A parsimonious q-rung orthopair fuzzy decision-making method for ship design concept evaluation

Early-stage ship design concept evaluation involves multiple conflicting criteria, uncertain expert judgments, and an increasing number of candidate schemes generated by modular design and Artificial Intelligence Generated Content (AIGC)-enabled workflows. However, existing Multi-Criteria Group Decision-Making (MCGDM) studies in ship and marine engineering have mainly emphasized weighting and ranking procedures, while paying less attention to the systematic construction of hierarchical evaluation attribute sets and to parsimonious weighting under richer uncertainty representation. To address these gaps, this study proposes a hybrid Parsimonious q-Rung Orthopair Fuzzy Set Hierarchical Multi-Criteria Decision-Making (P-q-ROFS-HMCDM) framework for ship design concept evaluation. The framework consists of three stages: (1) Large Language Model (LLM)-assisted attribute expansion, Decision-Making Trial and Evaluation Laboratory (DEMATEL)-based convergence, and K-Means-based hierarchical structuring for building a systematic evaluation attribute set; (2) hierarchical weight determination using the Parsimonious q-Rung Orthopair Fuzzy Set Analytic Hierarchy Process (P-q-ROFS-AHP) method to reduce expert comparison burden while preserving uncertainty modeling capability; and (3) alternative ranking using an improved q-Rung Orthopair Fuzzy Set Measurement Alternatives and Ranking according to Compromise Solution (q-ROFS-MARCOS) method with a new score function. The proposed framework is demonstrated through a case study of a 42-inch fishing ship design, involving qualitative and quantitative criteria as well as uncertain group judgments. Sensitivity and comparative analyses show that the obtained rankings remain stable under different parameter settings and are generally consistent with other q-rung orthopair fuzzy set-based methods. These results demonstrate the practical feasibility and robustness of the proposed framework and suggest its potential as a scalable decision support tool for early-stage ship design evaluation.