Considering the Criteria interdependency in the Matrix Approach to Robustness Analysis with Applying Fuzzy ANP

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

Choosing the appropriate strategy is the most vital decision for an organization. The real-world situation, comprising increasing criteria and alternatives; the criteria interdependency; environmental changes affecting the structure of the organization; the vagueness of the verbal judgments; and Increasing uncertainty about possible futures, forces the decision-makers to consider these two important elements, complexity and uncertainty, in their decision-making approach. While all of the most widely known approaches – the classic, scenario, MCDM, and robustness analysis approaches – have some weaknesses related to either complexity or uncertainty, the approach purposed in this study can overcome them, combining the matrix approach to the robustness analysis (MARA) with the fuzzy ANP method. This approach deals with the environmental uncertainty by reviewing the performance of the strategies among the alternative futures, the uncertainty related to the preference model of the human decision-maker (uncertain judgements) by using fuzzy set theory, specifically Chang’s extent analysis method, considers desired number of scenarios, criteria and options, and collects experts' judgments in an appropriate time, emphasizing interdependences among criteria. The proposed approach is applied to a real-world problem in the automotive industry of Iran and the results are compared with the previous studies.

Language:
English
Published:
Fuzzy Optimzation and Modeling, Volume:2 Issue: 2, Spring 2021
Pages:
22 to 33
https://www.magiran.com/p2331353  
سامانه نویسندگان
  • Corresponding Author (1)
    Ali Sorourkhah
    Associate Professor Department of Management, Ayandegan Institute of Higher Education, Tonekabon, Iran.
    Sorourkhah، Ali
  • Author (2)
    Seyyed Ahmad Edalatpanah
    (1394) دکتری ریاضی کاربردی، دانشگاه گیلان
    Edalatpanah، Seyyed Ahmad
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