{"title":"Improving Multi-agent Evolutionary Techniques with Local Search for Job Shop Scheduling Problem","authors":"Ahmad Balid, S. Minz","doi":"10.1109/WIIAT.2008.191","DOIUrl":null,"url":null,"abstract":"Scheduling is the allocation of shared resources over time in order to perform a number of tasks. Job Shop Scheduling Problem (JSSP) is the most commonly encountered scheduling problem. A wide range of approaches have been proposed to solve it. In this paper two multi-agent based evolutionary models are proposed to tackle JSSP. The first one is Multi-Agent based Genetic Algorithm (MAGA) and the second model is a Multi-Agent Particle Swarm Optimization (MAPSO). A proposed local search technique as self-learning procedure for agents is hybridized with both of the multi-agent models to enhance their efficiency. The proposed models have been implemented using REPAST toolkit. Encouraging results from both models have been obtained for standard benchmarks from OR library.","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WIIAT.2008.191","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Scheduling is the allocation of shared resources over time in order to perform a number of tasks. Job Shop Scheduling Problem (JSSP) is the most commonly encountered scheduling problem. A wide range of approaches have been proposed to solve it. In this paper two multi-agent based evolutionary models are proposed to tackle JSSP. The first one is Multi-Agent based Genetic Algorithm (MAGA) and the second model is a Multi-Agent Particle Swarm Optimization (MAPSO). A proposed local search technique as self-learning procedure for agents is hybridized with both of the multi-agent models to enhance their efficiency. The proposed models have been implemented using REPAST toolkit. Encouraging results from both models have been obtained for standard benchmarks from OR library.