Research on Job Scheduling Optimization for Automated Dual-Station Stacker Cranes in Stereoscopic Warehouses

To address the inefficiencies in the job scheduling of stacker cranes and to enhance system throughput, this paper constructs an optimization model aimed at minimizing the total travel distance of the stacker crane, in light of the storage and retrieval characteristics of dual-station stacker cranes. An improved Ant Colony Algorithm (ACA) with enhanced initial pheromone concentration, pheromone update strategy, and transition rules is designed to solve the model. A simulation analysis is conducted using a real-world case of a shelf storage and retrieval task in Company Y's hardware warehouse, and the results are compared against random access strategy, first-come, first-served strategy, and the nearest neighbor strategy. The findings indicate that the improved ACA yields paths that are 20.5%, 17.9%, and 14.6% shorter than those obtained from the aforementioned strategies, respectively. The experimental results validate that the enhanced ACA effectively optimizes the path of dual-station stacker cranes, thereby improving the storage and retrieval efficiency in stereoscopic warehouses.