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.