Geraldo Browne Ribeiro Filho, M. S. Nagano, L. A. N. Lorena
{"title":"混合元启发式遗传算法聚类搜索在流动车间排列生产系统中的优化","authors":"Geraldo Browne Ribeiro Filho, M. S. Nagano, L. A. N. Lorena","doi":"10.21528/LNLM-vol4-no1-art4","DOIUrl":null,"url":null,"abstract":"This paper deals with the Permutation Flow Shop scheduling problem with the objective of minimizing total flow time, therefore reducing in-process inventory. A new hybrid metaheuristic, Genetic Algorithm Cluster Search, is proposed for the scheduling problem solution. The proposed method is compared with the bests results reported in the literature. Experimental results show that the new method provides better solutions regarding the solution quality.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Metaheurística Híbrida Algoritmo Genético-Clustering Search Para A Otimização Em Sistemas De Produção Flow Shop Permutacional\",\"authors\":\"Geraldo Browne Ribeiro Filho, M. S. Nagano, L. A. N. Lorena\",\"doi\":\"10.21528/LNLM-vol4-no1-art4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper deals with the Permutation Flow Shop scheduling problem with the objective of minimizing total flow time, therefore reducing in-process inventory. A new hybrid metaheuristic, Genetic Algorithm Cluster Search, is proposed for the scheduling problem solution. The proposed method is compared with the bests results reported in the literature. Experimental results show that the new method provides better solutions regarding the solution quality.\",\"PeriodicalId\":386768,\"journal\":{\"name\":\"Learning and Nonlinear Models\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Learning and Nonlinear Models\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21528/LNLM-vol4-no1-art4\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Learning and Nonlinear Models","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21528/LNLM-vol4-no1-art4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Metaheurística Híbrida Algoritmo Genético-Clustering Search Para A Otimização Em Sistemas De Produção Flow Shop Permutacional
This paper deals with the Permutation Flow Shop scheduling problem with the objective of minimizing total flow time, therefore reducing in-process inventory. A new hybrid metaheuristic, Genetic Algorithm Cluster Search, is proposed for the scheduling problem solution. The proposed method is compared with the bests results reported in the literature. Experimental results show that the new method provides better solutions regarding the solution quality.