In the present fast-paced and globalized age of war, special operations forces have a comparative advantage over conventional forces because of their small, highly-skilled…
In the present fast-paced and globalized age of war, special operations forces have a comparative advantage over conventional forces because of their small, highly-skilled units. Largely because of these characteristics, special operations forces spend a disproportionate amount of time deployed. The amount of time spent deployed affects service member’s quality of life and their level of preparedness for the full spectrum of military operations. In this paper, the authors ask the following question: How many force packages are required to sustain a deployed force package, while maintaining predetermined combat-readiness and quality-of-life standards?
The authors begin by developing standardized deployment-to-dwell metrics to assess the effects of deployments on service members’ quality of life and combat readiness. Next, they model deployment cycles using continuous time Markov chains and derive closed-form equations that relate the amount of time spent deployed versus at home station, rotation length, transition time and the total force size.
The expressions yield the total force size required to sustain a deployed capability.
Finally, the authors apply the method to the US Air Force Special Operations Command. This research has important implications for the force-structure logistics of any military force.
Army and Joint Transformation initiatives in U.S. national defense (Shinseki, 2000) underscore the need to plan and meet mission requirements for individual soldier and small unit deployment in “close fight” scenarios (e.g. close combat, direct fire, complex terrain). This has focused interest and attention on the need for improved individual human performance research data, models, and high-fidelity simulations that can accurately represent human behavior in individual and small unit settings. New strategies are now needed to bridge the gap between performance outcome assessment and prediction (see also Pew & Mavor, 1998). The purpose of this chapter is to address epistemological and methodological issues that are fundamentally relevant to this goal.