Abstract: Restoring damaged road network is a basic part of post-disaster emergency response，and studies mainly focus on how to develop a restoration scheme for the repair crew to quickly open up a rescue route and ensure that the rescue teams，equipment，and relief supplies can be transported to the disaster area in a timely manner.However，most existing works focus on a static road network，which is difficult to adapt to the complex emergency scenarios in extraordinarily serious natural disasters，such as earthquakes and floods.A dynamic disaster road network model is constructed to simulate the dynamic deterioration of emergency scenarios.Based on the Markov decision process，a dynamic decision model for the emergency repair team is constructed，and a corresponding state space，action space， and return function are designed. Finally，an Improved Q-Learning（IQL）for Dynamic Scheduling（IQLDS） algorithm is proposed to adapt to the current state of the road network and quickly make better repair strategies.Experimental results show that，compared with Ant Colony Optimization（ACO） algorithm and the IQL algorithm，the value of the objective function obtained by our method for a large-scale and high-damage road network is reduced by approximately 50%.Furthermore，our method can adjust the learning strategy in a timely manner after accurately perceiving the changes in the road network and make full use of historical experience to obtain a better repair plan.These improvements may help emergency management departments facilitate a schedule plan for the repair crew for restoring a damaged road network more efficiently and effectively.
post-disaster emergency response,
road network restoration,
dynamic scheduling of the repair crew,
optimal action set updating