{"title":"Multi objective fuzzy inventory model with demand dependent unit cost and lead time constraints – A Karush Kuhn Tucker conditions approach","authors":"S. Rànganayaki, C. Seshaiah","doi":"10.12988/IJMA.2014.310252","DOIUrl":null,"url":null,"abstract":"In this paper a multi-objective inventory model with demand dependent unit cost and leading time has been formulated with number of orders and production cost as constraints . In most of the real world situations the cost parameters, the objective function and constraints of the decision makers are imprecise in nature. A demand dependent unit cost is assumed and solved using Karush Kuhn Tucker conditions. Here the unit production cost is considered under fuzzy environment. The model has been solved with demand, lot size and leading time as decision variables.","PeriodicalId":431531,"journal":{"name":"International Journal of Mathematical Analysis","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Mathematical Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12988/IJMA.2014.310252","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper a multi-objective inventory model with demand dependent unit cost and leading time has been formulated with number of orders and production cost as constraints . In most of the real world situations the cost parameters, the objective function and constraints of the decision makers are imprecise in nature. A demand dependent unit cost is assumed and solved using Karush Kuhn Tucker conditions. Here the unit production cost is considered under fuzzy environment. The model has been solved with demand, lot size and leading time as decision variables.