当前位置: X-MOL 学术Int. J. Uncertain. Fuzziness Knowl. Based Syst. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Multiple Criteria Decision Analysis Based on Ill-Known Pairwise Comparison Data
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems ( IF 1.5 ) Pub Date : 2022-11-24 , DOI: 10.1142/s0218488523500022
Masahiro Inuiguchi 1 , Shigeaki Innan 1
Affiliation  

The analytic hierarchy process (AHP) provides a systematic approach to the evaluation of alternatives based on pairwise comparison matrices (PCMs) under multiple criteria. As human evaluation is not always accurate and precise, each component of a PCM showing relative importance has been expressed by an interval or a fuzzy number. In this paper, we treat a PCM whose components are represented by twofold intervals. The twofold intervals are composed of inner and outer intervals showing the range of surely acceptable values and the complement of the range of surely unacceptable values for relative importance, respectively. One may apply a fuzzy AHP approach by building a trapezoidal fuzzy number from the inner and outer intervals. However, this approach does not always fit the given information. Because the twofold interval information ambiguously specifies the range of acceptable values for the relative importance. Then we appropriately translate this information into a set of intervals including the inner interval and included in the outer interval, assuming that the decision-maker evaluates the priority weights implicitly as intervals. We investigate the decision analysis based on the twofold interval PCM. Three conflict resolution methods are proposed for treating the inconsistency in the twofold interval PCM. Parametric methods for visualizing possible alternative orderings are proposed. Numerical examples are given to demonstrate the differences between the proposed approach and the previous approaches.



中文翻译:

基于已知成对比较数据的多准则决策分析

层次分析法 (AHP) 提供了一种系统的方法来评估基于多标准下的成对比较矩阵 (PCM) 的备选方案。由于人类的评估并不总是准确和精确的,因此 PCM 中显示相对重要性的每个组件都用区间或模糊数表示。在本文中,我们处理一个 PCM,其分量由双重间隔表示。双区间由内部区间和外部区间组成,分别显示相对重要性的肯定可接受值范围和肯定不可接受值范围的补集。可以通过从内部和外部间隔构建梯形模糊数来应用模糊 AHP 方法。然而,这种方法并不总是适合给定的信息。因为双重区间信息含糊地指定了相对重要性的可接受值范围。然后我们将此信息适当地转换为一组区间,包括内部区间和包含在外部区间中,假设决策者将优先级权重隐式评估为区间。我们研究了基于双区间 PCM 的决策分析。提出了三种冲突解决方法来处理双区间PCM中的不一致。提出了用于可视化可能的替代排序的参数化方法。给出了数值示例来证明所提出的方法与以前的方法之间的差异。然后我们将此信息适当地转换为一组区间,包括内部区间和包含在外部区间中,假设决策者将优先级权重隐式评估为区间。我们研究了基于双区间 PCM 的决策分析。提出了三种冲突解决方法来处理双区间PCM中的不一致。提出了用于可视化可能的替代排序的参数化方法。给出了数值示例来证明所提出的方法与以前的方法之间的差异。然后我们将此信息适当地转换为一组区间,包括内部区间和包含在外部区间中,假设决策者将优先级权重隐式评估为区间。我们研究了基于双区间 PCM 的决策分析。提出了三种冲突解决方法来处理双区间PCM中的不一致。提出了用于可视化可能的替代排序的参数化方法。给出了数值示例来证明所提出的方法与以前的方法之间的差异。提出了三种冲突解决方法来处理双区间PCM中的不一致。提出了用于可视化可能的替代排序的参数化方法。给出了数值示例来证明所提出的方法与以前的方法之间的差异。提出了三种冲突解决方法来处理双区间PCM中的不一致。提出了用于可视化可能的替代排序的参数化方法。给出了数值示例来证明所提出的方法与以前的方法之间的差异。

更新日期:2022-11-24
down
wechat
bug