{"title":"A Joint Optimization Model of s , S Inventory and Supply Strategy Using an Improved PSO-Based Algorithm","authors":"Huayang Deng, Q. Shi, Yadong Wang","doi":"10.1155/2021/7621692","DOIUrl":null,"url":null,"abstract":"This paper mainly discussed the problem of a multiechelon and multiperiod joint policy of inventory and supply network. According to the random lead time and customers’ inventory demand, the \n \n \n \n s\n ,\n S\n \n \n \n policy was improved. Based on the multiechelon supply network and the improved, the dynasty joint model was built. The supply scheme in every period with the objective of minimum total costs is obtained. Considering the complexity of the model, the improved particle swarm optimization algorithm combining the adaptive inertia weight and grading penalty function is adopted to calculate this model and optimize the spare part problems in various environments.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"1 1","pages":"7621692:1-7621692:17"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wirel. Commun. Mob. Comput.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2021/7621692","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper mainly discussed the problem of a multiechelon and multiperiod joint policy of inventory and supply network. According to the random lead time and customers’ inventory demand, the
s
,
S
policy was improved. Based on the multiechelon supply network and the improved, the dynasty joint model was built. The supply scheme in every period with the objective of minimum total costs is obtained. Considering the complexity of the model, the improved particle swarm optimization algorithm combining the adaptive inertia weight and grading penalty function is adopted to calculate this model and optimize the spare part problems in various environments.