An advanced hybrid approach for emergency healthcare pickup and delivery with unmanned aerial vehicles under a stochastic environment

This paper proposes an advanced hybrid approach for optimizing the pickup and delivery problem using unmanned aerial vehicles (UAVs) in emergency healthcare operations. We specifically account for scenarios involving stochastic demand and flying environments that may arise simultaneously during the distribution of healthcare resources and the collection of biological samples. We address the challenges of emergency logistics, such as inventory shortages, urgent and unpredictable demand, suddenness, and stochastic geographical obstacles. A mixed-integer linear programming model for the healthcare pickup and delivery with UAVs (HPDUP) is first formulated, aiming at maximizing the total weighted coverage from healthcare demands of patient groups. An extended model for HPDUP under stochastic environment (HPDU-SEP) is then developed to manage the uncertainty in demand and traveled distance. An adaptive large neighborhood search (ALNS) integrated with Q-learning for UAV trajectory planning (ALNS-QLTP) is proposed, where Q learning receives geographical information and feedback distance parameters to the optimization model. Compared with static or semi-dynamic methods, Q-learning achieves higher trajectory optimization efficiency in large-scale uncertain environments by utilizing offline training and scenario updates. ALNS-QLTP exhibits a strong performance on HPDU-SEP instances, guaranteeing 71.70% and 92.47% patient coverage with a limited and sufficient number of UAVs, respectively.