A multi-objective simulation–optimization for a joint problem of strategic facility location, workforce planning, and capacity allocation: A case study in the Royal Australian Navy

Abstract

The availability of assets crucially depends on the interplay between the facility locations, workforce planning, and capacity allocation. Therefore, in this paper, we model and solve a novel multi-objective optimization problem for strategic facility location, workforce planning, and capacity allocation in the context of the military. The developed model deviates from more common approaches and adapts a dynamic capacity scheme for both the workforce and facilities to capture the dynamic nature of future demand, such as maintenance requirements for assets. The capacity allocation and workforce planning problem focus on mainly strategic decisions involving from workforce and facilities. A system dynamics (SD) simulation model enables a decision-maker to analyze the effects of decision variables by modeling the system complexities, uncertainties, and interactions between facilities, workforce, and assets. However, the simulation model neither suggests nor seeks the best solution strategy or strategies. To overcome this shortcoming, we propose a simulation–optimization approach that uses a Non-dominated Sorting Genetic Algorithm-II (NSGA-II). NSGA-II generates feasible solution strategies (candidate solutions) such as time and amount of capacity expansion and downsizing for both the workforce and facilities as well as the amount of crew recruitment. These solution strategies are fed to the simulation model where it evaluates the fitness of candidate solutions. We test the applicability of the proposed method on a realistic case study from the Royal Australian Navy. Finally, we present scenario-based sensitivity analysis arising from the decision variables to support decision-makers.