Optimization case study: ISR allocation in the Global Force Management process

Abstract

Global Force Management (GFM) is a force-allocation process-driven system that distributes military forces across the globe to meet Combatant Commander objectives. The goal is to match military capabilities provided by the military services to Geographic Combatant Commander requirements. This thesis is a proof of concept for an optimization model that maximizes the distribution of a finite number of full motion video intelligence, surveillance, and reconnaissance (ISR) assets to a prioritized list of requirements to meet national security objectives. This thesis examines the ISR GFM process. With the insight gained to the process, the model applies a mixed integer linear programming formulation to provide an optimized force allocation recommendation The model's objective function managed the trade-off between FADM priority and platform consideration, which optimized the allocation 902 hours per day of full motion video to meet 1902 hours per day of 20 CCDR requirements. The research, methodology, and analyses presented in this thesis is a successful proof of concept proving that this optimization model will objectively inform senior decision makers in the Department of Defense for intelligence surveillance reconnaissance Global Force Management allocation.