E. Ásgeirsson, Guðrún Sjöfn Axelsdóttir, H. Stefánsson
{"title":"使制药公司的手工生产调度过程自动化","authors":"E. Ásgeirsson, Guðrún Sjöfn Axelsdóttir, H. Stefánsson","doi":"10.1109/CIPLS.2011.5953358","DOIUrl":null,"url":null,"abstract":"We look at the automation of a manual production scheduling process at a pharmaceutical company, by using mixed integer optimization and a simple greedy algorithm. The pharmaceutical company is a make to order producer with highly utilized resources and flexible production processes. We present the algorithms and analyze their performance using real data and compare the results with the manual approach currently used at the company. The results indicate that automated scheduling approaches can be used to improve the production scheduling process, while at the same time reducing the valuable time the human schedulers spend on preparing the production schedules.","PeriodicalId":103768,"journal":{"name":"2011 IEEE Workshop On Computational Intelligence In Production And Logistics Systems (CIPLS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Automating a manual production scheduling process at a pharmaceutical company\",\"authors\":\"E. Ásgeirsson, Guðrún Sjöfn Axelsdóttir, H. Stefánsson\",\"doi\":\"10.1109/CIPLS.2011.5953358\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We look at the automation of a manual production scheduling process at a pharmaceutical company, by using mixed integer optimization and a simple greedy algorithm. The pharmaceutical company is a make to order producer with highly utilized resources and flexible production processes. We present the algorithms and analyze their performance using real data and compare the results with the manual approach currently used at the company. The results indicate that automated scheduling approaches can be used to improve the production scheduling process, while at the same time reducing the valuable time the human schedulers spend on preparing the production schedules.\",\"PeriodicalId\":103768,\"journal\":{\"name\":\"2011 IEEE Workshop On Computational Intelligence In Production And Logistics Systems (CIPLS)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE Workshop On Computational Intelligence In Production And Logistics Systems (CIPLS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIPLS.2011.5953358\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE Workshop On Computational Intelligence In Production And Logistics Systems (CIPLS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIPLS.2011.5953358","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automating a manual production scheduling process at a pharmaceutical company
We look at the automation of a manual production scheduling process at a pharmaceutical company, by using mixed integer optimization and a simple greedy algorithm. The pharmaceutical company is a make to order producer with highly utilized resources and flexible production processes. We present the algorithms and analyze their performance using real data and compare the results with the manual approach currently used at the company. The results indicate that automated scheduling approaches can be used to improve the production scheduling process, while at the same time reducing the valuable time the human schedulers spend on preparing the production schedules.