Abstract
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.
Highlights
M ULTI-ATTRIBUTE decision making (MADM) is a process of ranking alternatives or selecting the best alternative from several alternatives with respect to a set of attributes [1]
It is evident from the figure that there are two features of the prospect values associated with alternatives with respect to each attribute: (a) the prospect values are all negative, this is because the reference points are set by using positive idea points, its merit lies that the direction of distance is not required to be firstly detected, which eliminates the bias brought by subjective judgements; (b) the rankings of alternatives are different under different attributes, illustrating that an appropriate approach is required for aggregating these values, and the following step is used to determine the attribute weights
For MADM problems with some uncertainty and risk being included, we propose a NC-behavioral MADM (BMADM) method by considering decision maker (DM)’s risk attitudes and pre-evaluation information
Summary
M ULTI-ATTRIBUTE decision making (MADM) is a process of ranking alternatives or selecting the best alternative from several alternatives with respect to a set of attributes [1]. Besides the theoretical researches of normal cloud model, it has been used for solving many practical MADM problems, such as selection and evaluation of groundwater management schemes [7], sustainable supplier selection [13], and risk evaluation [14] These existing studies have played an important role in the development of rational decision theory with uncertainty. Different from traditional prediction approaches which are only based on the past data of one alternative, pre-evaluation refers to as an advance evaluation for the future performance of one alternative through identifying and analyzing potential favorable and negative factors of one alternative’s development according to some relevant basic materials collected and sorted [30] It is one requirement of successful decision-makings, that is because: (a) it offers more helpful and explicit information for DMs to make an evaluation and selection; (b) it is the embodiment of grasping the development law and essence of things, and helps DMs have a definite object in view; (c) the pre-evaluation information might provide DMs’ a reference for choosing a long-term cooperative partner, and helps to reduce the follow-up selection cost.
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