Decreasing airline delay propagation by re-allocating scheduled slack

Passenger airline delays have received increasing attention over the past several years as air space congestion, severe weather, mechanical problems, and other sources cause substantial disruptions to a planned flight schedule. Adding to this challenge is the fact that each flight delay can propagate to disrupt subsequent downstream flights that await the delayed flight's aircraft and crew. This potential for delays to propagate is exacerbated by a fundamental conflict: slack in the planned schedule is often viewed as undesirable, as it implies missed opportunities to utilize costly perishable resources, whereas slack is critical in operations as a means for absorbing disruption. This article shows how delay propagation can be reduced by redistributing existing slack in the planning process, making minor modifications to the flight schedule while leaving the original fleeting and crew scheduling decisions unchanged. Computational results based on data from a major U.S. carrier are presented that show that significant improvements in operational performance can be achieved without increasing planned costs.

[1]  Peter Belobaba,et al.  Analysis of the potential for delay propagation in passenger airline networks , 2008 .

[2]  John R. Birge,et al.  A Stochastic Programming Approach to the Airline Crew Scheduling Problem , 2006, Transp. Sci..

[3]  François Soumis,et al.  An Optimization Model for the Simultaneous Operational Flight and Pilot Scheduling Problem , 2001, Manag. Sci..

[4]  Allan Larsen,et al.  Airline Disruption Management - Perspectives, Experiences and Outlook , 2007 .

[5]  Edmund K. Burke,et al.  A multi-objective approach for robust airline scheduling , 2010, Comput. Oper. Res..

[6]  George L. Nemhauser,et al.  A Robust Fleet-Assignment Model with Hub Isolation and Short Cycles , 2004, Transp. Sci..

[7]  Cynthia Barnhart,et al.  Planning for Robust Airline Operations: Optimizing Aircraft Routings and Flight Departure Times to Minimize Passenger Disruptions , 2006, Transp. Sci..

[8]  George L. Nemhauser,et al.  Airline Crew Recovery , 2000, Transp. Sci..

[9]  Ahmed F. Abdelghany,et al.  A Proactive Crew Recovery Decision Support Tool for Commercial Airlines During Irregular Operations , 2004, Ann. Oper. Res..

[10]  Jephthah A. Abara,et al.  Applying Integer Linear Programming to the Fleet Assignment Problem , 1989 .

[11]  Srini Ramaswamy,et al.  Airline Crew Scheduling with Regularity , 2001, Transp. Sci..

[12]  Arthur E. McGarity,et al.  Design And Operation Of Civil And Environmental Engineering Systems , 1997 .

[13]  George L. Nemhauser,et al.  A Stochastic Model of Airline Operations , 2002, Transp. Sci..

[14]  François Soumis,et al.  An integrated aircraft routing, crew scheduling and flight retiming model , 2005, Comput. Oper. Res..

[15]  Jacques Desrosiers,et al.  AN OPTIMIZATION MODEL FOR A REAL-TIME FLIGHT SCHEDULING PROBLEM , 2002 .

[16]  Omer Tsimhoni,et al.  A recursion-based approach to simulating airline schedule robustness , 2008, 2008 Winter Simulation Conference.

[17]  Shangyao Yan,et al.  A decision support framework for handling schedule perturbation , 1996 .

[18]  Cynthia Barnhart,et al.  Flight operations recovery: New approaches considering passenger recovery , 2006, J. Sched..

[19]  George L. Nemhauser,et al.  Airline Crew Scheduling Under Uncertainty , 2005, Transp. Sci..

[20]  Xiangtong Qi,et al.  Disruption Management: Framework, Models And Applications , 2004 .

[21]  SoumisFrançois,et al.  An integrated aircraft routing, crew scheduling and flight retiming model , 2007 .

[22]  Cynthia Barnhart,et al.  Airline Fleet Assignment with Time Windows , 2000, Transp. Sci..

[23]  Diego Klabjan,et al.  Airline Crew Scheduling , 2003 .

[24]  Laurence A. Wolsey,et al.  Integer and Combinatorial Optimization , 1988, Wiley interscience series in discrete mathematics and optimization.

[25]  Amos Levin Scheduling and Fleet Routing Models for Transportation Systems , 1971 .