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Developing a model for solving the flight perturbation problem

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http://dx.doi.org/10.3926/jairm.31
Abstract (2. Language): 
Purpose: In the aviation and airline industry, crew costs are the second largest direct operating cost next to the fuel costs. But unlike the fuel costs, a considerable portion of the crew costs can be saved through optimized utilization of the internal resources of an airline company. Therefore, solving the flight perturbation scheduling problem, in order to provide an optimized schedule in a comprehensive manner that covered all problem dimensions simultaneously, is very important. In this paper, we defined an integrated recovery model as that which is able to recover aircraft and crew dimensions simultaneously in order to produce more economical solutions and create fewer incompatibilities between the decisions. Design/methodology: Current research is performed based on the development of one of the flight rescheduling models with disruption management approach wherein two solution strategies for flight perturbation problem are presented: Dantzig-Wolfe decomposition and Lagrangian heuristic. Findings: According to the results of this research, Lagrangian heuristic approach for the DW-MPsolved the problem optimally in all known cases. Also, this strategy based on the Dantig-Wolfe decomposition manage to produce a solution within an acceptable time (Under 1 Sec). Originality/value: This model will support the decisions of the flight controllers in the operation centers for the airlines. When the flight network faces a problem the flight controllers achieve a set of ranked answers using this model thus, applying crew’s conditions in the proposed model caused this model to be closer to actual conditions.
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REFERENCES

References: 

