首页|Design alternative assessment and selection: A novel Z-cloud rough number-based BWM-MABAC model
Design alternative assessment and selection: A novel Z-cloud rough number-based BWM-MABAC model
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NSTL
Elsevier
Design alternative assessment is vital in product development since it directly influences the directions of subsequent design and manufacturing activities. The alternative assessment information chiefly depends on experts' subjective perceptions and preferences, which include several types of uncertainty, such as intrapersonal perception ambiguousness, personal judgment reliability, and interpersonal preference inconsistency. However, previous studies usually just consider one of the various uncertainties, which may affect their effectiveness. To fill this gap, we develop an integrated design alternative assessment model integrating Z-cloud rough numbers (ZCRNs), best-worst method (BWM), and multi attributive border approximation area comparison (MABAC). First, to fully handle various uncertainties, a new concept of ZCRN is developed by combining the benefits of cloud model in addressing intrapersonal uncertainty, the merits of Z-numbers in reflecting judgmental reliability, and the strengths of rough numbers in handling interpersonal uncertainty. Some arithmetic operating rules, Minkowski-type distance, comparison measure, correlation measure, and aggregation operators for ZCRNs are also introduced. Furthermore, a ZCRN-BWM method and a ZCRN-MABAC method are developed to calculate criteria weights and rank design alternatives. Finally, a case study, sensitivity analysis on two parameters and a normalization method, and several comparisons are performed to elaborate and validate the developed model.(c) 2022 Elsevier Inc. All rights reserved.
Design alternative assessmentMultiple layers of uncertaintiesZ-cloud rough numbersBest-worst methodMABACGROUP DECISION-MAKINGBEST-WORSTAGGREGATION OPERATORSFEEDBACK MECHANISMGROUP CONSENSUSTOPSISSET
Huang, Guangquan、Xiao, Liming、Pedrycz, Witold、Pamucar, Dragan、Zhang, Genbao、Martinez, Luis
NSTL
Elsevier
