{"title":"具有专用机器、作业分割和设置资源的统一并行机器调度","authors":"Hyun-Jung Kim, Jun-Ho Lee","doi":"10.1109/COASE.2018.8560409","DOIUrl":null,"url":null,"abstract":"This paper examines a uniform parallel machine scheduling problem in which jobs can be split arbitrary into multiple sections and such job sections can be processed on a set of dedicated machines simultaneously. Once a job type is changed, a setup performed by an operator is required. The setup time is sequence-independent, and the number of setup operators is limited. Machines conduct the same operation but have different speeds. The objective is to minimize the maximum completion time. This problem is motivated from real-life applications that manufacture automotive pistons in Korea. We propose efficient heuristic algorithms for this problem and show experimentally that the performance of the algorithms is good enough to be used in practice.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"54 1","pages":"661-663"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Uniform Parallel Machine Scheduling with Dedicated Machines, Job Splitting and Setup Resources\",\"authors\":\"Hyun-Jung Kim, Jun-Ho Lee\",\"doi\":\"10.1109/COASE.2018.8560409\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper examines a uniform parallel machine scheduling problem in which jobs can be split arbitrary into multiple sections and such job sections can be processed on a set of dedicated machines simultaneously. Once a job type is changed, a setup performed by an operator is required. The setup time is sequence-independent, and the number of setup operators is limited. Machines conduct the same operation but have different speeds. The objective is to minimize the maximum completion time. This problem is motivated from real-life applications that manufacture automotive pistons in Korea. We propose efficient heuristic algorithms for this problem and show experimentally that the performance of the algorithms is good enough to be used in practice.\",\"PeriodicalId\":6518,\"journal\":{\"name\":\"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)\",\"volume\":\"54 1\",\"pages\":\"661-663\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COASE.2018.8560409\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COASE.2018.8560409","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Uniform Parallel Machine Scheduling with Dedicated Machines, Job Splitting and Setup Resources
This paper examines a uniform parallel machine scheduling problem in which jobs can be split arbitrary into multiple sections and such job sections can be processed on a set of dedicated machines simultaneously. Once a job type is changed, a setup performed by an operator is required. The setup time is sequence-independent, and the number of setup operators is limited. Machines conduct the same operation but have different speeds. The objective is to minimize the maximum completion time. This problem is motivated from real-life applications that manufacture automotive pistons in Korea. We propose efficient heuristic algorithms for this problem and show experimentally that the performance of the algorithms is good enough to be used in practice.