Optimizing energy consumption in stochastic production systems: Using a simulation-based approach for stopping policy

In response to the escalating need for sustainable manufacturing, this study introduces a Simulation-Based Approach (SBA) to model a stopping policy for energy-intensive stochastic production systems, developed and tested in a real-world industrial context. The case company – a lead-acid battery manufacturer – faces significant process uncertainty in its heat-treatment operations, making static planning inefficient and limiting energy efficiency. To evaluate a potential sensor application for real-time control, the SBA leverages simulated sensor data (using a Markovian model) to iteratively refine Bayesian energy estimates and dynamically adjust batch-specific processing times. A discrete-event simulation, mirroring the company's 2024 heat-treatment process, evaluates the SBA's energy reduction potential, configuration robustness, and sensitivity to process uncertainty and sensor distortion. Results are benchmarked against three planning scenarios: (1) the company's Current Baseline Practice; (2) Optimized Planned Processing Times (OPT); and (3) an Ideal Scenario with perfectly known energy requirements. SBA significantly outperforms OPT across all tested environments and in some cases even performs statistically equivalent to an Ideal Scenario. Compared to the Current Baseline Practice, energy input is reduced by 14–25 %, depending on uncertainty and sensor accuracy. A Pareto analysis further highlights SBA's ability to balance energy and inspection-labour costs, offering actionable insights for industrial decision-makers.