Uncertainty is rampant in military expeditionary operations spanning high intensity combat to humanitarian operations. These missions require rapid planning and decision-support tools to address the logistical challenges involved in providing support in often austere environments. The U.S. Army's adoption of an enterprise resource planning (ERP) system provides an opportunity to develop automated decision-support tools and other analytical models designed to take advantage of newly available logistical data. This research presents a tool that runs in near-real time to assess risk while conducting capacity planning and performance analysis designed for inclusion in a suite of applications dubbed the Military Logistics Network Planning System (MLNPS) which previously only evaluated the mean sample path. Logistical data from combat operations during Operation Iraqi Freedom (OIF) drives supply requisition forecasts for a contingency scenario in a similar geographic environment. A nonstationary queueing network model is linked with a heuristics logistics scheduling methodology to provide a stochastic framework to account for uncertainty and assess risk.

A case study demonstrates the capabilities of the MLNPS for analyzing the logistics requirements of an expeditionary operation. In the case study, an Infantry Brigade Combat Team (IBCT) and a Stryker Brigade Combat Team (SBCT) conduct operations from South Sudan into Sudan against the Islamic State of Iraq and Syria (ISIS). Particular focus is placed on the requirements for the last tactical mile (LTM) trucks. New graphical and risk-analysis tools support (i) comparing alternative courses of action, and (ii) evaluating the performance of a selected course of action under a complete disruption of the sustainment vehicles.