Task Allocation Algorithm and Simulation Analysis for Multiple AMRs in Digital-Intelligent Warehouses

Abstract

In digital-intelligent warehouses, the heavy handling tasks, complex algorithms with high computational demands, and vast solution spaces pose significant challenges to achieving stable, efficient, and balanced operation of multiple Autonomous Mobile Robots (AMRs) for automated cargo handling. This paper focuses on a virtual smart warehouse environment and employs Python software to conduct simulation experiments for multi-AMR task allocation. The simulated smart warehouse comprises three idle AMRs and 16 task points that require transportation. The experimental simulations demonstrate that the improved genetic algorithm can find the global optimal solution with relatively low computational cost, meeting the fast response requirements in real-world operations. It enables stable operation, high efficiency, and balanced task allocation for multiple AMRs. The simulation results validate the reliability of the proposed method, effectively addressing the issues of multi-AMR task allocation and path planning in digital-intelligent warehouses.