{"title":"基于混合遗传算法的智能流水车间生产计划与调度优化","authors":"P. Semanco, V. Modrák","doi":"10.1109/INES.2011.5954777","DOIUrl":null,"url":null,"abstract":"The paper introduces a proposal of three-metaheuristic versions to optimize flow-shop problem emphasized on total flow time criterion in Intelligent Manufacturing Systems. The approach employs constructive heuristic, namely CDS, Gupta's algorithm, and Palmer's Slope Index, in conjunction with GA-based metaheuristic. The approach is tested on Reeves' benchmark set of 21 flow-shop problems range from 20 to 75 jobs and 5 to 20 machines.","PeriodicalId":414812,"journal":{"name":"2011 15th IEEE International Conference on Intelligent Engineering Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Hybrid GA-based metaheuristics for production planning and scheduling optimization in intelligent flow-shop manufacturing systems\",\"authors\":\"P. Semanco, V. Modrák\",\"doi\":\"10.1109/INES.2011.5954777\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper introduces a proposal of three-metaheuristic versions to optimize flow-shop problem emphasized on total flow time criterion in Intelligent Manufacturing Systems. The approach employs constructive heuristic, namely CDS, Gupta's algorithm, and Palmer's Slope Index, in conjunction with GA-based metaheuristic. The approach is tested on Reeves' benchmark set of 21 flow-shop problems range from 20 to 75 jobs and 5 to 20 machines.\",\"PeriodicalId\":414812,\"journal\":{\"name\":\"2011 15th IEEE International Conference on Intelligent Engineering Systems\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 15th IEEE International Conference on Intelligent Engineering Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INES.2011.5954777\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 15th IEEE International Conference on Intelligent Engineering Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INES.2011.5954777","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hybrid GA-based metaheuristics for production planning and scheduling optimization in intelligent flow-shop manufacturing systems
The paper introduces a proposal of three-metaheuristic versions to optimize flow-shop problem emphasized on total flow time criterion in Intelligent Manufacturing Systems. The approach employs constructive heuristic, namely CDS, Gupta's algorithm, and Palmer's Slope Index, in conjunction with GA-based metaheuristic. The approach is tested on Reeves' benchmark set of 21 flow-shop problems range from 20 to 75 jobs and 5 to 20 machines.
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