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Optimizing a multi-vendor multi-retailer vendor managed inventory problem: Two tuned meta-heuristic algorithms

机译：优化多供应商，多零售商，供应商管理的库存问题：两种调整后的元启发式算法

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摘要

The vendor-managed inventory (VMI) is a common policy in supply chain management (SCM) to reduce bullwhip effects. Although different applications of VMI have been proposed in the literature, the multi-vendor multi-retailer single-warehouse (MV-MR-SW) case has not been investigated yet. This paper develops a constrained MV-MR-SW supply chain, in which both the space and the annual number of orders of the central warehouse are limited. The goal is to find the order quantities along with the number of shipments received by retailers and vendors such that the total inventory cost of the chain is minimized. Since the problem is formulated into an integer nonlinear programming model, the meta-heuristic algorithm of particle swarm optimization (PSO) is presented to find an approximate optimum solution of the problem. In the proposed PSO algorithm, a genetic algorithm (GA) with an improved operator, namely the boundary operator, is employed as a local searcher to turn it to a hybrid PSO. In addition, since no benchmark is available in the literature, the GA with the boundary operator is proposed as well to solve the problem and to verify the solution. After employing the Taguchi method to calibrate the parameters of both algorithms, their performances in solving some test problems are compared in terms of the solution quality.
机译：供应商管理的库存（VMI）是供应链管理（SCM）中的通用策略，可减少牛鞭效应。尽管在文献中已经提出了VMI的不同应用，但是尚未研究多供应商多零售商单仓库（MV-MR-SW）的情况。本文开发了一个受约束的MV-MR-SW供应链，其中中央仓库的空间和年度订单数量都受到限制。目的是找到订单数量以及零售商和供应商收到的发货数量，以使链的总库存成本最小化。由于将问题表述为整数非线性规划模型，因此提出了粒子群优化算法（PSO）的元启发式算法，以找到问题的近似最优解。在提出的PSO算法中，采用具有改进算子（即边界算子）的遗传算法（GA）作为本地搜索器，将其转换为混合PSO。另外，由于在文献中没有基准可用，因此还提出了带有边界算子的遗传算法来解决问题并验证解决方案。在使用Taguchi方法校准两种算法的参数后，根据解决方案质量比较了它们在解决某些测试问题上的性能。

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