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A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network

机译:基于层次分析法,模糊集理论和神经网络的供应商选择混合群决策支持系统

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Within procurement literature; many studies focus on providing decision support to the supplier selection problem. However, studies on group decision support are yet to be explored extensively within supplier selection literature, despite its benefits. This study presents the application of a hybrid approach for group decision support for the supplier selection problem. fuzzy set theory, analytic hierarchy process and neural networks have been integrated to provide group decision support under consensus achievement. Discriminant analysis has been used for the purpose of supplier base rationalization, through which suppliers have been mapped to highly suitable and less suitable supplier classes. The proposed integrated approach has been further studied through two case studies and the proposed approach has been compared with another approach for group decision making under consensus and other approaches for prioritization using AHP, without consensus achievement. A very high accuracy in capturing the collective consensual preferences of the group was established across eight cross-validation tests from the two case studies, for the hybrid approach, even with extremely limited count of data sets which were used for training the hybrid model.
机译:在采购文献中;许多研究致力于为供应商选择问题提供决策支持。然而,尽管有很多好处,但是关于团体决策支持的研究尚未在供应商选择文献中得到广泛探讨。这项研究提出了一种混合方法在供应商选择问题的群体决策支持中的应用。模糊集理论,层次分析法和神经网络相结合,为达成共识下的群体决策提供支持。判别分析已用于供应商基础合理化的目的,通过该分析将供应商映射到高度合适和不太合适的供应商类别。拟议的综合方法已通过两个案例研究进行了进一步研究,并将拟议的方法与在共识下进行小组决策的另一种方法进行了比较,并在没有达成共识的情况下使用了AHP进行其他优先排序。在两个案例研究的八项交叉验证测试中,对于混合方法,即使在用于训练混合模型的数据集数量极少的情况下,也能非常准确地捕获该组的集体共识偏好。

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