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

Area of Science:

• Operations Research
• Decision Science
• Supply Chain Management

Background:

• Improvisational emergency supplier selection (IESS) is a complex multi-attribute decision-making (MADM) problem due to inherent uncertainty and urgency.
• Existing IESS methods struggle with subjective expert weights, uncertain opinions, and high computational costs, limiting real-world application.

Purpose of the Study:

• To introduce a novel Robust Consensus Ordinal Priority Approach (OPA-RC) for IESS.
• To overcome limitations of existing methods by providing objective weight derivation and robust optimization.

Main Methods:

• OPA-RC objectively derives expert importance scores using pairwise ranking concordance.
• A robust optimization framework incorporates these scores via a budget-based ambiguity set to handle score ambiguity.
• The model is reformulated as a computationally efficient linear program with a closed-form solution.

Main Results:

• OPA-RC demonstrates effectiveness through scenario-based experiments, including result, sensitivity, and comparison analyses.
• The approach objectively derives expert importance, mitigating subjectivity.
• The method provides a computationally efficient and robust decision-support tool.

Conclusions:

• OPA-RC offers a transparent, robust, and computationally efficient solution for IESS.
• The approach enables reliable and resilient supplier selection in critical disaster scenarios.
• This method enhances decision-making under uncertainty in emergency logistics.