Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Airline Disruption Management Using Ant Colony Optimization Algorithm with Re-timing Strategy

항공사 비정상 운항 복구를 위한 리-타이밍 전략과 개미군집최적화 알고리즘 적용

  • Kim, Gukhwa (School of Air Transport, Transportation, and Logistics, Korea Aerospace University) ;
  • Chae, Junjae (School of Air Transport, Transportation, and Logistics, Korea Aerospace University)
  • 김국화 (한국항공대학교 항공교통물류학부) ;
  • 채준재 (한국항공대학교 항공교통물류학부)
  • Received : 2017.02.17
  • Accepted : 2017.05.15
  • Published : 2017.06.30
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Abstract

Airline schedules are highly dependent on various factors of uncertainties such as unfavorable weather conditions, mechanical problems, natural disaster, airport congestion, and strikes. If the schedules are not properly managed to cope with such disturbances, the operational cost and performance are severely affected by the delays, cancelations, and so forth. This is described as a disruption. When the disruption occurs, the airline requires the feasible recovery plan returning to the normal operations in a timely manner so as to minimize the cost and impact of disruptions. In this research, an Ant Colony Optimization (ACO) algorithm with re-timing strategy is developed to solve the recovery problem for both aircraft and passenger. The problem consists of creating new aircraft routes and passenger itineraries to produce a feasible schedule during a recovery period. The suggested algorithm is based on an existing ACO algorithm that aims to reflect all the downstream effects by considering the passenger recovery cost as a part of the objective function value. This algorithm is complemented by re-timing strategy to effectively manage the disrupted passengers by allowing delays even on some of undisrupted flights. The delays no more than 15 minutes are accepted, which does not influence on the on-time performance of the airlines. The suggested method is tested on the real data sets from 2009 ROADEF Challenge, and the computational results are compared with the existing ones on the same data sets. The method generates the solution for most of problem set in 10 minutes, and the result generated by re-timing strategy is discussed for its impact.

