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Projecte llegit

Títol: Holistic DCB with entry counts


Estudiants que han llegit aquest projecte:


Director/a: MELGOSA FARRÉS, MARC

Departament: FIS

Títol: Holistic DCB with entry counts

Data inici oferta: 12-03-2024     Data finalització oferta: 13-03-2024



Estudis d'assignació del projecte:
    GR ENG SIST AEROESP
Tipus: Individual
 
Lloc de realització: EETAC
 
Paraules clau:
Demand; Capacity; Heuristic; optimisation; ATFCM
 
Descripció del contingut i pla d'activitats:
Nowadays air traffic demand and capacity is deployed sequentially and separately. First, the sector opening scheme is defined considering the forecast traffic demand. Then, if needed the demand management initiatives are deployed. Hence, the proposed opening scheme might be not optimal when the demand initiatives have finished. For this reason, considering demand and capacity management together in the same optimisation problem might improve the performance of the Air Traffic Flow and Capacity Management initiatives.
The TFG must study the holistic DCB model and must provide solution methods.
 
Overview (resum en anglès):
The constant increase in the complexity of the ATM environment is generating big challenges to the sector. One of the biggest challenges is balancing traffic demand (constantly growing) with the available capacity (at its limits).

Traditional Demand-Capacity Balancing methods treat demand and capacity as separate entities, what outcomes not optimal. This project works on the implementation of an Holistic DCB model that is designed to integrate both demand and capacity management in a single framework. This will be performed by defining a model that analyses demand and capacity at the same time, which will provide a more accurate approach of the real operational conditions. The basis of the model is minimizing the operation cost of the hole studied time period. This cost is based on both the demand-related cost and the capacity-related cost. The optimization problem aims at finding the best sector opening scheme in the capacity side, by applying a big penalty to the measured overload, at the same time that it tries to find alternatives for the trajectories (either alternative trajectories and/or delay-appliance). With this, the model intends to \textbf{eliminate overload} (or keep it at the minimum feasible), maintaining demand below capacity in a more efficient and optimal way.

The proposed model is addressed with an heuristic method (implemented in C++) based on Simulated Annealing and A-star (A*). This method allows to tackle large-scale scenarios, where finding an analytical solution is typcally not feasible for computation burden constraints. The method explores different airspace configurations, alternative trajectories and delay allocation, finding the optimal balance between minimizing delays and reducing operational costs.

The results of the model's coded execution, which has been tested under various scenarios, show the validity of this method and its possible use to significantly reduce delays and operational costs while improving the overall efficiency of the system, while it leaves the door open to constant developments and improvements.


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