A parallel approach with the strategy-proof mechanism for large-scale group decision making: An application in industrial internet

Abstract

The consensus-reaching process (CRP) is essential for forming a solution in large-scale group decision-making (LSGDM). We designed a parallel method with a strategy-proof mechanism to support CRP in the LSGDM. First, considering the previous clustering methods’ poor performance in non-convex datasets, a density-based clustering method (DBCM) is proposed. Because the parameters of DBCM influence the performance of clustering, they are optimized based on the CRP. Second, after clustering, the analytical target cascading (ATC) method is proposed to support CRP. For ATC, we set the moderator as the first layer and subgroups as the second layer. Each subgroup connects only to the moderator and realizes the consensus separately. The final consensus is realized when the difference among the subgroups’ alternatives is lower than a threshold value. The ATC method supports the high-efficiency, independent, and distributed CRP in LSGDM, which is feasible in new situations prompted by COVID-19, like telecommuting, shared manufacturing, cloud-based medical treatment, and distributed designing. To enhance the efficiency of CRP, we propose a preference learning method based on big data. Third, a strategy-proof mechanism is proposed to prevent manipulation in LSGDM, which indicates that the expert’s profit is higher than that of the expert with manipulation, regardless of the manipulating possibility of experts. The mechanism avoids the loss caused by experts’ manipulation in the CRP. Finally, we design an enhanced gray wolf algorithm to solve the optimization problem. The advantages of the proposed method are verified by the slewing bearing design in the industrial internet.