A Novel TOPSIS based Consensus Technique for Multiattribute Group Decision Making

Chakraborty, Subrata and Mandal, Anuradha (2018) A Novel TOPSIS based Consensus Technique for Multiattribute Group Decision Making. In: 18th International Symposium on Communications and Information Technologies (ISCIT), 26-29 Sep 2018, Bangkok, Thailand.

Abstract

Consensus in a group decision setting is a challenging task. We propose a new group consensus technique utilizing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) based preference aggregation approach. The technique applies ranking outcome similarity based selection approach in order to select the most preferred consensus technique. We then present a numerical example for better illustrating the new developments along with simulation based comparative results with traditional approach. The new technique provides better overall decision similarity to the group members’ individual decisions and produces lower tied rankings.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 July 2013 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 July 2013 -)
Date Deposited: 04 Feb 2020 06:15
Last Modified: 11 Feb 2020 04:11
Uncontrolled Keywords: Group decision ,multiattribute decision making, consensus, TOPSIS, Additives, Decision making, Information and communication technology, Euclidean distance, Q measurement, Problem-solving
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080605 Decision Support and Group Support Systems
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1109/ISCIT.2018.8587952
URI: http://eprints.usq.edu.au/id/eprint/35360

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