CBL - Campus del Baix Llobregat

Projecte llegit

Títol: Análisis de protección de pasajeros por ajustes de capacidad a corto plazo de una aerolinea


Estudiants que han llegit aquest projecte:


Director/a: MELGOSA FARRÉS, MARC

Departament: FIS

Títol: Análisis de protección de pasajeros por ajustes de capacidad a corto plazo de una aerolinea

Data inici oferta: 05-03-2022     Data finalització oferta: 05-11-2022



Estudis d'assignació del projecte:
    Tipus: Individual
     
    Lloc de realització: Fora UPC    
     
            Supervisor/a extern: Raquel Rubion Sarbosa
            Institució/Empresa: Vueling
            Titulació del Director/a: Scheduling and Distribution Manager
     
    Paraules clau:
    Pasajeros, protección, aerolinea
     
    Descripció del contingut i pla d'activitats:
    En este proyecto se pretende analizar el impacto generado a los clientes por la reducción de capacidad a corto plazo en una aerolínea. Más en particular en un periodo en el que la demanda y ocupación de los vuelos ha tenido una reducción drástica debido a los múltiples efectos derivados de la pandemia del Covid y esto ha obligado a las aerolíneas a hacer cambios en su planificación de unas dimensiones y una agilidad nunca vistas anteriormente en el sector.

    El propósito es diseñar una herramienta que permita realizar una simulación más detallada de las alternativas generadas a los pasajeros en caso de cancelación de los vuelos, permitiendo así una evaluación más detallada y rápida para la toma de decisiones basada tanto en las pérdidas directas de la operativa (VM) como en el disruptiva generada a los pasajeros (NPS).
     
    Overview (resum en anglès):
    On this document, it has been evaluated the impact due to Covid 19 pandemic on the design and development process for the schedule of flights for an airline. This project also proposes the design and implementation of a tool that allows to analyze the impact and the viability of tactical cancellations. The tool provides information to evaluate whhich reaccommodation strategy for the disrupted passengers is the best and obtain the economic impact and the effect of the movement of passengers on the inventory.
    The principal consequence that all airlines have suffered has been the variability of the demand, which has been highly responsive to the changes of the mobility of the population and the specific sanitary evolution of each region around the world.
    To adapt to the demand, which has been mainly decreased, many airlines have faced to the need of reducing the published capacity that will finally be operated in order to minimize the loss. This implies the cancellation of bookings for millions of passengers that have been affected whom the regulation CE261 requires to find an alternative flight with the minor impact for the customer.
    To compute the impact of the cancellations and the reacommodations of each passenger, a support tool has been designed. This allows to simulate the adjustments that are produced on the inventory once the flight is cancelled, considering the type of booking and the requirements that must be considered according to the regulation and each typology of passenger.
    The implementation of the tool has provided to reduce more than 90% of the time resources required to realize this exercise manually. From the evaluation of the first results, it has been deduced that mostly for any scenario where the load factors are low, the impact and the cost does not have a dependency on the strategy and priority that has been selected. For other scenarios where the load factors are higher, there has been spotted some differences on the reacommodations and the cost for each one. However, those results cannot be considered defining due to the randomness of part of the input information
    used on the exercise which has been considered mainly with academic purposes.
    From this point, it is proposed to continue with the analysis of new scenarios, including the real and updated information in order to try to obtain more clear conclusions or detect patterns that may be helpful knowledge for subsequent capacity adjustments.


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