Supplier selection in the shipbuilding industry is a typical multicriteria group decision-making (MCGDM) problem, often characterized by significant uncertainty and fuzziness. To address this issue effectively, this paper proposes a novel integrated approach for supplier selection in shipbuilding enterprises by combining intuitionistic fuzzy sets (IFSs) with the weighted aggregated sum product assessment (WASPAS) method. The proposed method utilizes IFS operators alongside an innovative process for evaluating indicator weights. Initially, an intuitionistic fuzzy number approach is employed to obtain indicator data, which effectively captures the uncertainty of linguistic variables and ensures accurate reflection of real-world conditions. Subsequently, the indicator weights are evaluated by integrating subjective weights, derived through the best–worst method, with objective weights, calculated using an entropy-based approach, resulting in more balanced and realistic weight assignments. Subsequently, the WASPAS method is used to prioritize alternative suppliers, and a shipbuilding enterprise in Shanghai is taken as an example to verify the effectiveness of the model. In addition, to evaluate the stability of the proposed method, sensitivity analyses were performed for varying attribute values. The results demonstrate that the combination of subjective and objective weights enhances the stability of the method under varying attribute weights. Finally, a comparison with various existing methods based on intuitionistic fuzzy information proves that the proposed method exhibits certain advantages in solving the MCGDM problem under uncertain environments.

Improving efficiency, reducing expenses, and ensuring sustainability in today’s competitive market is made possible by optimising the performance of the food supply chain (FSC). A strong decision-making framework is required to overcome obstacles including high transportation and production costs, uneven quality, and the impact on the environment. To solve these problems, this paper presents an all-inclusive multi-criteria decision making (MCDM) framework that uses enhanced relative utility and nonlinear standardisation (ERUNS) to rank alternatives, Logarithmic percentage change-driven objective weighing (LOPCOW) to determine objective weights, and subjective weight assignment by ratio analysis (SWARA) to determine subjective criterion weights. LOPCOW offers objective weights grounded in facts and indifference thresholds, whereas SWARA records decision-makers (DMs) preferences in weighing factors like sustainability and cost efficiency. The ERUNS methodology then uses utility degree assessments and matrix standardization to rank and evaluate alternatives. An improved method of assessment is to make use of intuitionistic fuzzy sets (IFS), which deal with ambiguity by adding membership, non-membership, and hesitation levels. By merging subjective and objective data, this integrated method maximizes FSC performance, provides useful insights for enhancing overall efficiency and sustainability, and offers a viable answer to contemporary difficulties. It also offers helpful insights for improving overall efficiency and sustainability, and it provides a practical response to the challenges that are now being faced.