Cascading Delay Risk of Airline Workforce Deployments with Crew Pairing and Schedule Optimization

Sai Ho Chung, Hoi Lam Ma, Hing Kai Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

26 Citations (Scopus)

Abstract

Insufficient crew members for a flight will lead to flight disruptions such as delays or cancellations. In reality, most of these disruption cases are due to arrival delays of the previous flights. To tackle this problem, many research studies have examined the assignment method based on the historical flight arrival delay data of the concerned flights. However, flight arrival delays can be triggered by numerous factors. Accordingly, this article proposes a new forecasting approach using a cascade neural network, which considers a massive amount of historical flight arrival and departure data. The approach also incorporates learning ability so that unknown relationships behind the data can be revealed. Based on the expected flight arrival delay, the buffer time can be determined and a new dynamic reserve crew strategy can then be used to determine the required number of reserve crews. Numerical experiments are carried out based on one year of flight data obtained from 112 airports around the world. The results demonstrate that by predicting the flight departure delay as the input for the prediction of the flight arrival delay, the prediction accuracy can be increased. Moreover, by using the new dynamic reserve crew strategy, the total crew cost can be reduced. This significantly benefits airlines in flight schedule stability and cost saving in the current big data era.
Original languageEnglish
Pages (from-to)1443-1458
Number of pages16
JournalRisk Analysis
Volume37
Issue number8
DOIs
Publication statusPublished - 1 Aug 2017

Keywords

  • Big data
  • flight reliability
  • robust crew pairing

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Physiology (medical)

Cite this