Energy-Efficient Scheduling in Heat Treatment Workshops Based on Task Clustering and Job Batching

The development of green and efficient manufacturing has brought on complex trade-offs between energy consumption control and resource utilization efficiency in heat treatment tasks. Traditional single-piece scheduling methods are challenged in addressing the complexity of multiple tasks and energy optimization. In this paper, an optimized scheduling method for heat treatment workshops is proposed by integrating task grouping and batch combination strategies. Specifically, a genetic algorithm enhanced with local search and adaptive mutation operators is proposed under constraints such as delivery deadlines and equipment capacity. During the strategy generation process, equipment changeover and idle time are considered. By performing multi-dimensional matching of workpiece processing processes, heat treatment requirements, and quality characteristics, an innovative clustering mechanism for dynamic production batches based on task similarity is constructed. To validate the effectiveness, actual production data from a heat treatment workshop were selected for analysis and evaluation. The results show that the proposed method reduces the total production time by 31.6% with on-time delivery of orders, and the equipment operation frequency is reduced by 28.4%, which verifies the practicality and advancement of the proposed method.