{"title":"利用最大加区间线性方程的l -定域解的可接受产品定价问题","authors":"Worrawate Leela-apiradee, P. Thipwiwatpotjana","doi":"10.1109/NAFIPS.2016.7851577","DOIUrl":null,"url":null,"abstract":"Product pricing is one of the most important strategies in doing any business. In this paper, we propose a specific marketing situation as an acceptable product pricing problem when the data on purchasing power and transportation cost cannot be measured exactly but can be shown as intervals of possible values. This problem is formulated as a minimization problem of a differentiable convex objective function with max-plus interval linear constraints A ⊗ x = b where x is called an L-localized solution. Its feasible region is nonconvex but could be viewed by the union of box constraints. The steepest descent algorithm is applied to optimize the objective function with respect to each of these box constraints and obtained an optimal solution from the best value.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Acceptable product pricing problem using L-localized solutions of max-plus interval linear equations\",\"authors\":\"Worrawate Leela-apiradee, P. Thipwiwatpotjana\",\"doi\":\"10.1109/NAFIPS.2016.7851577\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Product pricing is one of the most important strategies in doing any business. In this paper, we propose a specific marketing situation as an acceptable product pricing problem when the data on purchasing power and transportation cost cannot be measured exactly but can be shown as intervals of possible values. This problem is formulated as a minimization problem of a differentiable convex objective function with max-plus interval linear constraints A ⊗ x = b where x is called an L-localized solution. Its feasible region is nonconvex but could be viewed by the union of box constraints. The steepest descent algorithm is applied to optimize the objective function with respect to each of these box constraints and obtained an optimal solution from the best value.\",\"PeriodicalId\":208265,\"journal\":{\"name\":\"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAFIPS.2016.7851577\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAFIPS.2016.7851577","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Acceptable product pricing problem using L-localized solutions of max-plus interval linear equations
Product pricing is one of the most important strategies in doing any business. In this paper, we propose a specific marketing situation as an acceptable product pricing problem when the data on purchasing power and transportation cost cannot be measured exactly but can be shown as intervals of possible values. This problem is formulated as a minimization problem of a differentiable convex objective function with max-plus interval linear constraints A ⊗ x = b where x is called an L-localized solution. Its feasible region is nonconvex but could be viewed by the union of box constraints. The steepest descent algorithm is applied to optimize the objective function with respect to each of these box constraints and obtained an optimal solution from the best value.