The impact of COVID-19 on the global outbreak of supply chain is enormous. It is crucial for governments to take policy recommendations to enhance the supply chain resilience to mitigate the negative impact of COVID-19. For such a major issue, it is a common occurrence that a large number of decision-makers (DMs) are invited to participate in the decision-making process so as to ensure the comprehensiveness and reliability of decision results. Since the attitudinal characteristics of DMs are important factors affecting decision results, this study focuses on capturing the attitudinal characteristics of DMs in the large-scale group decision making process. The capturing process combines the ordinal k-means clustering algorithm, gained and lost dominance score method and personalized quantifiers. To enable DMs to express their cognitions in depth, we use the probabilistic linguistic term set to express the evaluation information of DMs. A case study on selecting the optimal policy recommendation for improving the integration capability of supply chain is given to illustrate the applicability of the proposed process. The superiority of the proposed algorithm is highlighted through sensitive analysis and comparative analysis.

COVID-19对全球供应链爆发的影响是巨大的。各国政府采取政策建议以增强供应链弹性以减轻COVID-19的负面影响至关重要。对于这样一个重大问题，通常会邀请许多决策者（DM）参与决策过程，以确保决策结果的全面性和可靠性。由于决策者的态度特征是影响决策结果的重要因素，本研究着重于在大规模群体决策过程中捕获决策者的态度特征。捕获过程结合了有序的k均值聚类算法，获得和失去的优势评分方法以及个性化量词。为了使DM能够深入表达自己的认知，我们使用概率语言术语集来表达DM的评估信息。通过案例研究选择最佳的政策建议以提高供应链的整合能力，以说明所提出流程的适用性。通过敏感性分析和比较分析突出了所提算法的优越性。