Application of Multi-queuing Network Systems for Warehouse Logistics and AGVs Scheduling Using Ant Colony and SIMULINK

Abstract

This research addresses efficiency challenges in large-scale distribution centers by developing an integrated multi-queuing network model optimized with an ant colony optimization (ACO) algorithm for dynamic automated guided vehicle (AGV) scheduling. Traditional scheduling methods often lead to congestion, long cycle times, and underutilized resources in multi-tier warehouse systems. The proposed methodology models warehouse zones as interconnected service nodes and employs ACO with adaptive parameter control for real-time path planning. Simulations conducted in MATLAB/SIMULINK under varied configurations demonstrated substantial performance gains over conventional methods. Key results include a 16.1% increase in throughput (to 176.8 tasks/hour), a 24.7% reduction in cycle time (to 6.7 minutes), a 32.4% decrease in daily travel distance, and a 64.3% reduction in queue wait time. The ACO algorithm converged efficiently, and an optimal configuration using 12 AGVs in a mixed layout was identified. All improvements were statistically significant (p < 0.001). The study concludes that the integrated ACO multi-queuing system significantly enhances warehouse operational efficiency and provides a robust framework for dynamic AGV coordination. Future research should explore machine learning integration for predictive scheduling and model validation in diverse industrial environments.