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RESEARCH PAPER
A Z-number and MABAC method based on reliability analysis and evaluation of product design concept
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Shandong Jiaotong University, China
 
 
Submission date: 2023-11-01
 
 
Final revision date: 2023-11-29
 
 
Acceptance date: 2024-01-05
 
 
Online publication date: 2024-01-08
 
 
Publication date: 2024-01-08
 
 
Corresponding author
Zhe Chen   

Shandong Jiaotong University, China
 
 
Eksploatacja i Niezawodność – Maintenance and Reliability 2024;26(1):178304
 
HIGHLIGHTS
  • Utilizes Z-numbers for detailed customercentric product design evaluation.
  • Proposes MABAC for optimal design choice using expert and attribute weight integration.
  • Comparing trapezoidal and triangular fuzzy numbers in customer-centric design decisions.
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ABSTRACT
Modular design is a significant method for complicated product development. In the context of modular design, involving users in concept assessment boosts a product's appeal but also introduces decision uncertainty and unreliability. As a solution, this paper proposed a hybrid method by integrating expert consensus modeling, attribute weighting, Z-number, and the Multi-Attribute Border Approximation Area Comparison (MABAC) method. Initially, a consensus model is established using consistency theory to determine expert weights, and attribute priorities are determined through the entropy weighting method. Subsequently, the Z-number-based MABAC method ranks the alternatives, determining the optimal solution among them. Using an automated outdoor cleaning vehicle as an example, the proposed method is compared to other techniques. The sensitivity analysis and the comparisons show that the proposed method improves the reliability and objective of the decision-making process.
ACKNOWLEDGEMENTS
This work was supported by the Shandong Construction Machinery Intelligent Equipment Innovation& Entrepreneurship Community (GTT20230102) awarded to Baoping Wang; Science and Technology oriented Small and Medium sized Enterprise Innovation Capability Enhan cement Project of Shandong Province (2021TSGC1441) awarded to Baoping Wang.
eISSN:2956-3860
ISSN:1507-2711
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