TY - JOUR
T1 - A hybrid decision making aided framework for multi-criteria decision making with R-numbers and preference models
AU - Zhao, Qian
AU - Ju, Yanbing
AU - Dong, Peiwu
AU - Gonzalez, Ernesto D.R.Santibanez
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/5
Y1 - 2022/5
N2 - 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.
AB - 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.
KW - Multi-attributive border approximation area comparison
KW - Multi-criteria decision making
KW - R-numbers
KW - Robust ordinal regression
KW - Stochastic multiobjective acceptability analysis
UR - http://www.scopus.com/inward/record.url?scp=85125923911&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2022.104777
DO - 10.1016/j.engappai.2022.104777
M3 - Article
AN - SCOPUS:85125923911
SN - 0952-1976
VL - 111
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 104777
ER -