一种新的循环直觉模糊AHP-VIKOR方法:在多专家供应商评价问题中的应用

IF 0.4 Q4 ENGINEERING, MULTIDISCIPLINARY
Irem Otay, C. Kahraman
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引用次数: 14

摘要

VIKOR方法是一种常用的多准则决策方法,它基于正理想解和负理想解的备选距离,并给出折衷解。AHP是另一种MCDM方法,通过对标准和备选方案的两两比较,将大问题划分为小问题和可管理的问题。在这些方法中,语言评估通常是首选,因为标准值的精确数值分配非常困难,专家无法用清晰的数字反映他们心中的想法。模糊集理论通过模糊数成功地捕捉了这些语言评价中的模糊性和不精确性。循环直觉模糊集(Circular intuistic fuzzy set, C-IFS)是由Atanassov[1]提出的对普通模糊集的最新推广。C-IFS通过纳入这些度的不确定性,帮助专家定义成员(归属)和非成员(不归属)度。本文提出了一种集成的C-IF AHP和C-IF VIKOR方法,并将其应用于多专家问题
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel circular intuitionistic fuzzy AHP-VIKOR methodology: An application to a multi-expert supplier evaluation problem
VIKOR method being one of the frequently used Multi-Criteria Decision Making (MCDM) methods, is based on the distances of alternatives to positive and negative ideal solutions, and presents compromising solutions. AHP is another MCDM method dividing the big problem into small and manageable problems through pairwise comparisons of criteria and alternatives. In these methods, linguistic assessments are generally preferred since exact numerical assignments of criteria values are really difficult and experts can not reflect the thoughts in their minds with crisp numbers. The fuzzy set theory captures the vagueness and impreciseness in these linguistic assessments successfully thorough fuzzy numbers. Circular intuitionistic fuzzy sets (C-IFS) are the latest extension of ordinary fuzzy sets, which was introduced by Atanassov [1]. C-IFS help experts to define membership (belongingness) and non- membership (unbelongingness) degrees by incorporating the uncertainty of these degrees. In this paper, an integrated C-IF AHP & C- IF VIKOR methodology is developed and applied to a multi-expert
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自引率
25.00%
发文量
49
审稿时长
25 weeks
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