Date of Award

3-2025

Document Type

Thesis

Degree Name

Master of Science in Operations Research

Department

Department of Operational Sciences

First Advisor

Matthew A. Robbins, PhD

Abstract

This research formulates the medical evacuation (MEDEVAC) dispatching problem as a sequential decision process and investigates the application of reinforcement learning under nonstationary conditions. We model the dynamic arrival rate of MEDEVAC requests using a nonstationary Hawkes process and design a Double Deep Q-Network algorithm that incorporates belief states to anticipate future requests. Through computational experimentation, we analyze the impact of belief formulation on decision quality and system performance. Results indicate that policies incorporating belief states significantly outperform myopic dispatching policies, reducing urgent casualty wait times by up to 49.68% and increasing on-time evacuations by up to 21.91%.

AFIT Designator

AFIT-ENS-MS-25-M-187

Comments

An embargo was observed for this posting.

Approved for Public Release, Distribution Unlimited. PA case number 88ABW-2025-0306

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