{"title":"基于起始时间的粒子群优化生产调度方法","authors":"J. Grobler, A. Engelbrecht, J. Joubert, S. Kok","doi":"10.1109/SCIS.2007.367679","DOIUrl":null,"url":null,"abstract":"This paper provides a generic formulation for the complex scheduling problems of Optimatix, a South African company specializing in supply chain optimization. To address the complex requirements of the proposed problem, various additional constraints were added to the classical job shop scheduling problem. These include production downtime, scheduled maintenance, machine breakdowns, sequence-dependent set-up times, release dates and multiple predecessors per job. Differentiation between primary resources (machines) and auxiliary resources (labour, tools and jigs) were also achieved. Furthermore, this paper applies particle swarm optimization (PSO), a stochastic population based optimization technique originating from the study of social behavior of birds and fish, to the proposed problem. Apart from the significance of the paper in that the proposed problem has not been addressed before, the benefit of an improved production schedule can be generalized to include cost reduction, customer satisfaction, improved profitability and overall competitive advantage","PeriodicalId":184726,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Scheduling","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A starting-time-based approach to production scheduling with Particle Swarm Optimization\",\"authors\":\"J. Grobler, A. Engelbrecht, J. Joubert, S. Kok\",\"doi\":\"10.1109/SCIS.2007.367679\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper provides a generic formulation for the complex scheduling problems of Optimatix, a South African company specializing in supply chain optimization. To address the complex requirements of the proposed problem, various additional constraints were added to the classical job shop scheduling problem. These include production downtime, scheduled maintenance, machine breakdowns, sequence-dependent set-up times, release dates and multiple predecessors per job. Differentiation between primary resources (machines) and auxiliary resources (labour, tools and jigs) were also achieved. Furthermore, this paper applies particle swarm optimization (PSO), a stochastic population based optimization technique originating from the study of social behavior of birds and fish, to the proposed problem. Apart from the significance of the paper in that the proposed problem has not been addressed before, the benefit of an improved production schedule can be generalized to include cost reduction, customer satisfaction, improved profitability and overall competitive advantage\",\"PeriodicalId\":184726,\"journal\":{\"name\":\"2007 IEEE Symposium on Computational Intelligence in Scheduling\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE Symposium on Computational Intelligence in Scheduling\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCIS.2007.367679\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Symposium on Computational Intelligence in Scheduling","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCIS.2007.367679","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A starting-time-based approach to production scheduling with Particle Swarm Optimization
This paper provides a generic formulation for the complex scheduling problems of Optimatix, a South African company specializing in supply chain optimization. To address the complex requirements of the proposed problem, various additional constraints were added to the classical job shop scheduling problem. These include production downtime, scheduled maintenance, machine breakdowns, sequence-dependent set-up times, release dates and multiple predecessors per job. Differentiation between primary resources (machines) and auxiliary resources (labour, tools and jigs) were also achieved. Furthermore, this paper applies particle swarm optimization (PSO), a stochastic population based optimization technique originating from the study of social behavior of birds and fish, to the proposed problem. Apart from the significance of the paper in that the proposed problem has not been addressed before, the benefit of an improved production schedule can be generalized to include cost reduction, customer satisfaction, improved profitability and overall competitive advantage