大量需求点下基于深度Q学习的受损路网抢修队调度

受损路网抢修是重特大自然灾害发生后开展应急处置和救援的一个基本前提,主要研究如何对道路抢修队进行合理的调度以快速恢复路网畅通、保障救援队伍和应急物资从出救点及时输送到各需求点.鉴于已有研究在面向大量需求点时往往很难给出有效的调度策略,首先基于路网模型和马尔科夫决策过程分析抢修队修复受损路网的关键因素,并设计一种双反馈回报函数;然后基于深度Q学习求解抢修队的最优调度策略;最后通过对比实验结果表明,在大量需求点环境下,所提出方法具有较好的稳定性和可靠性,兼顾受损路网的修复效率和运输效率,能够以更少的修复代价令所有需求点可达,为灾后复杂应急场景下的受损路网抢修提供有益的尝试.

Repair crew scheduling for damaged road network with enormous demand points using deep Q-learning

Repairing the damaged road network, which mainly focuses on how to reasonably schedule the repair crew to quickly unblock the road network and ensure that rescue teams and emergency resources in the source node can be delivered to different demand nodes in time, is a basic premise for emergency disposal and rescue after the occurrence of extraordinarily serious natural disasters. However, it is difficult for the existing methods to find a feasible scheduling strategy under enormous demand nodes. Therefor the key factors of repairing the damaged road network are first analyzed according to the road network model and the Markov decision-making process, based on which a double-feedback reward function is designed. Then, the deep Q-learning is utilized to solve the optimal scheduling strategy of the repair crew. Finally, comprehensive experimental studies show that for the damaged road network with enormous demand points, the proposed method has high stability and reliability, can achieve a good balance between the repair efficiency and the transportation efficiency, and can make all the demand points achievable with less repair cost, which may provide a useful attempt to repair the damaged road network in complex emergency scenarios of post-disaster.