Multi-period emergency resource allocation problem with a hybrid ant colony optimization and deep Q-network algorithm

To mitigate losses caused by emergency resource shortages, this paper investigates a multi-period resource allocation problem. By recognizing the interdependence among affected areas, demand points are modeled as a system. A systemic loss metric model is developed to quantify the cascading impacts arising from these interdependencies. Then, an emergency resource allocation model is constructed to minimize the loss and maximize the fairness. To solve the proposed model efficiently, a novel hybrid algorithm (ACO-DQN) that integrates ant colony optimization (ACO) with deep q-network (DQN) is designed. To enhance the convergence and stability of the algorithm, the pheromone mechanism of the ACO is employed to dynamically guide the exploration process and to adjust the Q-value update strategy. Numerical experiments demonstrate that, compared to the DQN, the proposed ACO-DQN shows significant advantages in terms of solution quality, convergence speed, and robustness. Finally, a case study based on the Wenchuan earthquake shows that considering interdependence enables decision-makers to better balance efficiency and fairness when resources are constrained. The findings provide important decision support for improving the overall resilience and recovery post disasters.