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Article type: Research Article
Authors: Hafezalkotob, Ariana | Hafezalkotob, Ashkanb; *
Affiliations: [a] Department of Mechanical Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran | [b] Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
Correspondence: [*] Corresponding author. Ashkan Hafezalkotob, Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran. Tel.: +98 21 7750 8894; Fax: +98 21 7750 0506; E-mail: [email protected].
Abstract: The selection of proper material is a formative stage in engineering design and production process. Without a systematic approach, many practical engineering materials may be neglected for selection. Multiple attribute decision making techniques as efficient methodologies can aid decision-makers in finding the optimal option. As a robust multiple attribute decision making approach, the MULTIMOORA method can be usefully employed in materials selection process. In this study, we extend the MULTIMOORA approach based on Shannon entropy concept under fuzzy environment to solve a materials selection case that is a group multiple attribute decision making problem. In the present paper, subjective weights of attributes and the ratings of alternatives on attributes are assumed as fuzzy sets. The entropy concept is considered to assign objective relative importance to decision-making attributes. The fuzzy subjective weights of attributes that are based on decision-makers comments and the crisp objective weights of attributes obtained using the entropy concept are consolidated to generate the fuzzy integrated entropy weights. We evaluate a practical materials selection problem related to automotive industry. We perform a sensitivity analysis by altering aggregation and normalization techniques besides the types of weighting. The materials rankings of the example are compared with the related study.
Keywords: Multiple attribute decision making, MULTIMOORA, fuzzy logic, Shannon entropy, materials selection
DOI: 10.3233/IFS-162186
Journal: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 3, pp. 1211-1226, 2016
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