A Multistage Two‐Sided Matching Model in Interval‐Valued Triangular Fuzzy Environments

In two‐sided matching problems, accurately quantifying preference intensity using precise numerical values remains a significant challenge due to the inherent complexity and fuzziness of real‐world decision‐making. Moreover, existing research rarely considers the staged dynamics of matching processes across multiple time periods. To address these gaps, this study proposes a novel multistage dynamic two‐sided matching method based on interval‐valued triangular fuzzy sets. First, dynamic stage weights are determined by maximizing the comprehensive mean of optimal matching information at each period. Second, an uncertain dynamic interval triangular fuzzy weighted geometric (UDITFWG) operator is introduced to aggregate multistage preference information, where the geometric aggregation characteristic effectively captures the noncompensatory nature of evaluation criteria. Third, a one‐to‐one two‐sided matching optimization model is constructed with the objective of maximizing the overall comprehensive mean. A numerical example of venture capital investment matching demonstrates the feasibility and effectiveness of the proposed method. Sensitivity analysis reveals that both the intermediary’s risk preference and the decision power of matching parties significantly influence the optimal matching schemes. Comparative analysis shows that the UDITFWG operator yields more balanced matching results compared to arithmetic aggregation approaches, particularly when decision criteria exhibit “shortboard effects.” This research contributes to the theoretical development of two‐sided matching in fuzzy environments and offers practical implications for resource allocation in areas such as investment, human resources, and supply chain management.