Automated Scheduling Algorithm Research in Warehouse Business System

This study addresses multi-AGV scheduling challenges in modern warehouse operations through an improved ant colony algorithm for task allocation and dynamic path planning. The proposed solution implements a hierarchical scheduling framework that effectively decouples task allocation from path planning, incorporating an innovative space-time resource reservation mechanism for conflict prevention. Extensive testing demonstrates exceptional performance with 235ms response time and 98.6% completion rate for concurrent operations of 100 AGVs. Following a 30-day deployment in a real warehouse environment, the system achieved remarkable improvements: daily order processing increased by 42.3%, AGV utilization reached 86.3%, and energy consumption decreased by 18.5%. These results significantly enhance warehouse operational efficiency while reducing costs, demonstrating the practical value of our approach in smart warehousing applications.