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

机译：通过机组配对和日程优化实现航空公司人员部署的级联延误风险

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摘要

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.
机译：本文涉及两个关联航班之间的缓冲时间的分配以及机组配对中为减少由于航班到达延误而造成的航班中断的后备机组人数。机组人员不足，将导致航班延误，例如延误或取消。实际上，大多数此类干扰情况是由于先前航班的到港延误所致。为了解决这个问题，许多研究已经基于有关航班的历史航班到达延迟数据检查了分配方法。但是，航班到达延误可能由许多因素触发。因此，本文提出了一种使用级联神经网络的新的预测方法，该方法考虑了大量的历史航班到达和离开数据。该方法还具有学习能力，因此可以揭示数据背后的未知关系。基于预期的航班到达延迟，可以确定缓冲时间，然后可以使用新的动态后备人员策略来确定所需的后备人员数量。基于从全球112个机场获得的一年飞行数据进行了数值实验。结果表明，通过预测飞行离开延迟作为预测飞行到达延迟的输入，可以提高预测精度。此外，通过使用新的动态后备船员策略，可以减少船员总成本。在当前的大数据时代，这极大地有利于航空公司的航班时刻表稳定性和成本节省。

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  • 来源
    《Risk analysis》 |2017年第8期|1443-1458|共16页
  • 作者

    Chung Sai Ho; Ma Hoi Lam; Chan Hing Kai;

  • 作者单位

    Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Hong Kong, Peoples R China;

    Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Hong Kong, Peoples R China;

    Univ Nottingham Ningbo China, Nottingham Univ Business Sch China, Ningbo, Zhejiang, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Big data; flight reliability; robust crew pairing;

    机译：大数据;飞行可靠性;强大的机组配对;

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