Dynamic unbalanced task allocation of warehouse AGVs using integrated adaptive large neighborhood search and Kuhn–Munkres algorithm

Dynamic task allocation poses a complex and challenging decision problem for automated guided vehicles operating within warehouse environments. In this study, we investigate a new method for dynamically allocating unbalanced tasks to a multi-AGV system, with a focus on real-time task arrivals. The research treats this problem as a dynamic vehicle routing problem with pickups and deliveries and proposes the use of a rolling horizon strategy to periodically reallocate tasks by iteratively solving mixed integer programming. To enhance the computational efficiency, a novel metaheuristic is developed, which integrates adaptive large neighborhood search and the Kuhn–Munkres algorithm. Comprehensive numerical experiments are conducted to demonstrate the potential of the proposed approach, in comparison with state-of-the-art heuristics and metaheuristic algorithms, providing insight into the efficiency and effectiveness of the proposed dynamic unbalanced task allocation method for multi-AGV systems in warehouse environments.