Abdelghany, K.F., Abdelghany, A.F., & Ekollu, G. (2008). An Integrated Decision Support Tool
for Airlines Schedule Recovery During Irregular Operations. European Journal of Operational
Research, 185(2), 825-848. http://dx.doi.org/10.1016/j.ejor.2006.12.045
Andersson, T. (2006). Solving the flight perturbation problem with metaheuristics. Journal of
Heuristics, 12(1-2), 37-53. http://dx.doi.org/10.1007/s10732-006-4833-4
Andersson, T., & Värbrand, P. (2004). The Flight Perturbation Problem. Transportation Planning
and Technology, 27(2), 91-117. http://dx.doi.org/10.1080/0308106042000218195
Argüello, M., & Bard, J. (1997). A Grasp for Aircraft Routing in Response to Groundings and
Delays. Journal of Combinatorial Optimization, 5, 211-228.
http://dx.doi.org/10.1023/A:1009772208981
Bard, J., Yu, G., & Argüello, M. (2001). Optimizing Aircraft Routings in Response to Groundings
and Delays. IIE Transactions, 33, 931-947. http://dx.doi.org/10.1080/07408170108936885
Cao, J., & Kanafani, A. (1997a). Real-time Decision Support for Integration of Airline Flight
Cancellations and Delays, Part I: Mathematical Formulation. Transportation Planning and
Technology, 20, 189-199.
Cao, J., & Kanafani, A. (1997b). Real-time Decision Support for Integration of Airline Flight
Cancellations and Delays, Part II: Algorithm and Computational Experiments. Transportation
Planning and Technology, 20, 201-217. http://dx.doi.org/10.1080/03081069708717589
Caprara, A., Fischetti, M., & Toth, P. (1999). A heuristic method for the set covering problem.
Operation Research, 47, 730-743. http://dx.doi.org/10.1287/opre.47.5.730
Castro, A.M., & Oliveira, E. (2009). Quantifying Quality Operational Costs in a Multi-Agent
System for Airline Operations Recovery. International Review on Computers and Software
(IRECOS), 4(4), 504-516.
Castro, A.M. (2013). A Distributed Approach to Integrated and Dynamic Disruption
Management in Airline Operations Control. PhD Thesis. University of Porto, Porto, Portugal.
Deng, D., & Lin, W. (2011). Ant Colony optimization-based Algorithm for Airline Crew
Scheduling Problem. Expert Systems with Applications, 38, 5787-5793.
http://dx.doi.org/10.1016/j.eswa.2010.10.053
Dunbar, M., Froyland, G., & Wu, C. (2014). An Integrated Scenario-Based Approach for Robust
Aircraft Routing, Crew Pairing and Re-Timing. Computers & Operations Research, 45, 68-86.
http://dx.doi.org/10.1016/j.cor.2013.12.003
Gao, C., Johnson, E., & Smith, B. (2009). Integrated Airline Fleet and Crew Robust Planning.
Transportation Science, 43(1), 2-16. http://dx.doi.org/10.1287/trsc.1080.0257
Janic, M. (2009). Modelling Airport Operations Affected by a Large-Scale Disruption. Journal ofTransportation Engineering, 135(4), 206-216.
http://dx.doi.org/10.1061/(ASCE)0733-947X(2009)135:4(206)
Jarrah, I.Z., Yu, G., Krishnamurthy, N., & Rakshit, A. (1993). A Decision Support Framework for
Airline Flight Cancellations and Delays. Transportation Science, 27(3), 266-280.
http://dx.doi.org/10.1287/trsc.27.3.266
Kohla, N., Larsenb, A., Larsenc, J., Rossd, A., & Tiourine, S. (2007). Airline Disruption
Management—Perspectives, Experiences and Outlook. Journal of Air Transport Management,
13, 149-162. http://dx.doi.org/10.1016/j.jairtraman.2007.01.001
Lettovsky, L., Johnson, E., & Nemhauser, G. (2000). Airline Crew Recovery. Transportation
Science, 34, 37-48. http://dx.doi.org/10.1287/trsc.34.4.337.12316
Mathaisel, D.F.X. (1996). Decision Support for Airline System Operations Control and Irregular
Operations. Computers Ops Res., 23(11), 1083-1098.
http://dx.doi.org/10.1016/0305-0548(96)00007-X
Polyak, B.T. (1969). Minimization of unsmooth functional. USSR Computational Mathematics
and Mathematical Physics, 9, 14-29. http://dx.doi.org/10.1016/0041-5553(69)90061-5
Rafiei, F., Manzari, S., & Khashei, M. (2012). Scheduling FlightPerturbations with Ant Colony
Optimization Approach. International Journal of Computer Science and Artificial Intelligence,
2(2), 1-9. http://dx.doi.org/10.5963/IJCSAI0202001
Rakshit, A., Krishnamurthy, N., & Yu, G. (1996). A Real-Time Decision Support System for
Managing Airline Operations at United Airlines. Interfaces, 26(2), 50-58.
http://dx.doi.org/10.1287/inte.26.2.50
Saddoune, M., Desaulniers, G., Elhallaoui, I., & Soumis, F. (2012). Integrated Airline Crew
Pairing and Crew Assignment by Dynamic Constraint Aggregation. Transportation Science,
46(1), 39-55. http://dx.doi.org/10.1287/trsc.1110.0379
Sherali, H., Bae, K., & Haouari, M. (2013). An Integrated Approach for Airline Flight Selection
and Timing, Fleet Assignment, and Aircraft Routing. Transportation Science, 47(4), 455-476.
http://dx.doi.org/10.1287/trsc.2013.0460
Talluri, K.T. (1996). Swapping Applications in a Daily Airline Fleet Assignment. Transportation
Science, 30(3), 237-248. http://dx.doi.org/10.1287/trsc.30.3.237
Teodorović, D., & Guberinić, S. (1984). Optimal Dispatching Strategy on an Airline Network
after a Schedule Perturbation. European Journal of Operational Research, 15, 178-182.
http://dx.doi.org/10.1016/0377-2217(84)90207-8
Teodorović, D., & Stojković, S. (1990). Model for Operational Daily Airline Scheduling.
Transportation Planning and Technology, 14(4), 273-285.
http://dx.doi.org/10.1080/03081069008717431Teodorović, D.,& Stojković, S. (1995). Model to Reduce Airline Schedule Disturbances. Journal
of Transportation Engineering, 121, 324-331.
http://dx.doi.org/10.1061/(ASCE)0733-947X(1995)121:4(324)
Thengvall, B.G., Bard, J.F., & Yu, G. (2000). Balancing User Preferences for Aircraft Schedule
Recovery During Irregular Operations. IIE Transactions, 32(3), 181-193.
http://dx.doi.org/10.1080/07408170008963891
Wei, G., & Yu, G. (1997). Optimization Model and Algorithm for Crew Management During
Airline Irregular Operations. Journal of Combinatorial Optimization, 1, 305-321.
http://dx.doi.org/10.1023/A:1009780410798
Yan, S., & Yang, D. (1996) A Decision Support Framework for Handling Schedule Perturbations.
Transportation Research Part B, 30(6), 405-419. http://dx.doi.org/10.1016/0191-2615(96)00013-6

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