situation of assembly line balancing problem with station restriction and zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively, so we aim to provide its exploring capability by sequentially hybridizing the well known assignment rules heuristics with genetic algorithm.","PeriodicalId":310750,"journal":{"name":"2017 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA)","volume":"520 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LOGISTIQUA.2017.7962865","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, we propose a hybrid genetic algorithm to solve assembly line balancing problem type E. There are two objectives to be achieved: Maximizing line efficiency balancing the workstation simultaneously. The model provide more realistic situation of assembly line balancing problem with station restriction and zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively, so we aim to provide its exploring capability by sequentially hybridizing the well known assignment rules heuristics with genetic algorithm.