Entire-process scheduling optimization strategy for railway emergency logistics based on two-stage multi-objective programming
Bibliographic record
Abstract
Conventional railway emergency logistics frameworks are typically characterized by transport capacity adjustments to prioritize emergency material transportation. However, this paradigm frequently results in extended emergency response times and substantial delays in conventional freight operations. To address these limitations, an entire-process optimization strategy encompassing the Emergency Recovery Phase (ERP) and Post-Emergency Recovery Phase (PERP) was formulated, accompanied by a two-stage multi-objective optimization model. Diverging from conventional frameworks that necessitate operation plan reconfiguration for emergency train deployment, the proposed strategy streamlined operation plan replanning in the ERP through formation and loading plan optimization, while concurrently incorporating transportation cost-effectiveness in the PERP into the holistic optimization framework. The ERP submodel was designed to ensure the balanced allocation of limited emergency materials while achieving significant reductions in emergency response time. Subsequently, the PERP submodel incorporated dual considerations of transportation cost-effectiveness for railway carriers and cargo owners, while mitigating delay losses in conventional freight operations. To resolve this multi-objective optimization model, the Adaptive Variable Neighborhood Non-dominated Sorting Genetic Algorithm-II (AVNNSGA-II) was developed. The following results were obtained by this empirical study. (1) The ERP submodel attained emergency material satisfaction rates exceeding 51.28% across multiple disaster-affected areas while achieving emergency response time reductions of 6.16–19.22% relative to conventional railway emergency logistics frameworks. Notably, it demonstrated superior performance relative to road-based emergency logistics under different speed scenarios, with 55.9–69.4% response time reductions. (2) The PERP submodel effectively reduced delay losses in non-emergency freight operations by 50.49% through the implementation of differentiated transport prioritization mechanisms. (3) The superiority of this algorithm was confirmed with 97% of Pareto front solutions of AVNNSGA-II exceeding those of conventional NSGA-II. In conclusion, the proposed strategy is demonstrated to synergistically balance emergency response efficiency and transportation cost-effectiveness, thereby significantly enhancing railway emergency logistics performance. Furthermore, the integration of AVNNSGA-II with the multi-objective optimization model provides innovative perspectives for addressing large-scale rail freight allocation and scheduling challenges.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".