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References

  1. Abdelghany, A., Ekollu, G., Narasimhan, R., and Abdelghany, K., A Proactive Crew Recovery Decision Support Tool for Commercial Airlines during Irregular Operations, Ann. Oper. Res., 2004, Vol. 127, No. 1-4, pp. 309-331. https://doi.org/10.1023/B:ANOR.0000019094.19940.41
  2. Aloulou, M.A., Haouari, M., and Mansour, F.Z., Robust aircraft routing and flight retiming, Electron. Notes Discret. Math., 2010, Vol. 36, No. C, pp. 367-374. https://doi.org/10.1016/j.endm.2010.05.047
  3. Ball, M., Barnhart, C., Nemhauser, G., and Odoni, A., Chapter 1 Air Transportation : Irregular Operations and Control, Handbooks in Operations Research and Management Science, 2007, Vol. 14, No. C, pp. 1-67.
  4. Bratu, S. and Barnhart, C., Flight operations recovery : New approaches considering passenger recovery, J. Sched., 2006, Vol. 9, No. 3, pp. 279-298. https://doi.org/10.1007/s10951-006-6781-0
  5. Castro, A. J. M. and Oliveira, E., Airline Operations Control : A New Concept for Operations Recovery, Airline Industry : Operations and Safety, 2011.
  6. Chen, X., Chen, X., and Zhang, X., Crew scheduling models in airline disruption management, in 2010 IEEE 17th International conference, 2010, pp. 1032-1037.
  7. Clarke, M.D.D., Irregular airline operations : a review of the state-of-the-practice in airline operations control centers, J. Air Transp. Manag., 1998, Vol. 4, No. 2, pp. 67-76. https://doi.org/10.1016/S0969-6997(98)00012-X
  8. Clausen, J., Larsen, A., Larsen, J., and Rezanova, N.J., Disruption management in the airline industry-Concepts, models and methods, Comput. Oper. Res., 2010, Vol.37, No. 5, pp. 809-821. https://doi.org/10.1016/j.cor.2009.03.027
  9. Dorigo, M. and Di Caro, G., Ant colony optimization : a new meta-heuristic, Proc. 1999 Congr. Evol. Comput. (Cat. No. 99TH8406), 1999, Vol. 2, pp. 1470-1477.
  10. Jafari, N. and Zegordi, S.H., Simultaneous recovery model for aircraft and passengers, in Journal of the Franklin Institute, 2011, Vol. 348, No. 7, pp. 1638-1655. https://doi.org/10.1016/j.jfranklin.2010.03.012
  11. Jarrah, A.I., Yu, G., Krishnamurthy, N., and Rakshit, A., A Decision Support Framework for Airline Flight Cancellations and Delays, Transp. Sci., 1993, Vol. 27, No. 3, pp. 266-280. https://doi.org/10.1287/trsc.27.3.266
  12. Kohl, N., Larsen, A., Larsen, J., Ross, A., and Tiourine, S., Airline disruption management-Perspectives, experiences and outlook, J. Air Transp. Manag., 2007, Vol. 13, No. 3, pp. 149-162. https://doi.org/10.1016/j.jairtraman.2007.01.001
  13. Lan, S., Clarke, J.P., and Barnhart, C., Planning for Robust Airline Operations : Optimizing Aircraft Routings and Flight Departure Times to Minimize Passenger Disruptions, Transp. Sci., 2006, Vol. 40, No. 1, pp. 15-28. https://doi.org/10.1287/trsc.1050.0134
  14. Ministry of Land, Infrastructure, and Transport., Reduced inconveniences for airline passenger due to flight delays, 2016, [Online] : Available: https://www.molit.go.kr/USR/NEWS/m_71/dtl.jsp?id=95078299.
  15. Niehues, A., Berlin, S., Hansson, T., Hauser, R., Mostajo, M., and Richter, J., Punctuality : How Airlines Can Improve On-Time Performance, Booz Allen Hamilt., 2001.
  16. Petersen, J.D., Solveling, G., Clarke, J.P., Johnson, E.L., and Shebalov, S., An Optimization Approach to Airline Integrated Recovery, Transp. Sci., 2012, Vol. 46, No. 4, pp. 482-500. https://doi.org/10.1287/trsc.1120.0414
  17. Stojkovic, G., Soumis, F., Desrosiers, J., and Solomon, M.M., An optimization model for a real-time flight scheduling problem, Transp. Res. Part A Policy Pract., 2002, Vol. 36, No. 9, pp. 779-788. https://doi.org/10.1016/S0965-8564(01)00039-8
  18. Talluri, K.T., Swapping Applications in a Daily Airline Fleet Assignment, Transp. Sci., 1996, Vol. 30, No. 3, pp. 237-248. https://doi.org/10.1287/trsc.30.3.237
  19. Teodorvic, D., Airline Operations Research, Gordon and Breach Science Publishers, 1988.
  20. Yan, S. and Lin, C.G., Airline Scheduling for the Temporary Closure of Airports, Transp. Sci., 1997, Vol. 31, No. 1, pp. 72-82. https://doi.org/10.1287/trsc.31.1.72
  21. Yan, S. and Yang, D.H., A decision support framework for handling schedule perturbation, Transp. Res. Part B Methodol., 1996, Vol. 30, No. 6, pp. 405-419. https://doi.org/10.1016/0191-2615(96)00013-6
  22. Yun, H.K., Lee, J.J., and Lee, J.C., A Study on Development of the Aircraft Scheduling System Using ILOG, J. Soc. Korea Ind. Syst. Eng., 2001, Vol. 24, No. 62, pp. 89-102.
  23. Zegordi, S.H. and Jafari, N., Solving the Airline Recovery Problem By Using Ant Colony Optimization, Int. J. Ind. Eng. Prod. Res., 2010, Vol. 21, No. 3, pp. 121-128.