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A revised procedure to identify λ~0-measure values for applying Choquet integral in solving multi-attribute decision problems

机译:修订程序,用于识别λ〜0度量的值,用于解决求解多属性决策问题的Choquet积分

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摘要

Choquet integral is currently being employed in many multi-attribute decision problems thanks to its ability in capturing the interactions that usually exist between the evaluation attributes during the aggregation process. However, the process of identifying 2~n values of fuzzy measure prior to applying Choquet integral normally turns into a complex one especially when the decision problem involves large number of evaluation attributes, n. Many patterns of fuzzy measure have been introduced to deal with this complexity and λ~0-measure is one such pattern. Unfortunately, the existing λ~0-measure identification procedure failed to provide clear indications as to which attributes need to be enhanced in order to significantly inflate the performance of alternatives. That being the case, this paper proposed a revised version of the original λ~0-measure identification procedure through the integration of decision making trial and evaluation laboratory (DEMATEL) model. The revised procedure uses DEMATEL to identify the causal-effect relations between the attributes. The outputs of DEMATEL (i.e. digraph and importance ratios) are then utilized to determine the inputs required to identify the complete set of λ~0-measure values. A vendor evaluation problem was used to demonstrate the feasibility of the procedure. The differences between the revised and original procedure were discussed as well.
机译:Choquet Integral目前在许多多属性决策问题中受雇于捕获聚合过程中评估属性之间通常存在的交互的能力。然而,在应用Choquet积分之前识别2〜n值的模糊测量值通常在决策问题涉及大量评估属性时变成复杂的一个复杂。已经引入了许多模糊措施模式来处理这种复杂性,并且λ〜0度量是一种这样的模式。不幸的是,现有的λ〜0度量识别程序未能提供明确的指示,以便需要增强哪些属性,以便显着地膨胀替代品的性能。就是这种情况,本文通过集成决策试验和评估实验室(DEMATEL)模型,提出了原始λ〜0-MEATION识别程序的修订版。修订后的程序使用Dematel来确定属性之间的因果关系。然后利用Dematel(即,Digraph和MaximaPrations)的输出来确定识别完整的λ〜0度量值所需的输入。供应商评估问题用于展示程序的可行性。还讨论了修订和原始程序之间的差异。

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