Reverse Supply Chain Design: A Neural Network Approach
Reverse Supply Chain Design: A Neural Network Approach
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DOI：
10.4018/978-1-60566-114-8.ch013
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
K. Pochampally;S. Gupta
中科院分区：
文献类型：
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作者：
K. Pochampally;S. Gupta
逆向供应链的成功很大程度上依赖于设计逆向供应链时所选择的收集设施和回收设施的效率。在本章中，我们提出了一种神经网络方法来评估感兴趣的设施（收集或回收）的效率，该设施正在考虑包含在逆向供应链中，使用已经存在于逆向供应链中的设施的可用语言数据。该方法分四个阶段进行，如下所示：在第一阶段，我们为参与逆向供应链的每个小组确定感兴趣的设施的评估标准。然后，在第二阶段，我们使用现有设施的模糊评级来构建一个神经网络，该网络给出了在第一阶段为每个组确定的标准的影响（重要性值）。然后，在第三阶段，使用在第二阶段获得的影响，我们采用模糊TOPSIS（通过与理想解决方案相似的顺序偏好技术）方法来获得每个组计算的感兴趣设施的总体评级。最后，在第四阶段，我们采用Borda选择规则来计算兴趣设施的最大共识评级（在所考虑的群体之间）。
The success of a reverse supply chain heavily relies on the efficiency of the collection facilities and recovery facilities chosen while designing that reverse supply chain. In this chapter, we propose a neural network approach to evaluate the efficiency of a facility (collection or recovery) of interest, which is being considered for inclusion in a reverse supply chain, using the available linguistic data of facilities that already exist in the reverse supply chain. The approach is carried out in four phases, as follows: In phase I, we identify criteria for evaluation of the facility of interest, for each group participating in the reverse supply chain. Then, in phase II, we use fuzzy ratings of already existing facilities to construct a neural network that gives impacts (importance values) of criteria identified for each group in phase I. Then, in phase III, using the impacts obtained in phase II, we employ a fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach to obtain the overall rating of the facility of interest, as calculated by each group. Finally, in phase IV, we employ Borda’s choice rule to calculate the maximized consensus (among the groups considered) rating of the facility of interest.