An integrated and discriminative approach for group decision-making with probabilistic linguistic information
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METHODOLOGIES AND APPLICATION
An integrated and discriminative approach for group decision-making with probabilistic linguistic information R. Krishankumar1 • Pratibha Rani2 • K. S. Ravichandran1
•
Manish Aggarwal3 • Xindong Peng4
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Group decision-making (GDM) is a complex process. The diversity, discrimination, and inevitable uncertainty due to human intervention characterize such problems that add to this complexity. To circumvent this challenge, there is an urge for an appropriate knowledge representation and decision-making approaches. The present paper is concerned with a prescriptive approach to GDM that can aid a group of decision-makers (DMs) to arrive at a decision. To this end, the recent concept of probabilistic linguistic term set is utilized. The discrimination among the alternatives, as in the real world, are mimicked using an integrated framework that adopts CRITIC and variance methods for attribute weight calculation, Gini index for calculating the weights of DMs, Maclaurin symmetric mean for aggregating preferences, and weighted distancebased approximation for prioritization of alternatives. A real-world problem on electric bike selection illustrates the usefulness of the proposed work. Finally, comparative analysis with extant methods demonstrates the technical results, and it is inferred that the proposed work is (i) highly consistent (from Spearman correlation) and (ii) produces broad rank values (from standard deviation) that could be efficiently discriminated for rational decision-making and backup management during critical situations. Keywords CRITIC method Discriminative weights Gini index Group decision-making Maclaurin symmetric mean
1 Introduction Communicated by V. Loia.
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00500-020-05361-1) contains supplementary material, which is available to authorized users. & K. S. Ravichandran [email protected]; [email protected] R. Krishankumar [email protected] Manish Aggarwal [email protected] Xindong Peng [email protected] 1
School of Computing, SASTRA University, Thanjavur, TN 613401, India
2
Department of Mathematics, National Institute of Technology, Warangal, India
3
School of Management & Entrepreneurship, IIT – Jodhpur, Jodhpur, 342001, Rajasthan, India
4
School of Information Science and Engineering, Shaoguan University, Shaoguan 512005, China
Multi-attribute group decision-making (MAGDM) is a complex problem that involves competing alternatives, each of which is described by a set of attributes (Sanayei et al. 2010). It is hard to find an alternative with a combination of the best attributes and hence arises the need to compromise on selecting the best alternative in the given choice set of alternatives. The compromise is specific to an individual. In the case of group decision-making, since a group of individuals are involved as decisi
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