Revised solution technique for a bi-level location-inventory-routing problem under uncertainty of demand and perishability of products

Fezzeh Partovi, M. Seifbarghy, M. Esmaeili
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引用次数: 4

Abstract

Bi-level programming is an efficient tool to tackle decentralized decision-making processes in supply chains with upper level (i.e., leader) and lower level (i.e., follower). The leader makes the first decision while the follower makes the second decision. In this research, a bi-level programming formulation for the problem of location-inventory-routing in a two-echelon supply chain, including a number of central warehouses in the first echelon and retailers in the second echelon with perishable products under uncertain demand, is proposed. The total operational costs at both levels are minimized considering capacity constraints. Due to the uncertain nature of the problem, a scenario-based programming is utilized. The economic condition or unforeseen events such as COVID-19 or Russia-Ukraine war can be good examples for uncertainty sources in today’s world. The model determines the optimal locations of warehouses, the routes between warehouses and retailers, number of received shipments and the amount of inventory held at each retailer. A revised solution method is designed by using multi-choice goal programming for solving the problem. The given revised method attempts to minimize the deviations of each decision maker’s solution from its ideal value assuming that the upper level is satisfied higher than the lower level. Base on some numerical analysis, the proposed solution technique is more sensitive to the upper bounds of the goals rather than the lower bounds.
需求不确定和产品易腐性不确定条件下的双层位置-库存-路线问题的修正求解技术
双层规划是解决供应链中上层(即领导者)和下层(即追随者)分散决策过程的有效工具。领导者做第一个决定,而追随者做第二个决定。本文针对需求不确定条件下存在多个中心仓库和零售商的两级供应链的位置-库存-路径问题,提出了一种双层规划公式。考虑到容量限制,这两个级别的总运营成本都最小化。由于问题的不确定性,采用了基于场景的编程。经济状况或诸如COVID-19或俄罗斯-乌克兰战争等不可预见的事件都是当今世界不确定性来源的好例子。该模型确定了仓库的最佳位置、仓库和零售商之间的路线、收到的货物数量以及每个零售商持有的库存数量。利用多选择目标规划设计了一种改进的求解方法。给出的修正方法试图在假设上层比下层更满意的情况下,使每个决策者的解决方案与其理想值的偏差最小化。数值分析表明,该方法对目标的上界比下界更敏感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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