Abstract
Recovery from major disasters is generally divided into an emergency recovery phase (ERP) and a comprehensive recovery phase (CRP). The ERP has multiple difficulties and limits. This paper investigates the optimization of the transportation network recovery strategy (TNRS) during the ERP under uncertainty. First, two resilience metrics are proposed to measure the recovery rapidity and the cumulative loss of network performance. Second, by applying connectivity as an indicator of transportation network performance, the resilience-based bi-level programming model is established for both deterministic and stochastic cases. The upper level determines which road segments need to be restored and the repair time sequence to maximize the system resilience. The lower level formulates the user response to the upper decision as a User Equilibrium (UE) with a time series. Then, a novel algorithm that integrates a genetic algorithm for the parallel machine scheduling problem (PMSP) and the Frank–Wolfe algorithm for the UE is designed. Finally, the procedure and the effectiveness of the proposed method are demonstrated via a case study.
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