A Multi-Objective Decision-Support Framework for Sustainable and Resilient Mid- to Long-Range UAV Cargo Supply Chains Under Demand and Climatic Uncertainty

The increasing integration of unmanned aerial vehicles (UAVs) into logistics systems introduces new strategic network design challenges, particularly for mid- to long-range cargo operations, where infrastructure availability, uncertain demand, and climatic conditions jointly affect operational performance, resilience, and sustainability. Unlike small-scale UAVs, cargo UAVs carrying relatively heavy payloads require airport infrastructure for takeoff and landing, making infrastructure availability a critical consideration in long-term supply chain network planning. This study proposes a multi-objective decision-support framework for designing sustainable and resilient UAV cargo supply chains under demand and climatic uncertainty. A mixed-integer programming model simultaneously optimizes four conflicting objectives representing infrastructure utilization, transportation efficiency, operational reliability, and economic performance. A prioritized optimization algorithm generates Pareto-efficient solutions according to decision-maker preferences. Truncated normal distributions are employed to model uncertainty in demand and unairworthy days, while a robustness analysis based on 50 independent runs confirms the stability of the proposed framework. The results demonstrate that the framework supports sustainable supply chain planning through efficient infrastructure utilization, reduced transportation requirements and logistics costs, and enhanced resilience under uncertain operating conditions. Overall, the proposed approach provides practical decision support for long-term strategic planning of efficient, sustainable, and resilient UAV cargo supply chains.