A two stage heuristic algorithm for the integrated aircraft and crew schedule recovery problems

We propose a new heuristic algorithm for the integrated aircraft and crew schedule recovery problem.We propose a new model for the integrated aircraft and flight recovery problem.We propose a new model for the crew schedule recovery problem.Three sets of disruption scenarios are simulated to test algorithms performance. Airline disruptions incurred huge cost for airlines and serious inconvenience for travelers. In this paper, we study the integrated aircraft and crew schedule recovery problem. A two stage heuristic algorithm for the integrated recovery problem is proposed. In the first stage, the integrated aircraft recovery and flight-rescheduling model with partial crew consideration is built. This model is based on the traditional multi-commodity network model for the aircraft schedule recovery problem. The objective of this model also includes minimization of the original crew connection disruption. In the second stage, the integrated crew schedule recovery and flight re-scheduling model with partial aircraft consideration is built. We proposed a new multi-commodity model for the crew schedule recovery. The main advantage of such model is that it is much more efficient to integrate the flight-scheduling and aircraft consideration. New constraints are incorporated to guarantee that the aircraft connections generated in the stage 1 are still feasible. Two stages are run iteratively until no improvement can be achieved. Experimental results show that our method can provide better recovery solutions compared with the benchmark algorithms.

[1]  Diego Klabjan,et al.  Integrated Airline Fleeting and Crew-Pairing Decisions , 2007, Oper. Res..

[2]  Dušan Teodorović,et al.  Optimal dispatching strategy on an airline network after a schedule perturbation , 1984 .

[3]  Allan Larsen,et al.  Disruption management in the airline industry - Concepts, models and methods , 2010, Comput. Oper. Res..

[4]  Gang Yu,et al.  Optimization Model and Algorithm for Crew Management During Airline Irregular Operations , 1997, J. Comb. Optim..

[5]  Dušan Teodorović,et al.  Model to Reduce Airline Schedule Disturbances , 1995 .

[6]  Michel Bierlaire,et al.  A column generation algorithm for disrupted airline schedules , 2007 .

[7]  Jonathan F. Bard,et al.  Multiple fleet aircraft schedule recovery following hub closures , 2001 .

[8]  Ahmad I. Jarrah,et al.  A Decision Support Framework for Airline Flight Cancellations and Delays , 1993, Transp. Sci..

[9]  Michael D. D. Clarke,et al.  Development of heuristic procedures for flight rescheduling in the aftermath of irregular airline operations , 1998 .

[10]  Michel Bierlaire,et al.  Constraint-specific recovery network for solving airline recovery problems , 2010, Comput. Oper. Res..

[11]  JONATHAN F. BARD,et al.  Optimizing Aircraft Routings in response to Groundings and Delays , 2001 .

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

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

[14]  Gang Yu,et al.  A Grasp for Aircraft Routing in Response to Groundings and Delays , 1997, J. Comb. Optim..

[15]  George L. Nemhauser,et al.  Air Transportation: Irregular Operations and Control , 2007 .

[16]  Cynthia Barnhart,et al.  The Global Airline Industry , 2009 .

[17]  Adib Kanafani,et al.  Real‐time decision support for integration of airline flight cancellations and delays part I: mathematical formulation , 1997 .

[18]  Tobias Andersson,et al.  Solving the flight perturbation problem with meta heuristics , 2006 .

[19]  Ladislav Lettovsky Airline operations recovery :an optimization approach , 1997 .

[20]  Knut Haase,et al.  Duty-period-based network model for crew rescheduling in European airlines , 2006, J. Sched..

[21]  Jon D. Petersen Large-scale mixed integer optimization approaches for scheduling airline operations under irregularity , 2012 .

[22]  Nikolaos Papadakos,et al.  Integrated airline scheduling , 2009, Comput. Oper. Res..

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

[24]  Jacques Desrosiers,et al.  The Operational Airline Crew Scheduling Problem , 1997, Transp. Sci..

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

[26]  Shangyao Yan,et al.  Multifleet routing and multistop flight scheduling for schedule perturbation , 1997 .

[27]  Ahmed F. Abdelghany,et al.  An integrated decision support tool for airlines schedule recovery during irregular operations , 2008, Eur. J. Oper. Res..

[28]  Shangyao Yan,et al.  Airline Scheduling for the Temporary Closure of Airports , 1997, Transp. Sci..

[29]  John-Paul Clarke,et al.  An Optimization Approach to Airline Integrated Recovery , 2012, Transp. Sci..

[30]  Michael D. D. Clarke Irregular airline operations: a review of the state-of-the-practice in airline operations control centers , 1998 .

[31]  Gang Yu,et al.  Special Issue: 2002 Franz Edelman Award for Achievement in Operations Research and the Management Sciences: A New Era for Crew Recovery at Continental Airlines , 2003, Interfaces.

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

[33]  George L. Nemhauser,et al.  Rerouting Aircraft for Airline Recovery , 2003, Transp. Sci..

[34]  Jonathan F. Bard,et al.  Balancing user preferences for aircraft schedule recovery during irregular operations , 2000 .

[35]  Dušan Teodorović,et al.  Model for operational daily airline scheduling , 1990 .

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