Large-scale group decision making model based on social network analysis: Trust relationship-based conflict detection and elimination

• Published in: European journal of operational research Vol. 275; no. 2; pp. 737 - 754
• Main Authors: Liu, Bingsheng, Zhou, Qi, Ding, Ru-Xi, Palomares, Iván, Herrera, Francisco
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2019
• Subjects: Conflict detection and elimination; Decision processes; Large-scale group decision making; Social network analysis; Trust propagation operator; Trust propagation operator; Conflict detection and elimination; Social network analysis; Decision processes; Large-scale group decision making
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2018.11.075

•An SNA based conflict detection and elimination decision making process is presented.•The impact of relationship strength on trust propagation efficiency is considered.•Multi-path trust propagation operator is presented to complete the social network.•Nonlinear optimization model guarantees a sufficient reduction of group conflict.•We promote the modification of the assessments by finding the optimal solution. The paper proposes a Trust Relationship-based Conflict Detection and Elimination decision making (TR-CDE) model, applicable for Large-scale Group Decision Making (LSGDM) problems in social network contexts. The TR-CDE model comprises three processes: a trust propagation process; a conflict detection and elimination process; and a selection process. In the first process, we propose a new relationship strength-based trust propagation operator, which allows to construct a complete social network by considering the impact of relationship strength on propagation efficiency. In the second process, we define the concept of conflict degree and quantify the collective conflict degree by combining the assessment information and trust relationships among decision makers in the large group. We use social network analysis and a nonlinear optimization model to detect and eliminate conflicts among decision makers. By finding the optimal solution to the proposed nonlinear optimization model, we promote the modification of the assessments from the DM who exhibits the highest degree of conflict in the process, as well as guaranteeing that a sufficient reduction of the group conflict degree is achieved. In the third and last process, we propose a new selection method for LSGDM that determines decision makers’ weights based on their conflict degree. A numerical example and a practical scenario are implemented to show the feasibility of the proposed TR-CDE model.