A bipolar fuzzy digraph-based integrated decision making model for sustainable underwater robot selection

Sustainable offshore operations increasingly rely on intelligent robotic systems, creating a critical need for robust and transparent methods to select underwater robots capable of performing safely and efficiently in harsh marine environments. This selection process is inherently complex due to uncertain data, conflicting performance criteria, and interdependencies among attributes. To address these challenges, this study develops an integrated multi-criteria decision-making framework that combines the digraph–matrix approach with the multi-attribute ideal–real comparative analysis model for ranking underwater robot alternatives. The proposed system captures directional relationships among evaluation criteria through digraph representations, while the bipolar fuzzy determinants model both supportive and opposing evidence embedded in expert judgments. A novel symmetry-aware ranking measure is defined and incorporated to ensure balanced and reliable assessment under positive and negative influences. The proposed framework is applied to an offshore oil-and-gas case study using expert-evaluated decision data for underwater robot selection. The obtained results demonstrate stable and interpretable rankings and show improved performance when compared with established methods such as multi-attribute ideal–real comparative analysis, digraph–matrix approach, additive ratio assessment, multi-attributive border approximation area comparison, technique for order preference by similarity to ideal solution, and VlseKriterijumska Optimizacija I Kompromisno Resenje. Furthermore, the model supports sustainability-oriented decision-making by identifying robots with improved energy efficiency, cost-effectiveness, and reduced environmental impact, aligning with Sustainable Development Goals 7, 9, 12, 13, and 14. Comparative experiments and sensitivity analyses across multiple parameter variations further confirm the robustness, consistency, and adaptability of the proposed decision-making framework.