Robust consensus ordinal priority approach for improvisational emergency supplier selection under expert consensus ambiguity

Emergencies are inherently uncertain, urgent, and complex, rendering improvisational emergency supplier selection (IESS) a demanding multi-attribute decision-making (MADM) problem. Existing methods are constrained by subjective or opaque expert weight assignments, inadequate handling of conflicting or uncertain opinions, and high computational demands that limit their practicality in time-sensitive contexts. To address these limitations, this study introduces a Robust Consensus Ordinal Priority Approach (OPA-RC) for IESS. The method derives nominal expert importance scores objectively through a consensus-building mechanism based on pairwise ranking concordance among experts. These scores are incorporated into a robust optimization framework via a budget-based ambiguity set, explicitly capturing the ambiguity in the nominal expert importance scores rather than relying on potentially subjective pre-assignment. The proposed model is then reformulated as a tractable linear program with a closed-form solution, combining computational efficiency with decision robustness. A scenario-based experiment is conducted to verify the effectiveness of OPA-RC, including result analysis, sensitivity analysis, and comparison analysis. The findings indicate that OPA-RC provides a transparent, robust, and computationally efficient decision-support tool, enabling reliable and resilient supplier selection under extreme disaster conditions.