Optimizing Scheduling for Enhanced Efficiency in Cube Storage Warehouses: A Genetic Algorithm Approach Across Different Schemes

This study investigates the application of a Genetic Algorithm (GA) for optimizing scheduling in automated cube storage warehouses, focusing on enhancing logistics processing speed. The findings underscore the GA’s capability to optimize scheduling processes across various warehouse configurations, including edge and hall depot station (DS) layouts. We demonstrate that the GA consistently outperforms alternative algorithms regarding total and maximum operation times through comprehensive simulations and detailed graph analyses. Specifically, our results show that the GA reduces total operation time by up to 32% and 31% and maximum operation time by up to 29% and 37%, for edge and hall DS, respectively, effectively balancing the workload among Cube Storage Automated Guided Vehicles.