{"title":"Integrating supplier selection decisions into an inventory location problem for designing the supply chain network","authors":"","doi":"10.1007/s10878-023-01100-y","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>This paper proposes a novel Inventory-Location problem that integrates supplier selection decisions to design a three-echelon supply chain network, under a continuous (<em>s</em>,<em>Q</em>) inventory control policy at the warehouses. In this problem, a set of warehouses must be selected within a set of potential locations to serve several customers or demand zones, additionally involving the selection of the suppliers for fulfilling incoming orders from the located warehouses. The optimal solution must be determined while minimizing total system costs including supplier selection, transportation (i.e., suppliers-warehouses and warehouses-customers), inventory (i.e., cycle and safety stock), and warehouse location costs. A key element of the problem is the consideration of variable lead-times for the warehouses, which are dependent on the selection of the supplier that serve them, thus increasing model complexity. Accordingly, an efficient algorithm based on the Generalized Benders Decomposition is developed and implemented to solve the proposed Mixed Integer, Nonlinear, Nonconvex, Programming Model. The proposed solution approach relies on a convenient model formulation and decomposition that yields a Mixed Integer Linear master problem and a continuous, convex subproblem. A wide set of medium-sized synthetic instances are optimally solved in affordable times, denoting the efficiency and effectiveness of the proposed model along with the proposed solution approach. Significant scientific and managerial insights are provided and discussed.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Combinatorial Optimization","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s10878-023-01100-y","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper proposes a novel Inventory-Location problem that integrates supplier selection decisions to design a three-echelon supply chain network, under a continuous (s,Q) inventory control policy at the warehouses. In this problem, a set of warehouses must be selected within a set of potential locations to serve several customers or demand zones, additionally involving the selection of the suppliers for fulfilling incoming orders from the located warehouses. The optimal solution must be determined while minimizing total system costs including supplier selection, transportation (i.e., suppliers-warehouses and warehouses-customers), inventory (i.e., cycle and safety stock), and warehouse location costs. A key element of the problem is the consideration of variable lead-times for the warehouses, which are dependent on the selection of the supplier that serve them, thus increasing model complexity. Accordingly, an efficient algorithm based on the Generalized Benders Decomposition is developed and implemented to solve the proposed Mixed Integer, Nonlinear, Nonconvex, Programming Model. The proposed solution approach relies on a convenient model formulation and decomposition that yields a Mixed Integer Linear master problem and a continuous, convex subproblem. A wide set of medium-sized synthetic instances are optimally solved in affordable times, denoting the efficiency and effectiveness of the proposed model along with the proposed solution approach. Significant scientific and managerial insights are provided and discussed.
期刊介绍：
The objective of Journal of Combinatorial Optimization is to advance and promote the theory and applications of combinatorial optimization, which is an area of research at the intersection of applied mathematics, computer science, and operations research and which overlaps with many other areas such as computation complexity, computational biology, VLSI design, communication networks, and management science. It includes complexity analysis and algorithm design for combinatorial optimization problems, numerical experiments and problem discovery with applications in science and engineering.
The Journal of Combinatorial Optimization publishes refereed papers dealing with all theoretical, computational and applied aspects of combinatorial optimization. It also publishes reviews of appropriate books and special issues of journals.
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