A minimum deviation approach for improving the consistency of uncertain 2-tuple linguistic preference relations
A minimum deviation approach for improving the consistency of uncertain 2-tuple linguistic preference relations
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DOI：
10.1016/j.cie.2018.01.024
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
Shengbao Yao;Jiaqiao Hu
中科院分区：
文献类型：
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作者：
Shengbao Yao;Jiaqiao Hu
提出了一种改进不确定二元组语言偏好关系一致性的新方法。特别地，我们引入了U2TLPR的一致性的新定义，并证明了给定U2TLPR的一致性程度可以通过最小化其与相容U2TLPR的偏差来显式地度量。基于这一发现，我们提出了一种迭代算法，在考虑决策者的初始偏好的同时，反复将U2TLPR的一致性调整到期望的水平。此外，通过分析算法的结构性质，我们进一步提出了一种改进的算法，无需任何迭代就可以直接获得可接受的U2TLPR。数值结果表明，该方法不仅计算简单、效率高，而且能有效地保留决策者提供的原始偏好信息。
This paper proposes a novel approach for improving the consistency of uncertain 2-tuple linguistic preference relations (U2TLPRs). In particular, we introduce a new definition of consistency for U2TLPRs and show that the degree of consistency of a given U2TLPR can be measured explicitly by minimizing its deviation from a consistent U2TLPR. Based on this finding, we provide an iterative algorithm for repeatedly adjusting the consistency of a U2TLPR to a desired level while taking into account the initial preferences of decision makers. In addition, by analyzing the structural properties of the algorithm, we further present an improved version of the procedure for directly obtaining an acceptable U2TLPR without any iteration. Numerical results indicate that the proposed method is not only simple and efficient in calculation but also effective in preserving the original preference information provided by decision makers.