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Multi-period design and planning of closed-loop supply chains with uncertain supply and demand

机译:供需不确定的闭环供应链的多周期设计与规划

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摘要

A design and planning approach is proposed for addressing general multi-period, multi-product closed-loop supply chains (CLSCs), structured as a 10-layer network (5 forward plus 5 reverse flows), with uncertain levels in the amount of raw material supplies and customer demands. The consideration of a multi-period setting leads to a multi-stage stochastic programming problem, which is handled by a mixed-integer linear programming (MILP) formulation. The effects of uncertain demand and supply on the network are considered by means of multiple scenarios, whose occurrence probabilities are assumed to be known. Several realistic supply chain requirements are taken into account, such as those related to the operational and environmental costs of different transportation modes, as well as capacity limits on production, distribution and storage. Moreover, multiple products are considered, which are grouped according to their recovery grade. The objective function minimizes the expected cost (that includes facilities, purchasing, storage, transport and emissions costs) minus the expected revenue due to the amount of products returned, from repairing and decomposition centers to the forward network. Finally, computational results are discussed and analyzed in order to demonstrate the effectiveness of the proposed approach. Due to the large size of the addressed optimization problem containing all possible scenarios for the two uncertain parameters, scenario reduction algorithms are applied to generate a representative, albeit smaller, subset of scenarios.
机译:提出了一种设计和计划方法来解决由多层次,多产品闭环供应链(CLSC)构成的10层网络(5个正向流和5个反向流)的结构,其中原始数量不确定材料供应和客户需求。多周期设置的考虑导致了多阶段随机编程问题,该问题由混合整数线性编程(MILP)公式处理。不确定的需求和供应对网络的影响是通过多种情况来考虑的,假定它们的出现概率是已知的。考虑了一些现实的供应链要求,例如与不同运输模式的运营和环境成本有关的要求,以及对生产,分配和存储的容量限制。此外,考虑了多个产品,这些产品根据其恢复等级进行分组。目标函数将预期成本(包括设施,采购,存储,运输和排放成本)减至最小,这要归因于从维修和分解中心到前期网络的退回产品数量所导致的预期收益。最后,对计算结果进行了讨论和分析,以证明所提方法的有效性。由于包含两个不确定参数的所有可能方案的已解决优化问题的规模很大,因此采用方案减少算法来生成代表性的方案(尽管规模较小)。

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