{"title":"基于仿真的遗传算法求解多属性组合调度决策问题","authors":"Y. Kuo, Yu Tie, Taho Yang","doi":"10.1109/ICMECH.2005.1529310","DOIUrl":null,"url":null,"abstract":"This paper presented a simulation-based genetic algorithms (GAs) approach in solving a multi-attribute combinatorial dispatching (MACD) decision problem in a flow shop with multiple processors (FSMP) environment. The simulation is capable of modeling non-linear and stochastic problem. GA is a proven tool in solving a complex optimization problem. The proposed GAs simulation approach addressed a complex MACD problem by solving a case study from multi-player ceramic capacitor (MLCC) manufacturing. Empirical results illustrated both the effectiveness and efficiency of the proposed methodology in solving the MACD problem. Managerial insights are drawn form the case study results and future research direction is discussed.","PeriodicalId":175701,"journal":{"name":"IEEE International Conference on Mechatronics, 2005. ICM '05.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A simulation-based genetic algorithms approach in solving a multi-attribute combinatorial dispatching decision problem\",\"authors\":\"Y. Kuo, Yu Tie, Taho Yang\",\"doi\":\"10.1109/ICMECH.2005.1529310\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presented a simulation-based genetic algorithms (GAs) approach in solving a multi-attribute combinatorial dispatching (MACD) decision problem in a flow shop with multiple processors (FSMP) environment. The simulation is capable of modeling non-linear and stochastic problem. GA is a proven tool in solving a complex optimization problem. The proposed GAs simulation approach addressed a complex MACD problem by solving a case study from multi-player ceramic capacitor (MLCC) manufacturing. Empirical results illustrated both the effectiveness and efficiency of the proposed methodology in solving the MACD problem. Managerial insights are drawn form the case study results and future research direction is discussed.\",\"PeriodicalId\":175701,\"journal\":{\"name\":\"IEEE International Conference on Mechatronics, 2005. ICM '05.\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Conference on Mechatronics, 2005. ICM '05.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMECH.2005.1529310\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Conference on Mechatronics, 2005. ICM '05.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMECH.2005.1529310","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A simulation-based genetic algorithms approach in solving a multi-attribute combinatorial dispatching decision problem
This paper presented a simulation-based genetic algorithms (GAs) approach in solving a multi-attribute combinatorial dispatching (MACD) decision problem in a flow shop with multiple processors (FSMP) environment. The simulation is capable of modeling non-linear and stochastic problem. GA is a proven tool in solving a complex optimization problem. The proposed GAs simulation approach addressed a complex MACD problem by solving a case study from multi-player ceramic capacitor (MLCC) manufacturing. Empirical results illustrated both the effectiveness and efficiency of the proposed methodology in solving the MACD problem. Managerial insights are drawn form the case study results and future research direction is discussed.