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Title: A fuzzy rough number-based AHP-TOPSIS for design concept evaluation under uncertain environments
Authors: Zhu, Guoniu
Hu, Jie
Ren, Hongliang
Keywords: Engineering::Mechanical engineering
Issue Date: 2020
Source: Zhu, G., Hu, J. & Ren, H. (2020). A fuzzy rough number-based AHP-TOPSIS for design concept evaluation under uncertain environments. Applied Soft Computing Journal, 91, 106228-.
Journal: Applied Soft Computing Journal
Abstract: Design concept evaluation in the early phase of product design plays a crucial role in new product development as it considerably determines the direction of subsequent design activities. However, it is a process involving uncertainty and subjectivity. The evaluation information mainly relies on expert's subjective judgment, which is imprecise and uncertain. How to effectively and objectively evaluate the design concept under such subjective and uncertain environments remains an open question. To fill this gap, this paper proposes a fuzzy rough number-enhanced group decision-making framework for design concept evaluation by integrating a fuzzy rough number-based AHP (analytic hierarchy process) and a fuzzy rough number-based TOPSIS (technique for order preference by similarity to ideal solution). First of all, a fuzzy rough number is presented to aggregate personal risk assessment information and to manipulate the uncertainty and subjectivity during the decision-making. Then a fuzzy rough number-based AHP is developed to determine the criteria weights. A fuzzy rough number-based TOPSIS is proposed to conduct the alternative ranking. A practical case study is put forward to illustrate the applicability of the proposed decision-making framework. Experimental results and comparative studies demonstrate the superiority of the fuzzy rough number-based method in dealing with the uncertainty and subjectivity in design concept evaluation under group decision-making environment.
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2020.106228
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2020 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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