Högskolan i Skövde

Rising energy costs, energy supply uncertainties, and the sustainability crisis have intensified the need for energy efficiency in industrial manufacturing. This adds complexity to balancing traditional production goals such as productivity, quality, and cost. While prior studies address energy-intensive processes or throughput bottlenecks, they often lack integrated decision-support for evaluating optimal trade-offs. To address this gap, this study proposes a novel simulation-based multi-objective optimization framework combined with a knowledge discovery module, demonstrated in an industrial case study. The framework systematically identifies energy and productivity losses, evaluates improvement strategies to determine optimal trade-off solutions, and extracts actionable rules to guide decision making. Case study results show a 23.9% reduction in specific energy consumption and a 27.9% increase in throughput, while emphasizing the need to balance inventory levels. The approach offers a robust, data-driven method for supporting energy-efficient manufacturing. Future research will explore integration with real-time monitoring and extension to additional objectives such as costs and emissions.