{"title":"基于遗传算法的预制件生产调度","authors":"W. Chan, H. Hu","doi":"10.1109/CEC.2000.870768","DOIUrl":null,"url":null,"abstract":"A flow shop sequencing model (FSSM) that incorporates actual constraints encountered in practice is proposed for the difficult case of specialized precast production scheduling. The model is solved using a genetic algorithm (GA). The traditional minimize makespan and the more practical minimize tardiness penalty objective functions are optimized separately, as well as simultaneously using a weighted approach. Experiments are conducted to investigate the effect of increasing population size and seeding the initial population with heuristic solutions. Comparisons between the GA and classical heuristic rules show that the GA is competitive, if not better than heuristic rules in discovering a set of good solutions.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Precast production scheduling with genetic algorithms\",\"authors\":\"W. Chan, H. Hu\",\"doi\":\"10.1109/CEC.2000.870768\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A flow shop sequencing model (FSSM) that incorporates actual constraints encountered in practice is proposed for the difficult case of specialized precast production scheduling. The model is solved using a genetic algorithm (GA). The traditional minimize makespan and the more practical minimize tardiness penalty objective functions are optimized separately, as well as simultaneously using a weighted approach. Experiments are conducted to investigate the effect of increasing population size and seeding the initial population with heuristic solutions. Comparisons between the GA and classical heuristic rules show that the GA is competitive, if not better than heuristic rules in discovering a set of good solutions.\",\"PeriodicalId\":218136,\"journal\":{\"name\":\"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2000.870768\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2000.870768","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Precast production scheduling with genetic algorithms
A flow shop sequencing model (FSSM) that incorporates actual constraints encountered in practice is proposed for the difficult case of specialized precast production scheduling. The model is solved using a genetic algorithm (GA). The traditional minimize makespan and the more practical minimize tardiness penalty objective functions are optimized separately, as well as simultaneously using a weighted approach. Experiments are conducted to investigate the effect of increasing population size and seeding the initial population with heuristic solutions. Comparisons between the GA and classical heuristic rules show that the GA is competitive, if not better than heuristic rules in discovering a set of good solutions.