{"title":"具有模糊需求的集成供应链模型及其算法","authors":"Yu Ying, Zhang Wei","doi":"10.1109/SYSTEMS.2008.4519039","DOIUrl":null,"url":null,"abstract":"An integrated supply chain model with fuzzy demand is built in this paper. The model is converted into a bilevel programming, in which the upper level programming is an uncertain programming with fuzzy demand, and the lower level programming is a certain programming with the specified parameters passed from the upper level. A genetic algorithm combined with fuzzy simulation technology is proposed to find the optimal decisions in the upper level programming. In the lower level, under the given decision from the upper level, a simulated annealing algorithm is provided to obtain the optimal values which are then sent back to the upper level. Through the evolutionary processes such as crossover and mutation operations, the optimal solutions to achieve the minimum system cost can be found. Lastly numerical examples are given to show the validity of the algorithm.","PeriodicalId":403208,"journal":{"name":"2008 2nd Annual IEEE Systems Conference","volume":"83 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An integrated supply chain model with fuzzy demand and its algorithm\",\"authors\":\"Yu Ying, Zhang Wei\",\"doi\":\"10.1109/SYSTEMS.2008.4519039\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An integrated supply chain model with fuzzy demand is built in this paper. The model is converted into a bilevel programming, in which the upper level programming is an uncertain programming with fuzzy demand, and the lower level programming is a certain programming with the specified parameters passed from the upper level. A genetic algorithm combined with fuzzy simulation technology is proposed to find the optimal decisions in the upper level programming. In the lower level, under the given decision from the upper level, a simulated annealing algorithm is provided to obtain the optimal values which are then sent back to the upper level. Through the evolutionary processes such as crossover and mutation operations, the optimal solutions to achieve the minimum system cost can be found. Lastly numerical examples are given to show the validity of the algorithm.\",\"PeriodicalId\":403208,\"journal\":{\"name\":\"2008 2nd Annual IEEE Systems Conference\",\"volume\":\"83 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 2nd Annual IEEE Systems Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SYSTEMS.2008.4519039\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 2nd Annual IEEE Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYSTEMS.2008.4519039","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An integrated supply chain model with fuzzy demand and its algorithm
An integrated supply chain model with fuzzy demand is built in this paper. The model is converted into a bilevel programming, in which the upper level programming is an uncertain programming with fuzzy demand, and the lower level programming is a certain programming with the specified parameters passed from the upper level. A genetic algorithm combined with fuzzy simulation technology is proposed to find the optimal decisions in the upper level programming. In the lower level, under the given decision from the upper level, a simulated annealing algorithm is provided to obtain the optimal values which are then sent back to the upper level. Through the evolutionary processes such as crossover and mutation operations, the optimal solutions to achieve the minimum system cost can be found. Lastly numerical examples are given to show the validity of the algorithm.