Optimization of Post-disaster Road Network Repair Strategy Considering Road Recovery Level

Existing studies on post-disaster road network repair strategies have ignored the impact of different levels of road damage and recovery on the efficiency of network repair. To solve this issue, this study integrates the construction material distribution (CMD) with the repair crew scheduling and routing problem (RCSRP), and determine the level of road recovery through the CMD. Then, a bi-level optimization model is proposed with network performance resilience and recovery speed resilience as the optimization objectives. A two-stage optimization algorithm (TSOA) composed of a genetic algorithm with an improved coding method (ICM-GA) and the Frank-Wolfe algorithm (FW) is then employed to solve this model. Finally, the effectiveness of the model and algorithm is validated through simulation experiments. The results indicate that, under given material and time constraints, the optimal repair strategy proposed in this study outperforms the repair strategy without considering road recovery level by 17.51% and 5.42% in terms of network performance resilience and recovery speed resilience, respectively. This demonstrates the positive significance of considering road recovery level in formulating road network repair strategies. Besides, this strategy can be applied to optimize the configuration of workstation count for different-scale networks.