A Multi-Agent Emergency Material Allocation Approach Based on a Markov Decision Process Under Demand Uncertainty for Sustainable Disaster Response

Effective emergency relief allocation in dynamic post-disaster environments depends critically on accurate and timely demand information. From a sustainability perspective, improving allocation accuracy is essential for using scarce rescue resources efficiently and supporting resilient disaster response. However, existing demand forecasting approaches frequently exhibit systematic bias, leading to resource misallocation and diminished rescue outcomes. Although deploying on-site assessment teams can partially mitigate this limitation, a unified framework that systematically embeds field assessment feedback into operational allocation processes remains lacking. To bridge this gap, this study proposes a multi-agent joint assessment-allocation model that facilitates coordinated operations between demand assessment and resource distribution activities. The sequential decision-making process is formulated as a Markov Decision Process (MDP), and deep reinforcement learning is employed to coordinate the actions of assessment and allocation teams, enabling allocation policies to be continuously refined through real-time field feedback. By improving the match between actual demand and material supply, the proposed model aims to support more resource-efficient disaster response under demand uncertainty. An empirical case study based on the 2025 Dingri County earthquake in Tibet is conducted to validate the proposed framework. Results demonstrate that integrating assessment feedback substantially improves resource allocation performance: in multi-site rescue scenarios, the framework increases the number of rescued individuals, reduces mission completion time, and enhances overall demand satisfaction. Further sensitivity analysis reveals that a moderate increase in team size strengthens cross-site coordination, whereas excessive team deployment yields diminishing returns and may generate operational redundancy. These findings suggest that sustainable emergency management depends not only on the availability of relief resources, but also on the efficient coordination of real-time information acquisition and material allocation. The proposed framework offers a generalizable approach for integrating real-time information acquisition with dynamic relief allocation. It improves the efficient utilization of scarce rescue resources, reduces avoidable operational redundancy, and strengthens the resilience of emergency response systems, thereby contributing to sustainable disaster risk reduction.