Multiple-attribute decision making under uncertainty: The evidential reasoning approach revisited
Multiple-attribute decision making under uncertainty: The evidential reasoning approach revisited
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DOI：
10.1109/tsmca.2005.855778
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发表时间：
2006-07-01
影响因子：
--
通讯作者：
Murai, Tetsuya
中科院分区：
文献类型：
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作者：
Huynh, Van-Nam;Nakamori, Yoshiteru;Murai, Tetsuya
在多属性决策（MADM）问题中，人们常常需要处理具有不确定性的决策信息。在过去的十年中，Yang 和 Singh（1994）提出并发展了一个证据。推理（ER）方法来处理此类 MADM 问题。本质上，该方法基于评估分析模型和登普斯特-谢弗（D-S）证据理论中的登普斯特组合规则。本文根据D-S理论对ER方法进行了明确的重新分析，然后提出了不确定性下MADM属性聚合的一般方案。本着这种重新分析的精神，我们回顾了以前的 ER 算法，并讨论了另外两种聚合方案。理论上，新的聚合方案也满足由 Yang 和 Xu (2002) 最近提出的综合公理，任何合理的聚合过程都应满足该公理。传统上在已发表的 ER 方法中检查的数值示例用于说明所讨论的技术。
In multiple-attribute decision making (MADM) problems, one often needs to deal with decision information with uncertainty. During the last decade, Yang and Singh (1994) have proposed and developed an evidential. reasoning (ER) approach to deal with such MADM problems. Essentially, this approach is based on an evaluation analysis model and Dempster's rule of combination in the Dempster-Shafer (D-S) theory of evidence. This paper reanalyzes the ER approach explicitly in terms of D-S theory and then proposes a general scheme of attribute aggregation in MADM under uncertainty. In the spirit of such a reanalysis, previous ER algorithms are reviewed and two other aggregation schemes are discussed. Theoretically, it is shown that new aggregation schemes also satisfy the synthesis axioms, which have been recently proposed by Yang and Xu (2002) for which any rational aggregation process should grant. A numerical example traditionally examined in published sources on the ER approach is used to illustrate the discussed techniques.