Abstract
Machine tool processing scheduling plays a pivotal role in modern manufacturing systems, significantly influencing production efficiency, resource utilization, and timely delivery. Due to its combinatorial and NP-hard characteristics, traditional optimization techniques often face challenges when dealing with large-scale and complex scheduling problems. In this paper, we present an optimization approach for machine tool scheduling that leverages the Differential Evolution (DE) algorithm. By tailoring DE for discrete scheduling environments through specialized encoding and decoding techniques, the algorithm is able to effectively explore the solution space while ensuring the generation of feasible schedules. The results from our experiments reveal that the proposed approach outperforms conventional heuristic methods, particularly in minimizing makespan and achieving a balanced workload distribution across machines. This study underscores the potential of DE as a robust, adaptive, and efficient optimization tool for tackling complex scheduling problems in the context of intelligent manufacturing systems.