A hybrid decision making aided framework for multi-criteria decision making with R-numbers and preference models

• Published in: Engineering applications of artificial intelligence 2022-05, Vol.111, p.104777, Article 104777
• Main Authors: Zhao, Qian, Ju, Yanbing, Dong, Peiwu, Gonzalez, Ernesto D.R. Santibanez
• Format: Article
• Language: English
• Subjects: Multi-attributive border approximation area comparison; Multi-criteria decision making; R-numbers; Robust ordinal regression; Stochastic multiobjective acceptability analysis
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2022.104777

As a risk modeling about fuzzy numbers, R-numbers have successfully extended to multi-criteria decision making (MCDM) methods for the real-life decision making problems involving the risk and uncertainties associated with fuzzy numbers. To obtain more reliable and robust multi-criteria ranking alternatives in these uncertain situations, a hybrid decision making aided framework involving stochastic multiobjective acceptability analysis (SMAA), robust ordinal regression (ROR), and multi-attributive border approximation area comparison (MABAC) is proposed for MCDM problems with risk factors and preference models. Firstly, some novel operations of the R-numbers associated with triangular fuzzy numbers are proposed to explore a broader application scope. Secondly, a novel MABAC method combined with the R-numbers is proposed for MCDM problems which focus on uncertainty and error of triangular fuzzy numbers. Thirdly, a hybrid decision making aided framework which applies SMAA and ROR into the novel MABAC method is proposed for obtaining robust multi-criteria ranking alternatives through two binary relations, and two measures complement each other. Moreover, a Monte Carlo simulation of the framework is performed. Lastly, an application of assessment of wind energy potential and comparative analysis is provided to illustrate the efficiency and superiority of the proposed framework. •A novel MABAC method combined with R-numbers is proposed.•We extend ROR to novel MABAC for the quantitative relation of alternatives.•We extend SMAA to novel MABAC for qualitative relationship of alternatives.•A hybrid decision making aided framework is proposed for robust ranking.•A Monte Carlo simulation of the aided framework is performed.