Determining Weights in Multi-Criteria Decision Making Based on Negation of Probability Distribution under Uncertain Environment

Abstract

Multi-criteria decision making (MCDM) refers to the decision making in the limited or infinite set of conflicting schemes. At present, the general method is to obtain the weight coefficients of each scheme based on different criteria through the expert questionnaire survey, and then use the Dempster–Shafer Evidence Theory (D-S theory) to model all schemes into a complete identification framework to generate the corresponding basic probability assignment (BPA). The scheme with the highest belief value is then chosen. In the above process, using different methods to determine the weight coefficient will have different effects on the final selection of alternatives. To reduce the uncertainty caused by subjectively determining the weight coefficients of different criteria and further improve the level of multi-criteria decision-making, this paper combines negation of probability distribution with evidence theory and proposes a weights-determining method in MCDM based on negation of probability distribution. Through the quantitative evaluation of the fuzzy degree of the criterion, the uncertainty caused by human subjective factors is reduced, and the subjective error is corrected to a certain extent.

Green Supplier Selection Based on Green Practices Evaluated Using Fuzzy Approaches of TOPSIS and ELECTRE with a Case Study in a Chinese Internet Company

“…In the decision-making process of selecting green suppliers, it is necessary to focus on uncertain language and incomplete information environment issues. Sun et al [10] proposed a weights-determining method in MCDM based on negation of probability distribution. This method combines probability distribution negation with evidence theory to reduce the uncertainty caused by human subjective factors through quantitative evaluation of criterion ambiguity.…”

An AHPSort II Based Analysis of the Inequality Reduction within European Union

“…This proposal is based on a multi-criteria decision analysis, which deals with the process of taking decisions among a decision maker who evaluate different alternatives on several criteria [15]. Classically, most of MCDM approaches have been formulated for choice and ranking problems in which it is obtained the best alternative for the decision problem or a ranking of them [16][17][18][19][20]. Nevertheless, this contribution focuses on MCDM sorting problems, in which the alternatives are classified into several categories previously defined.…”

Refined Expected Value Decision Rules under Orthopair Fuzzy Environment

“…In the real world, there are many uncertainties and unreliabilities [1][2][3][4]. In order to deal with the uncertainties [5][6][7], many mathematical theories are proposed, such as Bayesian network [8], hyper structures [9], fuzzy sets (FS) [10][11][12], hesitant fuzzy subalgebras [13], D-S evidence theory [14][15][16], information quality [17,18], Z-number [19,20], D-number [21], entropy [22,23] and belief structure [24] and are applied in many fields [25][26][27]. Among these theories and models, the orthopair fuzzy set (OFS) [28][29][30] allows the membership degree, non-membership degree and hesitancy degree to be [0, 1] × [0, 1], which results in the orthopair fuzzy set generalization of the intuitionistic fuzzy set and Pythagorean fuzzy set and giving great freedom to the modelers of systems in order to capture human knowledge.…”