Advertisement

The Airline Operations Control Problem

  • António J. M. CastroEmail author
  • Ana Paula Rocha
  • Eugénio Oliveira
Chapter
  • 907 Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 562)

Abstract

Chapter 3 was mainly about the state of the art in airline operations’ disruption management and related fields.We have additionally proposed a possible classification encompassing the existing work within this field. This chapter is about the main application domain we are interested in and the knowledge it entails. The information provided here is the result of the observation and interviews we have done at TAP Portugal1 Airline Operations Control Center (AOCC), complemented with information from related literature. As we stated in Chapter 1 and considering the line of research we chose to follow, it is important to have a rigorous description of the real application domain scenario as well as the problem to be solved. This chapter is a source of empirical knowledge, that will help the reader to understand both.We will introduce the AOCC organization and how it works, the type of problems that the AOCC human operators have to solve as well as the most common solutions and methods used therefore. Some real statistical data from TAP Portugal will be presented as well as the main costs involved in possible solutions.

Keywords

Crew Member Departure Delay Maintenance Service Disruption Management Aircraft Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • António J. M. Castro
    • 1
    Email author
  • Ana Paula Rocha
    • 2
  • Eugénio Oliveira
    • 2
  1. 1.LIACCUniversity of PortoPortoPortugal
  2. 2.LIACC, DEI, FEUPUniversity of PortoPortoPortugal

Personalised recommendations