ABSTRACT

Inventory control and logistics are both important to the success of a supply chain. These problems become highly complicated when the demand of end-users is uncertain. The demand uncertainty could have a considerable destructive effect on a supply chain, e.g. bullwhip effect. Therefore, this paper proposes a two-stage approach to study an inventory-routing problem for a three-echelon single-item supply chain, consisting of suppliers, manufacturers, distributors, and wholesalers. The approach utilizes the $(1,T)$ ordering policy in the first stage to determine the optimal replenishment ordering quantity of wholesalers under uncertainty. This stage aims to minimize the inventory holding and lost-sales costs. To the best of our knowledge, this is the first application of the $(1,T)$ policy to an inventory-routing problem. Afterward, a mathematical formulation is proposed in the second stage to study a new vehicle routing problem. This model minimizes the transportation cost. Based on the literature, the vehicle routing problem is NP-hard; therefore, a new accelerated Benders decomposition algorithm is developed to solve large-scale instances of the problem. The algorithm incorporates a modified $ϵ$-optimality accelerator. The computational results show the superior performance of the modified accelerator compared to the conventional one. To evaluate the performance of the accelerated Bender decomposition algorithm, we compare it with the mathematical programming and a Genetic algorithm using twenty benchmark examples. We also introduce and assess a real-case study in the automotive parts industry. Finally, we analyze some key features of the case study to provide managerial implications.

摘要

库存控制和物流对于供应链的成功都很重要。当最终用户的需求不确定时，这些问题变得非常复杂。需求不确定性可能对供应链产生相当大的破坏性影响，例如牛鞭效应。因此，本文提出了一种分两阶段的方法来研究由供应商，制造商，分销商和批发商组成的三级单项供应链的库存路由问题。该方法利用$（\mathrm{1个}，Ť）$第一阶段的订购政策，以确定不确定性下批发商的最佳补货订购量。此阶段旨在最小化库存持有和销售损失成本。据我们所知，这是$（\mathrm{1个}，Ť）$解决库存路由问题的策略。随后，在第二阶段提出了数学公式来研究新的车辆路径问题。该模型使运输成本最小化。根据文献，车辆路径问题是NP难的。因此，开发了一种新的加速Benders分解算法来解决该问题的大规模实例。该算法结合了修改后的$ϵ$-优化加速器。计算结果表明，与传统加速器相比，改进型加速器具有优越的性能。为了评估加速的Bender分解算法的性能，我们将其与数学编程和使用二十个基准示例的遗传算法进行了比较。我们还将介绍和评估汽车零部件行业的实际案例研究。最后，我们分析了案例研究的一些关键特征，以提供管理上的启示。