Resilience-Based Restoration Sequence Optimization for Metro Networks: A Case Study in China
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".