{"title":"具有不同到期日和放行时间的单机调度的混合遗传方法","authors":"Jae-Gyun Kim","doi":"10.1109/KORUS.1999.875916","DOIUrl":null,"url":null,"abstract":"The article addresses the n-job, non-preemptive and single machine scheduling problem of minimizing the sum of earliness and tardiness with different release times and due dates. To solve the problem, it proposes a hybrid genetic algorithm with a new crossover and mutation operators to adjust the job sequencing. To investigate the suitability of the parameters set and the quality of the solution, the article evaluates the number of corresponding solutions and the speed of converging to an optimal solution which is solved by an enumeration method for small size problems. To demonstrate the performance of the proposed GA, it is empirically evaluated by solving a large number of problems and compared with solutions obtained by genetic algorithms using the existing operators.","PeriodicalId":250552,"journal":{"name":"Proceedings Third Russian-Korean International Symposium on Science and Technology. KORUS'99 (Cat. No.99EX362)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A hybrid genetic approach for single machine scheduling with distinct due dates and release times\",\"authors\":\"Jae-Gyun Kim\",\"doi\":\"10.1109/KORUS.1999.875916\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article addresses the n-job, non-preemptive and single machine scheduling problem of minimizing the sum of earliness and tardiness with different release times and due dates. To solve the problem, it proposes a hybrid genetic algorithm with a new crossover and mutation operators to adjust the job sequencing. To investigate the suitability of the parameters set and the quality of the solution, the article evaluates the number of corresponding solutions and the speed of converging to an optimal solution which is solved by an enumeration method for small size problems. To demonstrate the performance of the proposed GA, it is empirically evaluated by solving a large number of problems and compared with solutions obtained by genetic algorithms using the existing operators.\",\"PeriodicalId\":250552,\"journal\":{\"name\":\"Proceedings Third Russian-Korean International Symposium on Science and Technology. KORUS'99 (Cat. No.99EX362)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Third Russian-Korean International Symposium on Science and Technology. KORUS'99 (Cat. No.99EX362)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/KORUS.1999.875916\",\"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 Third Russian-Korean International Symposium on Science and Technology. KORUS'99 (Cat. No.99EX362)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KORUS.1999.875916","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A hybrid genetic approach for single machine scheduling with distinct due dates and release times
The article addresses the n-job, non-preemptive and single machine scheduling problem of minimizing the sum of earliness and tardiness with different release times and due dates. To solve the problem, it proposes a hybrid genetic algorithm with a new crossover and mutation operators to adjust the job sequencing. To investigate the suitability of the parameters set and the quality of the solution, the article evaluates the number of corresponding solutions and the speed of converging to an optimal solution which is solved by an enumeration method for small size problems. To demonstrate the performance of the proposed GA, it is empirically evaluated by solving a large number of problems and compared with solutions obtained by genetic algorithms using the existing operators.