Solving Strategic Military Workforce Planning Problems with Simulation-optimization

Abstract

The recent budget cuts have compelled armed forces to reduce their workforce size while enhancing their operational availability. These goals can be realized by implementing effective workforce management plans. To recruit, train, and retain skilled workforce are ongoing challenges for military workforce, with serious implications on force capability and national security. In this study, we focus on strategic military workforce planning, which has long-term impacts on both the efficiency and the future structure of the workforce. Strategic military workforce planning includes decisions regarding recruitment, attrition, promotion, training, and retention policies. It is usually difficult to anticipate how the system, over time, will respond to the combined effects of these strategies Thus, we develop a simulation-optimization approach by coupling a system dynamics (SD) simulation model and a genetic algorithm (GA) to find the best long-term workforce decision(s). GA seeks the optimal workforce flow between different ranks (including promotion strategies) while the SD model simulates the career progression of the workforce from recruitment, throughout the interim separation, and retirement. The numerical experiments show that the developed simulation optimization approach finds strategies yielding reduced costs and improved operational availability.