A dynamic framework of multi-attribute decision making under Pythagorean fuzzy environment by using Dempster–Shafer theory. (May 2021)

• Record Type: Journal Article
• Authors: Fei, Liguo; Feng, Yuqiang
• Is Part Of: Engineering applications of artificial intelligence. Volume 101(2021)
• Publication Date: 2021-05
• Subjects: Dynamic multi-attribute decision making -- Dempster–Shafer theory -- Pythagorean fuzzy set -- Credibility decay model -- Attribute weight -- Period weight; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104213
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper presents a novel framework for dynamic multi-attribute decision making (MADM) in Pythagorean fuzzy environments based on Dempster–Shafer theory (DST). The credibility of decision information decreases with time, so it is significant to capture the dynamic credibility of decision information in the temporal dimension. The proposed credibility decay model discounts decision information based on the credibility decay factor to obtain the basic probability assignment (BPA) vector corresponding to individual periods. Weight determination is an important aspect of dynamic MADM. We propose weighting the attributes in-period and in each period via subjective approach (evidential best–worst method) and objective approach (entropy weight method), respectively. We determine the period weight from the dual dimensions of information and consistency. The dynamic MADM is completed by combining the decision information of all attributes in each period, fusing the decision information of different periods, and calculating the ranking index of each alternative. The proposed dynamic decision framework is validated by a case study.