Risk-Averse Two-Stage Stochastic Minimum Cost Consensus Models with Asymmetric Adjustment Cost

• Published in: Group decision and negotiation Vol. 31; no. 2; pp. 261 - 291
• Main Authors: Ji, Ying, Li, Huanhuan, Zhang, Huijie
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.04.2022; Springer Nature B.V
• Subjects: Adjustment; Algorithms; Asymmetry; Biological and Physical Anthropology; Business and Management; Cash flow; Consensus building; Coronaviruses; Decision making; Fuzzy sets; Group decision making; Interest rates; Peer to peer computing; Pneumonia; Risk assessment; Minimum cost consensus model; Directional constraints; Two-stage mean-risk stochastic programming; L-shaped algorithm
• ISSN: 0926-2644; 1572-9907
• DOI: 10.1007/s10726-021-09752-z

In the process of reaching consensus, it is necessary to coordinate different views to form a general group opinion. However, there are many uncertain factors in this process, which has brought different degrees of influence in group decision-making. Besides, these uncertain elements bring the risk of loss to the whole process of consensus building. Currently available models not account for these two aspects. To deal with these issues, three different modeling methods for constructing the two-stage mean-risk stochastic minimum cost consensus models (MCCMs) with asymmetric adjustment cost are investigated. Due to the complexity of the resulting models, the L-shaped algorithm is applied to achieve an optimal solution. In addition, a numerical example of a peer-to-peer online lending platform demonstrated the utility of the proposed modeling approach. To verify the result obtained by the L-shaped algorithm, it is compared with the CPLEX solver. Moreover, the comparison results show the accuracy and efficiency of the given method. Sensitivity analyses are undertaken to assess the impact of risk on results. And in the presence of asymmetric cost, the comparisons between the new proposed risk-averse MCCMs and the two-stage stochastic MCCMs and robust consensus models are also given.