Hai-yan Wang , Yan-wei Zhao , Xin-li Xu , Wan-liang Wang
{"title":"Scheduling Batch and Continuous Process Production based on an Improved Differential Evolution Algorithm","authors":"Hai-yan Wang , Yan-wei Zhao , Xin-li Xu , Wan-liang Wang","doi":"10.1016/S1874-8651(10)60086-5","DOIUrl":null,"url":null,"abstract":"<div><p>In order to solve the scheduling problems of mixed batch and continuous processes, continuous time was discretized, and an improved differential evolution algorithm was developed. A new chromosome representation was proposed, considering capacity constraints. Also, a new crossover method and a new mutation method were proposed based on the new chromosome representation. The value of the crossover probability <em>CR</em> was obtained by using the logistic chaotic map method, and the selection operator was improved to promote the global search ability. The results of the simulation indicate that the model and the method are feasible.</p></div>","PeriodicalId":101206,"journal":{"name":"Systems Engineering - Theory & Practice","volume":"29 11","pages":"Pages 157-167"},"PeriodicalIF":0.0000,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S1874-8651(10)60086-5","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Engineering - Theory & Practice","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1874865110600865","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
In order to solve the scheduling problems of mixed batch and continuous processes, continuous time was discretized, and an improved differential evolution algorithm was developed. A new chromosome representation was proposed, considering capacity constraints. Also, a new crossover method and a new mutation method were proposed based on the new chromosome representation. The value of the crossover probability CR was obtained by using the logistic chaotic map method, and the selection operator was improved to promote the global search ability. The results of the simulation indicate that the model and the method are feasible.