A novel decision-making system for offshore wind turbine selection integrating cubic bipolar fuzzy set with improved ELECTRE-I

Offshore wind turbine (OWT) selection is crucial for the economic viability and stability of offshore wind power projects. However, it is complicated by expert linguistic ambiguity and conflicting criteria, which introduce uncertainties. This paper proposes a novel multi-criteria decision-making (MCDM) system combining principal component analysis (PCA) and cubic bipolar fuzzy extension of improved ELECTRE-I (CBF-ELECTRE-I+). First, twelve secondary indicators are identified through literature review, and their weights are determined by PCA to reduce expert subjectivity. Next, expert evaluations of alternatives are quantified as CBF sets and then aggregated using the weighted geometric aggregation operator, thereby capturing incomplete expert information and integrating divergent opinions. Finally, the optimal scheme is determined by computing the significance of each OWT via CBF-ELECTRE-I+. Compared with the traditional CBF-ELECTRE-I, the proposed CBF-ELECTRE-I+ enhances ranking accuracy and simplifies the computational process. A case study demonstrates the effectiveness of the proposed system for OWT selection.