Risk-Based Multi-Attribute Decision-Making for Normal Cloud Model Considering Pre-Evaluation Information

IEEE Access

Zhenzhen Ma¹  Shitao Zhang² 

[1] School of Economics and Management, Beihang University, Beijing, China;School of Mathematics and Physics Science and Engineering, Anhui University of Technology, Ma&x2019;

关键词: Multi-attribute decision making;    normal cloud model;    risk attitudes;    pre-evaluation information;    prospect theory;   

DOI  :  10.1109/ACCESS.2020.3018153

来源: DOAJ

【 摘 要 】

An uncertain multi-attribute decision-making (MADM) problem is studied based on cloud models. Cloud models, referring to fuzziness and randomness, are utilized to depict evaluation and pre-evaluation information which can reflect the future development performance of alternatives. Because of bounded rationality, decision maker's (DM) risk attitudes should be considered when facing uncertainty. Thus, a behavioral MADM (BMADM) method is proposed by considering DM's risk attitudes and pre-evaluation. First, a distance measure for normal cloud models is developed with consideration of both DM's risk preferences and random distribution, aiming at making full use of information. Second, as a basis of applying prospect theory, positive ideal reference point is set by considering both evaluation and pre-evaluation information from three aspects: risk-averse, risk-neutral, and risk-seeking preference coefficients, in which the sign of distance is not necessary to determine. The third element is the establishment of an optimization model for handling incomplete attribute weights, following which is to obtain the ranking of alternatives. The final phase is the application of the proposed method to one case, along with sensitivity and comparison analyses, as a means of illustrating the applicability and feasibility of the new method.

【 授权许可】

Unknown