TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Issue 3 (April 2016)

• Record Type: Journal Article
• Authors: Biswas, Pranab; Pramanik, Surapati; Giri, Bibhas
• Is Part Of: Neural computing & applications. Volume 27:Issue 3(2016)
• Page Start: 727
• Page End: 737
• Publication Date: 2016-04
• Subjects: Fuzzy set -- Intuitionistic fuzzy set -- Multi-attribute group decision-making -- Neutrosophic set -- Single-valued neutrosophic set -- TOPSIS; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1891-2
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract A single-valued neutrosophic set is a special case of neutrosophic set. It has been proposed as a generalization of crisp sets, fuzzy sets, and intuitionistic fuzzy sets in order to deal with incomplete information. In this paper, a new approach for multi-attribute group decision-making problems is proposed by extending the technique for order preference by similarity to ideal solution to single-valued neutrosophic environment. Ratings of alternative with respect to each attribute are considered as single-valued neutrosophic set that reflect the decision makers' opinion based on the provided information. Neutrosophic set characterized by three independent degrees namely truth-membership degree (T ), indeterminacy-membership degree (I ), and falsity-membership degree (F ) is more capable to catch up incomplete information. Single-valued neutrosophic set-based weighted averaging operator is used to aggregate all the individual decision maker's opinion into one common opinion for rating the importance of criteria and alternatives. Finally, an illustrative example is provided in order to demonstrate its applicability and effectiveness of the proposed approach.