{"title":"无等待流车间的双目标案例","authors":"M. Jenabi, B. Naderi, S. Ghomi","doi":"10.1109/BICTA.2010.5645110","DOIUrl":null,"url":null,"abstract":"This paper studies multi-objective no-wait flows hop scheduling problems to minimize both makespan and total tardiness. This paper mathematically formulates it as two effective multi-objective mixed integer linear programming models. The multi-objective models are then solved using a multiple criteria decision making approach. Moreover, this paper proposes a novel multi-objective iterated local search algorithm incorporating with three types of local search engine, greedy and moderate and curtailed fashions. The algorithm is carefully evaluated for its performance against some available algorithms by means of multi-objective performance measures and statistical tools. The results show that the proposed solution method outperforms the others.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"87 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A bi-objective case of no-wait flowshops\",\"authors\":\"M. Jenabi, B. Naderi, S. Ghomi\",\"doi\":\"10.1109/BICTA.2010.5645110\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper studies multi-objective no-wait flows hop scheduling problems to minimize both makespan and total tardiness. This paper mathematically formulates it as two effective multi-objective mixed integer linear programming models. The multi-objective models are then solved using a multiple criteria decision making approach. Moreover, this paper proposes a novel multi-objective iterated local search algorithm incorporating with three types of local search engine, greedy and moderate and curtailed fashions. The algorithm is carefully evaluated for its performance against some available algorithms by means of multi-objective performance measures and statistical tools. The results show that the proposed solution method outperforms the others.\",\"PeriodicalId\":302619,\"journal\":{\"name\":\"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)\",\"volume\":\"87 6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BICTA.2010.5645110\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BICTA.2010.5645110","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper studies multi-objective no-wait flows hop scheduling problems to minimize both makespan and total tardiness. This paper mathematically formulates it as two effective multi-objective mixed integer linear programming models. The multi-objective models are then solved using a multiple criteria decision making approach. Moreover, this paper proposes a novel multi-objective iterated local search algorithm incorporating with three types of local search engine, greedy and moderate and curtailed fashions. The algorithm is carefully evaluated for its performance against some available algorithms by means of multi-objective performance measures and statistical tools. The results show that the proposed solution method outperforms the others.