A Hybrid Intuitionistic Fuzzy Entropy–BWM–WASPAS Approach for Supplier Selection in Shipbuilding Enterprises
Year: 2025
Type: article
Abstract: 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.
Source: Sustainability
DOI: 10.3390/su17041701
Authors: Qiankun Jiang, Haiyan Wang
Institution: Wuhan University of Technology
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4407678607