An Agentic Digital Twin Framework for Fuzzy Multi-Objective Optimization in Dynamic Humanitarian Logistics

Humanitarian logistics faces challenges such as conflicting objectives, severe uncertainty, temporal dynamics, and the need for interpretable decisions. This research presents an integrated decision-making framework that simultaneously considers fuzzy uncertainty, system dynamics, and adaptive decision logic. Operational uncertainties are modeled using triangular fuzzy numbers and a dynamic representation of the system allows for continuous updating of decisions over time. Computational results based on simulated data show that the proposed framework is capable of generating stable, diverse, and interpretable solutions. An improvement in the average quality of the Pareto front of more than 5% and a reduction in the distance from the reference front of about 30% are observed compared to non-adaptive approaches. Also, stability and dynamic behavior analyses show that the decisions are robust to changing environmental conditions and parameters and have high adaptability. These features make the proposed framework a reliable tool for decision support in relief operations.