Airline scheduling is characterized by numerous complexities, including a network of flights, different aircraft types, limited numbers of gates, air traffic control restrictions, environmental regulations, strict safety requirements, a myriad of crew work rules and complicated payment structures, and competitive, dynamic environments in which passenger demands are uncertain and pricing strategies are complex. This, layered with the airline industry’s endemic issues of low profitability, contentious labor issues, and outdated and inadequate infrastructure, poses daunting challenges that have intrigued operations researchers for at least a half-century, and have provided a fertile ground for the development and application of models and algorithms. In this talk, we first briefly summarize the optimization-based accomplishments in this area, highlighting the significant successes and impacts. While impressive, the problem is far from solved today. The focus of this talk, then, is on the many remaining opportunities and challenges, namely:

a) Robust scheduling: A trend in airline scheduling is to generate schedules that are “robust” to the disruptions that plague airline operations. Because airlines have typically constructed schedules with the assumption that every flight departs and arrives as planned, plans are frequently disrupted and airlines often incur significant additional costs beyond those originally planned. A more robust plan can reduce the occurrence and impact of these disruptions.

b) Dynamic scheduling: Stochasticity of passenger demands is a major challenge for the airlines in their quest to produce profit-maximizing schedules. Even using sophisticated optimization tools, many flights upon departure have empty seats, while others suffer a lack of seats to accommodate passengers who desire to travel. One approach to this challenge is to implement dynamic scheduling approaches that re-optimize elements of the flight schedule during the passenger booking process, recognizing that demand forecast quality for a particular date improves as the date approaches.

c) Recovery from irregular operations: We describe approaches designed for use in near real-time mode to adjust operations in response to a variety of disruptions. We present briefly some of the market-based mechanisms being considered to address this problem, with a particular focus on minimizing disruption and delay to passengers.

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Cynthia Barnhart
    • 1
  1. 1.Massachusetts Institute of Technology 

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