{"title":"一类带批处理的两阶段流水车间调度问题的表述与方法","authors":"Runsen Wang, Yilan Shen, Weihao Wang, Leyuan Shi","doi":"10.1109/CASE48305.2020.9216748","DOIUrl":null,"url":null,"abstract":"Motivated by the heat-treating process in a launch vehicles manufacturing plant, we study a two-stage scheduling problem with limited waiting time where the first stage is a batch processor and the second stage is a discrete machine. A mixed-integer programming model is developed and two lower bounds are derived to measure the performance of proposed algorithms. An efficient heuristic together with worst-case analysis is also proposed. Genetic Programming approaches are applied to the flow-shop scheduling problem. Numerical results demonstrate that the proposed algorithms perform better than other meta-heuristics in different production scenarios.","PeriodicalId":212181,"journal":{"name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Formulation and Methods for a Class of Two-stage Flow-shop Scheduling Problem with the Batch Processor\",\"authors\":\"Runsen Wang, Yilan Shen, Weihao Wang, Leyuan Shi\",\"doi\":\"10.1109/CASE48305.2020.9216748\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Motivated by the heat-treating process in a launch vehicles manufacturing plant, we study a two-stage scheduling problem with limited waiting time where the first stage is a batch processor and the second stage is a discrete machine. A mixed-integer programming model is developed and two lower bounds are derived to measure the performance of proposed algorithms. An efficient heuristic together with worst-case analysis is also proposed. Genetic Programming approaches are applied to the flow-shop scheduling problem. Numerical results demonstrate that the proposed algorithms perform better than other meta-heuristics in different production scenarios.\",\"PeriodicalId\":212181,\"journal\":{\"name\":\"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CASE48305.2020.9216748\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CASE48305.2020.9216748","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Formulation and Methods for a Class of Two-stage Flow-shop Scheduling Problem with the Batch Processor
Motivated by the heat-treating process in a launch vehicles manufacturing plant, we study a two-stage scheduling problem with limited waiting time where the first stage is a batch processor and the second stage is a discrete machine. A mixed-integer programming model is developed and two lower bounds are derived to measure the performance of proposed algorithms. An efficient heuristic together with worst-case analysis is also proposed. Genetic Programming approaches are applied to the flow-shop scheduling problem. Numerical results demonstrate that the proposed algorithms perform better than other meta-heuristics in different production scenarios.