Logistics in the Line of Fire: A Stochastic Programming Model for Contested Environments

Abstract

As military doctrine evolves to address the challenges of large-scale combat operations (LSCO), logistics planning must adapt to ensure the continuous sustainment of dispersed and contested forces. This thesis presents a two-stage stochastic mixed-integer programming (MIP) model designed to optimize military resupply under uncertainty. The formulation incorporates key doctrinal priorities, such as predictive logistics, prepositioned supply, and distribution network resilience, by explicitly modeling disruptions to supply routes and storage nodes. Using scenario data created from the Russo-Ukrainian War, the model evaluates the impact of adversarial attacks, storage costs, and vehicle availability on overall logistical performance. The results provide quantitative insight into the tradeoffs between transportation and storage-based supply strategies, revealing nonlinear thresholds that influence the model's behavior. The proposed framework offers a rigorous, extensible tool for analyzing logistics strategies in adversarial environments and contributes to the broader effort to formalize military logistics planning using mathematical modeling and data-driven decision making.