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Multiple order-up-to policy for mitigating bullwhip effect in supply chain network

机译:减少供应链网络中牛鞭效应的多重订购策略

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This paper proposes a multiple order-up-to policy based inventory replenishment scheme to mitigate the bullwhip effect in a multi-stage supply chain scenario, where various transportation modes are available between the supply chain (SC) participants. The proposed policy is similar to the fixed order-up-to policy approach where replenishment decision “how much to order” is made periodically on the basis of the pre-decided order-up-to inventory level. In the proposed policy, optimal multiple order-up-to levels are assigned to each SC participants, which provides decision making reference point for deciding the transportation related order quantity. Subsequently, a mathematical model is established to define optimal multiple order-up-to levels for each SC participants that aims to maximize overall profit from the SC network. In parallel, the model ensures the control over supply chain pipeline inventory, high satisfaction of customer demand and enables timely utilization of available transportation modes. Findings from the various numerical datasets including stochastic customer demand and lead times validate that—the proposed optimal multiple order-up-to policy based inventory replenishment scheme can be a viable alternative for mitigating the bullwhip effect and well-coordinated SC. Moreover, determining the multiple order-up-to levels is a NP hard combinatorial optimization problem. It is found that the implementation of new emerging optimization algorithm named bacterial foraging algorithm (BFA) has presented superior optimization performances. The robustness and applicability of the BFA algorithm are further validated statistically by employing the percentage heuristic gap and two-way ANOVA analysis.
机译:本文提出了一种基于策略的基于订单最多补货的库存补充方案,以缓解多阶段供应链场景中的牛鞭效应,在这种情况下,供应链(SC)参与者之间可以使用各种运输方式。拟议的政策类似于固定订购政策,即根据预先确定的订购库存水平定期做出补货决定“订购多少”。在提出的策略中,为每个SC参与者分配了最佳的多个订购级别,这为确定运输相关的订购量提供了决策参考点。随后,建立了一个数学模型来为每个SC参与者定义最佳的多个订购级别,以最大化SC网络的总体利润。同时,该模型可确保对供应链管道库存的控制,对客户需求的高度满意并能够及时利用可用的运输方式。从包括随机客户需求和交货时间在内的各种数值数据中得出的结论证实,基于提议的基于策略的最优多重订购策略的库存补充方案可以缓解牛鞭效应和协调良好的供应链。此外,确定多个最上级是NP硬组合优化问题。结果发现,新出现的名为细菌觅食算法(BFA)的优化算法的实现表现出了优越的优化性能。 BFA算法的鲁棒性和适用性通过采用百分比启发式差距和双向ANOVA分析进行了统计验证。

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