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The exponential rise in the global population, combined with limited food resources, poses a critical challenge to feeding more than 9 billion people. Furthermore, the increasing reliance of people on perishable and refrigerated products has proven that the cold supply chain (CSC) has inherent value in ensuring food quality and global food security. The cold supply chain (CSC) is crucial for maintaining food quality and ensuring global food security, yet it faces significant challenges that lead to quality deterioration and food loss. Therefore, a strategic decision support system is necessary to alleviate the severity of these obstacles and ensure the food quality in the CSC. This study aims to facilitate a novel two-stage decision support model that helps decision-makers identify and prioritize the most critical obstacles affecting CSC performance, and propose effective solutions to overcome these obstacles and ensure the food quality. The study integrates a novel Analytic Hierarchy Process (AHP) and a Multiplicative Multi-Objective Optimization based on Ratio Analysis (MULTIMOORA)-based hybrid MCDM decision-making methodology to establish the practical foundation of the model under interval-valued Pythagorean fuzzy (IVPF) set theory. The IVPF-AHP method is utilized to visualize the severity of the obstacles, while the IVPF-MULTIMOORA analyzes the importance and priorities of the alternatives. The reliability and robustness of the results through the IVPF-based AHP-MULTIMOORA methodology have been supported by conducting a total of thirty-one experiments under different sets of input parameters.
Kumar et al. (Thu,) studied this question.