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A Differential Evolution Algorithm for Military Workforce Planning Problems: A Simulation-Optimization Approach

Appropriate workforce planning in an organization can ensure maximum profitability. Better way of planning and scheduling is also very important to avoid unexpected expenditures and high employee turnovers. This paper proposes a simulation-optimization approach to predispose the best strategic decisions for a long-term workforce planning problem by taking into account the complex interactions among many components (e.g., recruitment, attrition, promotion, training, and retention) of workforce planning. We use a differential evolution (DE) algorithm as the optimization method to couple with a system dynamics (SD) simulation model. The set of all feasible workforce planning policies is developed by the SD simulation and then searched by the DE algorithm while the fitness evaluation (i.e., total cost) of policies is evaluated by the simulation model. We demonstrate the approach through numerical experiments on a military workforce planning problem to provide insight into how the different strategies affect the overall system performance with regards to both total cost and fleet availability.