Optimized production scheduling: a case study on food and steel industries

Abstract

Background

The transition to a climate-neutral energy system requires industrial demand-side flexibility to complement renewable energy sources. Energy-intensive industries, such as iron and steel or food industries, play a pivotal role in this transformation by adopting demand-side management strategies. However, challenges remain in terms of aligning production scheduling without compromising operational constraints.

Results

This study presents two use cases that employ integer linear programming to optimize scheduling and investigate industrial flexibility, focusing on baking ovens in an industrial-scale bakery and a rolling mill in an electric steel plant. In the bakery, optimized schedules could reduce energy consumption during the nightshift (dayshift) by 30% (43%) and total runtime by 43% (55%). The rolling mill model achieves cost savings of up to 7% by aligning production schedules with volatile electricity prices over the medium term. There is a correlation observed between electricity price volatility and cost savings, with greater fluctuations yielding higher savings. The potential for load-shifting potential is demonstrated, with weekly shifts reaching nearly 40% of original energy consumption in favorable periods.

Conclusions

The results highlight the importance of tailored scheduling models in unlocking the potential for demand-side flexibility in industrial processes. While optimized bakery schedules improve energy efficiency, optimized rolling mill schedules demonstrate the feasibility of minimizing costs in the medium term through implicit demand response. The findings demonstrate the challenges through low automation levels, which can be overcome by combining optimization approaches with manual ‘what-if’ tools. Additionally, the need for more accurate energy price forecasts or intermediate electricity markets to bridge the gap between short-term spot market and the long-term futures markets is demonstrated. Overcoming computational constraints, ensuring user acceptance, and addressing market barriers are essential for scaling these strategies across industries.