Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76092
Title: Cascading delay risk of airline workforce deployments with crew pairing and schedule optimization
Authors: Chung, SH 
Ma, HL 
Chan, HK
Keywords: Big data
Flight reliability
Robust crew pairing
Issue Date: 2017
Publisher: Wiley-Blackwell
Source: Risk analysis, 2017, v. 37, no. 8, special issue SI, p. 1443-1458 How to cite?
Journal: Risk analysis 
Abstract: This article concerns the assignment of buffer time between two connected flights and the number of reserve crews in crew pairing to mitigate flight disruption due to flight arrival delay. 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.
URI: http://hdl.handle.net/10397/76092
ISSN: 0272-4332
EISSN: 1539-6924
DOI: 10.1111/risa.12746
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