考虑库存政策的仓库位置分配双层问题

IF 1.6 4区 计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Networks Pub Date : 2024-05-31 DOI:10.1002/net.22235
José‐Fernando Camacho‐Vallejo, Dámaris Dávila, Leopoldo Eduardo Cárdenas‐Barrón
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引用次数: 0

摘要

本文探讨了一家公司面临的位置分配问题,该公司的目标是确定仓库位置,以便向一组客户供应产品。该公司的目标是最大限度地降低仓库选址的总成本和库存政策造成的成本。然而,这些库存决策是由不同的决策者做出的。换句话说,一旦公司做出了仓库选址决策,与每个仓库相关的决策者就必须确定自己的订货量。仓库允许有一定数量的最大滞销订单,这意味着仓库需要承担额外的成本。这种情况可以模拟为一个两级编程问题,上一级问题与公司相关,公司需要最大限度地降低与位置分配和每个仓库总订单相关的成本。每个仓库都与独立的下层相关联,其中仓库经理的目标是最大限度地降低库存总成本。双层问题导致单目标高层问题与非线性、多个独立的低层问题,因此要找到最优解通常具有挑战性。本文提出了一种基于群体的 "头脑风暴 "优化算法方案下的元启发式。为了解决与下层相关的每个非线性问题,采用了拉格朗日法。两个决策层都以嵌套方式求解,从而获得双层可行解。为了验证所提算法的有效性,我们采用了一种枚举算法。我们考虑了一组基准实例来进行计算实验。结果表明,对于小规模的实例,所提出的算法可以达到最优。对于较大的实例,所提出的算法也表现出同样的效率和一致的结果。最后,还介绍了从计算实验中得出的有趣的管理启示,以及对未来研究方向的一些建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Networks
Networks 工程技术-计算机:硬件
CiteScore
4.40
自引率
9.50%
发文量
46
审稿时长
12 months
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.
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