Multi‐objective airport gate assignment problem in planning and operations
Notice bibliographique
Résumé
SUMMARY We consider the assignment of gates to arriving and departing flights at a large hub airport. This problem is highly complex even in planning stage when all flight arrivals and departures are assumed to be known precisely in advance. There are various considerations that are involved while assigning gates to incoming and outgoing flights (such a flight pair for the same aircraft is called a turn) at an airport. Different gates have restrictions, such as adjacency, last‐in first‐out gates and towing requirements, which are known from the structure and layout of the airport. Some of the cost components in the objective function of the basic assignment model include notional penalty for not being able to assign a gate to an aircraft, penalty for the cost of towing an aircraft with a long layover, and penalty for not assigning preferred gates to certain turns. One of the major contributions of this paper is to provide mathematical model for all these complex constraints that are observed at a real airport. Further, we study the problem in both planning and operations modes simultaneously, and such an attempt is, perhaps, unique and unprecedented. For planning mode, we sequentially introduce new additional objectives to our gate assignment problem that have not been studied in the literature so far—(i) maximization of passenger connection revenues, (ii) minimization of zone usage costs, and (iii) maximization of gate plan robustness—and include them to the model along with the relevant constraints. For operations mode, the main objectives studied in this paper are recovery of schedule by minimizing schedule variations and maintaining feasibility by minimal retiming in the event of major disruptions. Additionally, the operations mode models must have very, very short run times of the order of a few seconds. These models are then applied to a functional airline at one of its most congested hubs. Implementation is carried out using Optimization Programming Language, and computational results for actual data sets are reported. For the planning mode, analyst perception of weights for the different objectives in the multi‐objective model is used wherever actual dollar value of the objective coefficient is not available. The results are also reported for large, reasonable changes in objective function coefficients. For the operations mode, flight delays are simulated, and the performance of the model is studied. The final results indicate that it is possible to apply this model to even large real‐life problems instances to optimality within short run times with clever formulation of conventional continuous time assignment model. Copyright © 2013 John Wiley & Sons, Ltd.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».