A Reputation-Enhanced Shapley–FAHP Method for Multi-Dimensional Food Safety Evaluation

Abstract

Ensuring food safety in complex supply chains requires evaluation frameworks that integrate multiple indicators, account for their interdependencies, and incorporate historical performance. This study proposes a novel RM–Shapley–FAHP framework that combines the Fuzzy Analytic Hierarchy Process, Shapley value contribution analysis, and a reputation decay mechanism to construct a dynamic, multi-year assessment model. The framework evaluates six governance subsystems, mitigates indicator redundancy, and links past performance to current risk posture. Applied to a leading food enterprise over three years, the method demonstrated superior consistency, interpretability, and operational relevance compared to FAHP, entropy weighting, and equal-weight baselines. The results demonstrate that RM–Shapley–FAHP framework effectively supports balanced development in food safety governance by capturing temporal dynamics and interdependencies, offering interpretable and operationally relevant guidance for decision makers. In future work, this framework may be extended with machine learning to improve adaptability for multi-dimensional and time-series evaluations, noted here as a research prospect rather than a present contribution.