Abstract
Metro station restoration sequence optimization is crucial during post-disaster recovery. Taking both budget limitations and repair time uncertainty into account, this paper proposes a resilience-based optimization model for choosing an optimal restoration sequence scheme, maximizing the global average efficiency, under the condition that the network accessibility meets given resilience requirements. Evolutionary algorithm NSGA-II is applied to solve the model. A Case study in Nanjing and Zhengzhou gives insights into restoration sequence strategies for decision-makers. Results show that a ring network is more robust than a radial network under the same scale attack. Under limited budget, the optimal restoration sequence is closely related to the damaged stations’ location and repair time. Specifically, if damaged stations’ distribution is relatively centralized and transfer stations need more repair time, giving repair priority to transfer stations is not always the best strategy. If damaged stations’ distribution is relatively scattered and all stations’ repair time is the same, the station with a bigger node degree should be repaired earlier. However, this conclusion may be invalid if transfer stations repair time is far longer than others. Sensitivity analysis show that the total budget is more sensitive than one day’s budget in the entire restoration phase. However, in the emergency phase, increasing one day’s budget is more significant for shortening recovery time. The proposed model can contribute to effective and flexible decision-making for metro network restorations.
Highlights
Some research has been done in the pursuit of the optimal restoration sequence scheme for transportation networks, which can be conceptualised as a network design problem (NDP). roughout the last decade, researchers have combined resilience evaluation and the NDP to explore the methodology of the resilience-based optimal restoration plan
Solution Process and Algorithm. e solution process of the proposed model for restoration sequence is shown as Figure 2. e solution process is divided into two-stage. e first stage aims to make the network accessibility’s resilience index RZ and recovery rate QZ reach to the threshold and maximize as much as possible. e NSGA-II algorithm is adopted to deal with this problem
Taking budget limitations and repair time uncertainty into account, combined with the network topology characteristics, the optimal restoration sequence scheme aims to maximize E, when the Z satisfies pregiven resilience requirements. e resilience requirements include the recovery ratio and the resilience index that can reflect the network performance change in process. e proposed method is applied to the Nanjing and Zhengzhou Metro network. e benefits of the proposed model are as follows: (1) e proposed method focuses on network topology characteristics and has a good handle on the node degree, network accessibility, and global average efficiency. e model presents a flexible framework
Summary
As one of the fastest and high-capacity means of transportation, metros have become an effective way of solving congestion problems. Resilience-based restoration optimization was widely used in road network or road-bridge network, but few studies applied to the metro network. Focusing on network topology characteristics, this paper proposes an optimal metro restoration sequence model to guide multi-objective decision-making. E proposed model focuses on metro topology characteristics, and can transfer a multiobjective problem to a single-objective combinatorial problem with additional constraints that respect the other goals. E majority of existing restoration optimization models based on resilience only considered the resilience index of the network performance, but ignored the recovery trajectories of restoration schemes. Focusing on network topology characteristics, this paper provides a valuable and flexible tool for optimizing the metro restoration sequences, while taking limited budget restraints and repair time uncertainty into account, giving a guideline for multi-objective decision-making.
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