A MAGDM algorithm with multi-granular probabilistic linguistic information
type: Item
instance of: scholarly article
title: A MAGDM algorithm with multi-granular probabilistic linguistic information
zbMATH Open document ID: 1418.91182
author: Ju-Xiang Wang
published in: Symmetry
publication date: 2019-07-10T00:00:00Z
review text: Summary: The traditional multi-attribute group decision making (MAGDM) method needs to be improved to the integration of assessment information under multi-granular probabilistic linguistic environments. Some novel distance measures between two multi-granular probabilistic linguistic term sets (PLTSs) are proposed, and distance measures are proved to be reasonable. To calculate the weights of the alternative attributes, the extended cross-entropy method for multi-granular probabilistic linguistic term sets is proposed. Then, a novel extended MAGDM algorithm based on prospect theory (PT) is proposed. Two case studies of decision making (DM) on purchasing a car is provided to illustrate the application of the extended MAGDM algorithm. The case analyses are proposed to illustrate the novelty, feasibility, and application of the proposed MAGDM algorithm by comparing the other three algorithms based on TOPSIS, VIKOR, and \textit{Q. Pang} et al.'s method [``Probabilistic linguistic term sets in multi-attribute group decision making'', Inf. Sci. 369, 128--143 (2016; \url{doi:10.1016/j.ins.2016.06.021})]. The analyses results demonstrate that the proposed algorithm based on PT is superior.
Mathematics Subject Classification ID: 91B06
zbMATH DE Number: 7078095
zbMATH Keywords: decision making
zbMATH Keywords: distance measure
zbMATH Keywords: probabilistic linguistic term set
zbMATH Keywords: prospect theory
MaRDI profile type: Publication
full work available at URL: sym11020127
OpenAlex ID: W2911531266
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Wikidata QID: Q128547777
DOI: 10.3390/SYM11020127
description: scientific article; zbMATH DE number 7078095