Integrating decision maker preferences to a risk-averse multi-objective simulation-based optimization for a military workforce planning, asset management and fleet management problem

The complexities associated with the interconnections between workforce and fleet of assets to achieve organizational objectives often lead to uncertain and multi-objective problems. Further, different decision makers may have different preferences towards the degree of risk they tend to accept in their decisions. We contribute to the literature by developing a risk-averse multi-objective optimization model to determine the appropriate settings of recruitment, promotion and fleet transition strategies that simultaneously minimize the weighted sum of expected and Conditional-Value-at-Risk values of several objectives. In this study, we consider three objectives, namely, workforce cost (WC), capability gap (CG) (defined as the unavailability of assets due to workforce shortages to crew assets) and capital and sustainment cost (CSC) of assets. To solve this problem, we develop a simulation-optimization method integrated with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as a hybrid solution approach. The simulation-optimization method combines Non-dominated Sorting Genetic Algorithm (NSGA) III and a System Dynamics (SD) simulation model. In an iterative process, NSGA-III generates the recruitment, promotion and fleet transition strategies and the SD simulation model evaluates the fitness of the generated strategies by calculating the values of three objectives. The output of the simulation-optimization method is a set of Pareto optimal solutions and the corresponding Pareto fronts. We use TOPSIS to rank the set of Pareto front points and determine the best point. We apply the proposed model and solution approach on a joint problem of workforce planning, fleet renewal and asset management inspired by the Australian Navy. The results provide valuable information for military decision makers regarding the trade-offs between various alternatives based on their preferences.