Optimizing Energy Consumption in Stochastic Production Systems: Using a Simulation-Based Approach for Stopping Policy

TL;DR

This paper presents a simulation-based approach to optimize energy consumption in stochastic production systems, demonstrating significant energy savings and robustness in a real-world industrial heat-treatment process.

Contribution

It introduces a novel simulation-based stopping policy that dynamically adjusts processing times using sensor data, improving energy efficiency in uncertain manufacturing environments.

Findings

Energy input reduced by 14-25% compared to baseline.

SBA outperforms static planning scenarios in energy savings.

Approach balances energy and labor costs effectively.

Abstract

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 - an energy-intensive lead-acid battery manufacturer - faces significant process uncertainty in its heat-treatment operations, making static planning inefficient. To evaluate a potential sensor-based solution, the SBA leverages simulated sensor data (using a Markovian model) to iteratively refine Bayesian energy estimates and dynamically adjust batch-specific processing times. A full-factorial numerical 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) Optimized Planned Processing Times (OPT); (2) the company's Current Baseline Practice; 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.