{"title":"考虑库存政策的仓库位置分配双层问题","authors":"José‐Fernando Camacho‐Vallejo, Dámaris Dávila, Leopoldo Eduardo Cárdenas‐Barrón","doi":"10.1002/net.22235","DOIUrl":null,"url":null,"abstract":"A location‐allocation problem faced by a company that aims to locate warehouses to supply products to a set of customers is addressed in this paper. The company's objective is to minimize the total cost of locating the warehouses and the cost due to inventory policies. However, these inventory decisions are made by a different decision‐maker. In other words, once the company makes the location decisions, the decision‐maker associated with each warehouse must determine its own order quantity. Warehouses are allowed to have a certain maximum number of backorders, which represents an extra cost for them. This situation can be modeled as a bilevel programming problem, where the upper level is associated with the company that needs to minimize the costs related to location‐allocation and the total orders of each warehouse. Each warehouse is associated with an independent lower level, in which a warehouse manager aims to minimize the total inventory cost. The bilevel problem results in a single‐objective upper‐level problem with non‐linear, multiple independent lower‐level problems, making it generally challenging to find an optimal solution. A population‐based metaheuristic under the Brain Storm Optimization algorithm scheme is proposed. To solve each non‐linear problem associated with the lower level, the Lagrangian method is applied. Both decision levels are solved in a nested manner, leading to obtaining bilevel feasible solutions. To validate the effectiveness of the proposed algorithm, an enumerative algorithm is implemented. A set of benchmark instances has been considered to conduct computational experiments. Results show that optimality is achieved by the proposed algorithm for small‐sized instances. In the case of larger‐sized instances, the proposed algorithm demonstrates the same efficiency and consistent results. Finally, interesting managerial insights deduced from the computational experimentation and some proposals for future research directions are included.","PeriodicalId":54734,"journal":{"name":"Networks","volume":"103 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A warehouse location‐allocation bilevel problem that considers inventory policies\",\"authors\":\"José‐Fernando Camacho‐Vallejo, Dámaris Dávila, Leopoldo Eduardo Cárdenas‐Barrón\",\"doi\":\"10.1002/net.22235\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A location‐allocation problem faced by a company that aims to locate warehouses to supply products to a set of customers is addressed in this paper. The company's objective is to minimize the total cost of locating the warehouses and the cost due to inventory policies. However, these inventory decisions are made by a different decision‐maker. In other words, once the company makes the location decisions, the decision‐maker associated with each warehouse must determine its own order quantity. Warehouses are allowed to have a certain maximum number of backorders, which represents an extra cost for them. This situation can be modeled as a bilevel programming problem, where the upper level is associated with the company that needs to minimize the costs related to location‐allocation and the total orders of each warehouse. Each warehouse is associated with an independent lower level, in which a warehouse manager aims to minimize the total inventory cost. The bilevel problem results in a single‐objective upper‐level problem with non‐linear, multiple independent lower‐level problems, making it generally challenging to find an optimal solution. A population‐based metaheuristic under the Brain Storm Optimization algorithm scheme is proposed. To solve each non‐linear problem associated with the lower level, the Lagrangian method is applied. Both decision levels are solved in a nested manner, leading to obtaining bilevel feasible solutions. To validate the effectiveness of the proposed algorithm, an enumerative algorithm is implemented. A set of benchmark instances has been considered to conduct computational experiments. Results show that optimality is achieved by the proposed algorithm for small‐sized instances. In the case of larger‐sized instances, the proposed algorithm demonstrates the same efficiency and consistent results. Finally, interesting managerial insights deduced from the computational experimentation and some proposals for future research directions are included.\",\"PeriodicalId\":54734,\"journal\":{\"name\":\"Networks\",\"volume\":\"103 1\",\"pages\":\"\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1002/net.22235\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Networks","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1002/net.22235","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
A warehouse location‐allocation bilevel problem that considers inventory policies
A location‐allocation problem faced by a company that aims to locate warehouses to supply products to a set of customers is addressed in this paper. The company's objective is to minimize the total cost of locating the warehouses and the cost due to inventory policies. However, these inventory decisions are made by a different decision‐maker. In other words, once the company makes the location decisions, the decision‐maker associated with each warehouse must determine its own order quantity. Warehouses are allowed to have a certain maximum number of backorders, which represents an extra cost for them. This situation can be modeled as a bilevel programming problem, where the upper level is associated with the company that needs to minimize the costs related to location‐allocation and the total orders of each warehouse. Each warehouse is associated with an independent lower level, in which a warehouse manager aims to minimize the total inventory cost. The bilevel problem results in a single‐objective upper‐level problem with non‐linear, multiple independent lower‐level problems, making it generally challenging to find an optimal solution. A population‐based metaheuristic under the Brain Storm Optimization algorithm scheme is proposed. To solve each non‐linear problem associated with the lower level, the Lagrangian method is applied. Both decision levels are solved in a nested manner, leading to obtaining bilevel feasible solutions. To validate the effectiveness of the proposed algorithm, an enumerative algorithm is implemented. A set of benchmark instances has been considered to conduct computational experiments. Results show that optimality is achieved by the proposed algorithm for small‐sized instances. In the case of larger‐sized instances, the proposed algorithm demonstrates the same efficiency and consistent results. Finally, interesting managerial insights deduced from the computational experimentation and some proposals for future research directions are included.
期刊介绍:
Network problems are pervasive in our modern technological society, as witnessed by our reliance on physical networks that provide power, communication, and transportation. As well, a number of processes can be modeled using logical networks, as in the scheduling of interdependent tasks, the dating of archaeological artifacts, or the compilation of subroutines comprising a large computer program. Networks provide a common framework for posing and studying problems that often have wider applicability than their originating context.
The goal of this journal is to provide a central forum for the distribution of timely information about network problems, their design and mathematical analysis, as well as efficient algorithms for carrying out optimization on networks. The nonstandard modeling of diverse processes using networks and network concepts is also of interest. Consequently, the disciplines that are useful in studying networks are varied, including applied mathematics, operations research, computer science, discrete mathematics, and economics.
Networks publishes material on the analytic modeling of problems using networks, the mathematical analysis of network problems, the design of computationally efficient network algorithms, and innovative case studies of successful network applications. We do not typically publish works that fall in the realm of pure graph theory (without significant algorithmic and modeling contributions) or papers that deal with engineering aspects of network design. Since the audience for this journal is then necessarily broad, articles that impact multiple application areas or that creatively use new or existing methodologies are especially appropriate. We seek to publish original, well-written research papers that make a substantive contribution to the knowledge base. In addition, tutorial and survey articles are welcomed. All manuscripts are carefully refereed.