NDLI: TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment

Content Provider	Springer Nature Link
Author	Biswas, Pranab Pramanik, Surapati Giri, Bibhas C.
Copyright Year	2015
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.
Starting Page	727
Ending Page	737
Page Count	11
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	27
Issue Number	3
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2015-04-08
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fuzzy set Intuitionistic fuzzy set Multi-attribute group decision-making Neutrosophic set Single-valued neutrosophic set TOPSIS Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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