A Data-Driven Method for Supporting Self-Adapt Large-Scale Group Decision-Making: A Case Study on Resilient Design of Firm’s Product

Large-scale group decision-making (LSGDM) has emerged as a prominent research area in various domains, such as high technology and complex engineering problems. The advent of machine learning techniques has revolutionized LSGDM by introducing new data-driven approaches. First, recurrent neural networks (RNNs) have been proposed as a data-driven method to effectively learn and predict experts’ preferences. Second, a self-adaptive method has been devised to optimize clustering parameters, considering their influence. The consensus-reaching process facilitates the reverse optimization of these parameters. Third, a novel approach called analysis target cascading (ATC) has been suggested to address the limitations of traditional weighing methods used in previous LSGDM studies. ATC comprehensively investigates the potential game among multiple subgroups, thereby resolving the consensus-reaching problem (CRP). Lastly, an improved artificial bee colony algorithm has been proposed to tackle the optimization problem presented in this study. This enhanced algorithm incorporates the levying mechanism and searching method from the gravity search algorithm. To validate the efficacy of the proposed methods, a case study involving a large-scale interdisciplinary team has been conducted.