Entire-process scheduling optimization strategy for railway emergency logistics based on two-stage multi-objective programming
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle