Multi-Mechanism-Driven Whale Optimization Algorithm for Collaborative Scheduling of Industrial Equipment

Cranes are typical industrial equipment frequently used in process industries. As the scale of cranes continues to grow, the complexity of their collaborative scheduling problems becomes increasingly prominent. However, traditional optimization algorithms cannot solve these problems well. The dynamic balancing of global search and local exploitation and sensitivity to local optimal solutions in collaborative scheduling problems of industrial equipment present significant hurdles. Hence, this paper proposes a multi-mechanism-driven whale optimization algorithm (MMD-WOA) for establishing a collaborative scheduling model involving multiple cranes operating on the same track. First, a dynamic inertia weight coefficient is devised to balance global search and local exploitation based on environmental cues. Then, a Levy mutation mechanism is integrated to augment the population's capability to escape local optima. After that, a boundary solution repair mechanism is formulated to enhance the utilization and diversity of individual populations. Finally, the effectiveness of the proposed MMD-WOA is validated across 23 benchmark functions and an industrial scheduling problem. The experimental results demonstrate the superior global optimal search performance and robustness of the proposed method.