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Per-Seat, On-Demand Air Transportation Part I: Problem Description and an Integer Multicommodity Flow Model

Author

Listed:
  • D. Espinoza

    (School of Industrial Engineering, Universidad de Chile, Santiago, Chile)

  • R. Garcia

    (DayJet Corporation, Boca Raton, Florida 33431)

  • M. Goycoolea

    (School of Business, Universidad Adolfo Ibáñez, Santiago, Chile)

  • G. L. Nemhauser

    (H. Milton School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • M. W. P. Savelsbergh

    (H. Milton School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

Abstract

The availability of relatively cheap small jet planes has led to the creation of on-demand air transportation services in which travelers call a few days in advance to schedule a flight. A successful on-demand air transportation service requires an effective scheduling system to construct minimum-cost pilot and jet itineraries for a set of accepted transportation requests. We present an integer multicommodity network flow model with side constraints for such dial-a-flight problems. We develop a variety of techniques to control the size of the network and to strengthen the quality of the linear programming relaxation, which allows the solution of small instances. In Part II, we describe how this core optimization technology is embedded in a parallel, large-neighborhood, local search scheme to produce high-quality solutions efficiently for large-scale real-life instances.

Suggested Citation

  • D. Espinoza & R. Garcia & M. Goycoolea & G. L. Nemhauser & M. W. P. Savelsbergh, 2008. "Per-Seat, On-Demand Air Transportation Part I: Problem Description and an Integer Multicommodity Flow Model," Transportation Science, INFORMS, vol. 42(3), pages 263-278, August.
    • Handle: RePEc:inm:ortrsc:v:42:y:2008:i:3:p:263-278
    DOI: 10.1287/trsc.1070.0227
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    References listed on IDEAS

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    5. Ryerson, Megan S., 2010. "Optimal Intercity Transportation Services with Heterogeneous Demand and Variable Fuel Price," University of California Transportation Center, Working Papers qt8696z26t, University of California Transportation Center.
    6. Yves Molenbruch & Kris Braekers & An Caris, 2017. "Typology and literature review for dial-a-ride problems," Annals of Operations Research, Springer, vol. 259(1), pages 295-325, December.
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    9. Pillac, Victor & Gendreau, Michel & Guéret, Christelle & Medaglia, Andrés L., 2013. "A review of dynamic vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 225(1), pages 1-11.
    10. D. Espinoza & R. Garcia & M. Goycoolea & G. L. Nemhauser & M. W. P. Savelsbergh, 2008. "Per-Seat, On-Demand Air Transportation Part II: Parallel Local Search," Transportation Science, INFORMS, vol. 42(3), pages 279-291, August.
    11. Faramroze G. Engineer & George L. Nemhauser & Martin W. P. Savelsbergh, 2011. "Dynamic Programming-Based Column Generation on Time-Expanded Networks: Application to the Dial-a-Flight Problem," INFORMS Journal on Computing, INFORMS, vol. 23(1), pages 105-119, February.
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    13. I. Campbell & M. Montaz Ali & M. Silverwood, 2020. "Solving a dial-a-flight problem using composite variables," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 123-153, April.
    14. Munari, Pedro & Alvarez, Aldair, 2019. "Aircraft routing for on-demand air transportation with service upgrade and maintenance events: Compact model and case study," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 75-84.
    15. Michelle Dunbar & Simon Belieres & Nagesh Shukla & Mehrdad Amirghasemi & Pascal Perez & Nishikant Mishra, 2020. "A genetic column generation algorithm for sustainable spare part delivery: application to the Sydney DropPoint network," Annals of Operations Research, Springer, vol. 290(1), pages 923-941, July.
    16. Shah, Nirav & Kumar, Subodha & Bastani, Farokh & Yen, I-Ling, 2012. "Optimization models for assessing the peak capacity utilization of intelligent transportation systems," European Journal of Operational Research, Elsevier, vol. 216(1), pages 239-251.

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