Leveraging simulation and robust decision-making to optimize military asset maintenance and workforce management

Purpose This study develops an integrated simulation and robust decision-making framework to support long-term military asset maintenance and workforce planning under deep uncertainty. The objective is to evaluate how alternative maintenance workforce strategies perform across a wide range of plausible future conditions where key system parameters cannot be reliably estimated. Design/methodology/approach A discrete-event simulation model is developed to represent maintenance operations, workforce competency progression, certification dynamics and resource constraints within a military maintenance system. The model is integrated with an exploratory modelling and analysis (EMA) framework implementing robust decision-making (RDM). Predefined workforce recruitment and periodic maintenance strategies are evaluated across ensembles of scenarios generated using Latin Hypercube sampling, followed by robustness assessment and scenario discovery analysis. Findings Results indicate that strategies combining lower workforce recruitment with higher maintenance intensity can outperform intuitively preferred high-resource strategies when evaluated under deep uncertainty. The analysis identifies workforce separation rates – particularly among coordinator and verifier roles – as key drivers of system vulnerability. The framework also reveals critical uncertainty thresholds that may act as early warning indicators of degradation in maintenance performance. Research limitations/implications The study focuses on a single asset class and workforce type to enable detailed behavioural representation. Future research may extend the framework to multiple asset portfolios, adaptive strategy design and dynamic policy adjustment. Practical implications The proposed approach provides defence planners with a transparent decision-support tool for evaluating maintenance and workforce policies when historical data are limited, enabling identification of robust strategies and workforce indicators that should be monitored to sustain long-term readiness. Originality/value This study presents an integrated framework combining simulation-based asset management modelling with robust decision-making to evaluate maintenance and workforce strategies for military systems under deep uncertainty. By assessing strategies across many scenarios using robustness metrics, the framework supports more resilient long-term sustainment planning.