Date of Award
Master of Science
Department of Operational Sciences
Phillip R. Jenkins, PhD
The military medical evacuation (MEDEVAC) dispatching problem seeks to determine high-quality dispatching policies to maximize the survivability of casualties within contingency operations. This research leverages applied operations research and machine learning techniques to solve the MEDEVAC dispatching problem and evaluate system performance. More specifically, we develop an infinite-horizon, continuous-time Markov decision process (MDP) model and approximate dynamic programming (ADP) solution approach to generate high-quality policies. The ADP solution approach utilizes an approximate value iteration algorithm strategy incorporating gradient descent Q-learning to approximate the value function. A notional, synthetically-generated scenario in Africa based around the capital city of Niger, Niamey is developed and utilized to compare the ADP-generated policies with the closest-available dispatching (i.e., myopic policy) currently employed by military medical planners. This research also develops a custom OpenAI gym environment in Python to evaluate system performance and the efficacy of the ADP solution approach. Initial results from our computational experiments indicate a 10.2% increase in performance over the myopic policy. Further testing indicates which problem features have the most significant impact on the system performance gap between the myopic policy and the ADP-generated policies. The model, methodologies, and results from this research may be utilized to advise current and future military medical planning procedures, operations, and tactics.
DTIC Accession Number
Gelbard, Andrew G., "Incorporating Armed Escorts to the Military Medical Evacuation Dispatching Problem via Stochastic Optimization and Reinforcement Learning" (2022). Theses and Dissertations. 5341.