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Article type: Research Article
Authors: Fan, Jianping | Zhai, Shanshan | Wu, Meiqin; *
Affiliations: School of Economics and Management, Shanxi University, Taiyuan, China
Correspondence: [*] Corresponding author. Meiqin Wu, Ph.D., School of Economics and Management, Shanxi University, No.92 Wucheng Rd. Taiyuan, 030006, China. Tel.: +8613834118599; E-mail: [email protected].
Abstract: Neutrosophic cubic set (NCS) can process complex information by combining interval neutrosophic set and single-valued neutrosophic set. It can simultaneously describe the uncertain and certain part of information. Prospect theory (PT) is based on bounded rationality and can reflect decision maker’s different risk attitudes to gains and losses. Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) method can measure and rank the alternatives according to compromise solution. Considering the bounded rationality of decision makers and compromise solution of alternatives, this paper combines the PT with MARCOS method to neutrosophic cubic environment to solve multi-attribute decision-making problem. First, the theoretical basis of NCS is introduced. Second, the PT and MARCOS method are combined. To reflect subjective views of decision makers and the objectivity of decision-making information, this paper uses geometric average method to combine subjective weights (calculated by the best-worst method) and objective weights (calculated ed by the entropy method). Then, the PT-MARCOS method is applied to a decision-making problem. Further, a sensitivity analysis is conducted to study the influence of different attenuation factor values and different expectation coefficient on the ranking; and through comparative analysis to illustrate the superiority of the PT-MARCOS method. Finally is the conclusion.
Keywords: Multi-attribute decision-making, neutrosophic cubic set, prospect theory, measurement of alternatives and ranking according to compromise solution, best-worst method
DOI: 10.3233/JIFS-211189
Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1737-1748, 2022
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