Consensus of large-scale group decision making in social network: the minimum cost model based on robust optimization
type: Item
instance of: scholarly article
title: Consensus of large-scale group decision making in social network: the minimum cost model based on robust optimization
zbMATH Open document ID: 1479.91282
author: Yanling Lu
author: Enrique Herrera-Viedma
author: Yefan Han
author: Ye-Jun Xu
published in: Information Sciences
publication date: 2021-12-02T00:00:00Z
review text: This paper studies the large scale group decision making consensus over social networks concerning the minimum cost. The paper proposes an expert clustering in social networks to enhance the consensus reaching using robust optimization technique. A consensus index is used to access the opinion difference between individuals in the network. The unit adjustment cost is considered to be limited by an ellipsoidal set. The model optimizes the total compensation cost in different scenarios on the basis of robust optimization. It is shown that the consensus reaching process of offline large scale group decision making is reached. Some comparison numerical examples have been performed to illustrate the algorithm.
reviewed by: Yilun Shang
Mathematics Subject Classification ID: 91D30
Mathematics Subject Classification ID: 91B06
zbMATH DE Number: 7437604
zbMATH Keywords: large-scale group decision making
zbMATH Keywords: consensus model
zbMATH Keywords: social network
zbMATH Keywords: robust optimization
Wikidata QID: Q99239730
MaRDI profile type: Publication
full work available at URL: j.ins.2020.08.022
OpenAlex ID: W3082147753
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DOI: 10.1016/J.INS.2020.08.022
description: scientific article; zbMATH DE number 7437604