Asynchronous Consensus Evolution Mechanism for Large Group Emergency Decision Making: Risk Mitigation Strategy Selection Under Uncertainty
Ya-Jing Zhou, Mi Zhou, Jian Wu, Witold Pedrycz, Xin-Bao Liu
Abstract
Supply chain disruptions pose substantial risks to the system-on-chip supply chain (SoCSC) within the electric vehicle (EV) industry, potentially resulting in production delays and financial losses. This study proposes a novel asynchronous consensus evolution mechanism (ACEM) designed to enhance large group emergency decision-making (LGEDM) under uncertainty, with specific application to the EV SoCSC. Unlike traditional synchronous approaches, ACEM enables decision makers (DMs) to contribute asynchronously, reducing wait times and accelerating consensus formation. The mechanism integrates uncertain scenario analysis with an optimization framework that dynamically allocates decision steps with relative weights, ensuring adaptability to complex and dynamic environments. We further develop a time-aware adaptive clustering (TAAC) algorithm to segment DMs based on decision quality and response speed, enhancing both the speed and the accuracy of consensus building. Simulation results indicate that ACEM significantly reduces decision latency and improves consensus efficiency under uncertain disruption scenarios. This work provides a robust framework for agile decision-making, enabling manufacturers to enhance SoCSC resilience in uncertain disruptions.