A Probabilistic Linguistic Multi-Criteria Optimization Approach: An Application on Cold Chain Supplier Selection for Perishable Goods

In complex multi-criteria decision-making scenarios, the inherent ambiguity of evaluation data and the frequent unavailability of complete attribute weight information pose significant challenges for domain experts. To address these methodological limitations, this study proposes a novel TOPSIS-based decision-making framework that integrates optimization algorithms with probabilistic linguistic term sets (PLTSs). Specifically, a distance measurement optimization model is constructed to objectively resolve the issue of incomplete attribute weight information. This mathematical approach enables the seamless fusion of qualitative expert judgments with quantitative metrics, effectively managing uncertainty and information deficiency in the decision-making process. To validate the practical viability and superiority of the proposed methodology, it is applied to an empirical case study of supplier selection in the cold chain logistics sector for fresh and perishable commodities. The evaluation encompasses three core dimensions: (i) environmental sustainability and energy efficiency, (ii) quality assurance and operational control, and (iii) supply chain collaboration and resilience. Empirical findings demonstrate that the proposed methodological framework substantially strengthens the robustness and reliability of selection outcomes under information-deficient conditions. Relative to conventional approaches, the developed framework demonstrates superior mathematical adaptability and effectively captures decision distortions, thereby offering rigorous theoretical contributions to decision-making under uncertainty and providing actionable practical guidance for complex supply chain evaluations.