Hyper-heuristic enhanced teaching-learning-based optimization for energy-efficient hybrid flow shop scheduling with batch processing machines under time-of-use tariffs

Key Points

• Improved energy efficiency results from enhanced scheduling under time-of-use tariffs, optimizing resource use and minimizing costs.
• The study demonstrates significant reductions in energy consumption, achieving up to 30% lower costs compared to conventional methods.
• This approach utilizes hyper-heuristic methods for optimization, allowing for more effective scheduling of batch processing machines.
• The findings may enable better energy management strategies in industrial settings, supporting sustainability and operational efficiency.