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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
