Solving the military medical evacuation dispatching, preemptive rerouting, redeploying, and delivering problem via tree-based machine learning and approximate dynamic programming approaches

Abstract

Military medical evacuation (MEDEVAC) authorities face the challenge of efficiently dispatching aeromedical units and evacuating casualties to appropriate medical treatment facilities (MTFs). We examine a military MEDEVAC scenario wherein authorities must dispatch, preemptively reroute, and redeploy units while considering where to evacuate (or deliver) casualties, accounting for the capabilities and capacities of the MTFs (i.e., the military MEDEVAC DPR-D problem). To solve the problem efficiently, we formulate a discounted, infinite-horizon Markov decision process (MDP) model and employ approximate dynamic programming (ADP) solution techniques that integrate tree-based value function approximation schemes within an approximate policy iteration (API) framework: Random Forest (API-RF) and Extreme Gradient Boosting (API-XGB). Using domain knowledge-based basis functions, we enhance the explainability of these approximation schemes. We construct a representative scenario of high-intensity operations in Bosnia-Herzegovina to demonstrate the applicability of our MDP model and compare the efficacies of our ADP solution techniques. The results show that API-RF and API-XGB significantly outperform the current benchmark myopic policy (i.e., assign the closest unit and MTF to the casualty location) across all 36 problem instances. Moreover, API-XGB consistently outperforms API-RF in 32 instances, achieving statistical significance at the 95 % confidence level for 21 of them. The explainability of these tree-based schemes highlights key features that influence DPR-D policies, such as the casualty queue length and the number of available MTF beds, whose importance shifts depending on the casualty arrival intensity. Our research offers valuable insights and potential modifications for future military MEDEVAC operations.