Performance Assessment of Shipbuilding Supply Chains Based on Hybrid Weighted Intuitionistic Fuzzy TOPSIS

The shipbuilding industry is characterized by long manufacturing periods, substantial investments, and high input costs, which introduce significant uncertainty and risk into its supply chains. To address these challenges, this paper proposes an enhanced Intuitionistic Fuzzy TOPSIS model tailored for evaluating supply chain performance in the shipbuilding sector. A comprehensive performance evaluation system was constructed based on survey data and expert interviews. Decision-maker weights were assigned according to their professional experience, educational background, and job titles. To mitigate the ambiguity and subjectivity inherent in performance assessment indicators, expert opinions were aggregated using the intuitionistic fuzzy weighted arithmetic mean operator to generate the decision matrix. The accuracy of the performance evaluation was enhanced by integrating both subjective and objective weights. Subjective weights were derived using the mean-variance decision-making approach to determine the subjective weights of the performance evaluation indicators. Objective Weights were calculated using a normal distribution-based method to establish the objective weights of these indicators. The final rankings were determined by calculating the closeness coefficient between each alternative and the positive ideal solution. The model's effectiveness and reliability were validated through a case study conducted in a Chinese shipbuilding company, with empirical results demonstrating its robustness and applicability.