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A New Yield Management Approach for Continuous Multi-Variable Bookings: The Airline Cargo Case

  • Stefano Gliozzi
  • Alberto Maria Marchetti
Chapter
Part of the Applied Optimization book series (APOP, volume 79)

Abstract

Yield Management is a well-known methodology whose main goal is to maximize a parameter (usually the revenue, but sometimes the profit, the yield, or the load factor) that can be obtained from a given supplied perishable resource under a stochastic demand which will pay different prices. Here a new Yield Management method is described, particularly suited in multi-variable environments, where more than one resource defines the capacity to be optimized. A typical case is the airline cargo, where each reservation uses simultaneously both the resources that a cargo plane offers: volume and weight. This innovative Yield Management method can be employed in the usual mono-dimensional environment of seat optimization as well, and its peculiarity is to leverage the stochastic linear programming technology based on scenarios, to take into account not only the randomness of the demand, but also the underlying relationship among all the intervening stochastic variables. For example in a cargo environment, the stochastic nature of the revenue is considered together with the demand uncertainty in weight and volume. This implies that many simplifications, adopted by other solutions to overcome the complexity of the problem, can be avoided, obtaining a higher accuracy, while the method remains intrinsically consistent, robust and secure.

Yield Management can be approached in several ways; thus a short introduction to Yield Management is reported to give the correct positioning of our solution with respect to others. Then the method is described with reasonable detail together with a discussion of its advantages.

Keywords

yield management revenue management stochastic linear programming forecasting systems 

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References

  1. [1]
    Belobaba P.P. Airline yield management: an overview of seat inventory control, Transportation Science, 21:(2), 63–73, 1987.CrossRefGoogle Scholar
  2. [2]
    Belobaba P. P. Application of a probabilistic decision model to airline seat inventory control, Operations Research , 37: (2), 183–197, 1989.CrossRefGoogle Scholar
  3. [3]
    Belobaba P. P., Farkas A. Yield management impacts on airline spill estimation, Transportation Science, 33:(2), 217–232, 1999.zbMATHCrossRefGoogle Scholar
  4. [4]
    Botimer T. C. Efficiency considerations in airline pricing and yield management, Transportation Research, part A: policy and practice, 30: (4), 307–317, 1996.CrossRefGoogle Scholar
  5. [5]
    Cappelletti S., Carcieri M., Falcomatà S., Paoletti B. Airline recovery model, the model suite for the real-time management of the operational irregularities, in this book, 305–331.Google Scholar
  6. [6]
    Ciancimino A., Inzerillo G., Lucidi S, Palagi L. Mathematical programming approach for the solution of the railway yield management problem, Transportation Science, 33:(2), 168–181, 1999.zbMATHCrossRefGoogle Scholar
  7. [7]
    Gliozzi S., Marchetti A.M. A new yield management method for multi-variable environments, US Patent Pending 2001 for IBM, ref. GB9–2001–0034, 2001.Google Scholar
  8. [8]
    Kasilingam R. G. Air cargo revenue management: characteristics and complexities, European Journal of Operational Research, 96, 36–44, 1996.CrossRefGoogle Scholar
  9. [9]
    Kraft E. R., Srikar B. N., Phillips R. L. Revenue management in railroad applications, Transportation Quarterly, 54: (1), 157–176, 2000.Google Scholar
  10. [10]
    Kuyumcu A., Garcia-Diaz A. Polyhedral graph theory approach to revenue management in the airline industry, Computers and Industrial Engineering, 38:(3), 375–396, 2000.CrossRefGoogle Scholar
  11. [11]
    Li M.Z.F. Airline yield management and fare pricing: the state of the art and new challenges, Proceedings of the First Asia Pacific Decision Sciences Institute Conference, 2, 467–477, 1996.zbMATHGoogle Scholar
  12. [12]
    McGill J.I., Van Ryzin G.J. Revenue management: research overview and prospects, Transportation Science, 33: (2), 233–256, 1999.zbMATHCrossRefGoogle Scholar
  13. [13]
    Mitev N. N. A comparative analysis of information technology strategy in American Airlines and French Railways, Proceedings of the Thirty First Hawaii International Conference on System Sciences, (cat.98TB100216), 6, 611–621, 1998.CrossRefGoogle Scholar
  14. [14]
    Selby D. A. Materialisation forecasting: a data mining perspective, in this book, 393–406.Google Scholar
  15. [15]
    Sen S., Higle J.L. An introductory tutorial on stochastic linear programming models, Interfaces, 29:(2), 33–61, 1999.CrossRefGoogle Scholar
  16. [16]
    Stanger M. An introduction to revenue management, Operations Research, keynote & tutorial papers, presented at the Young OR Conference, 93–102, 1998.Google Scholar
  17. [17]
    Strasser S. Effect of yield management on railroads, Transportation Quarterly, 50:(2), 47–55, 1996.MathSciNetGoogle Scholar
  18. [18]
    Subramanian J., Stidham S.Jr. Airline yield management with overbooking, cancellations, and no-shows, Transportation Science, 33:(2), 147–167, 1999.zbMATHCrossRefGoogle Scholar
  19. [19]
    Talluri K., van Ryzin G. Analysis of bid-price controls for network revenue management, Management Science, 44: (11), 1577–1593, 1998.zbMATHCrossRefGoogle Scholar
  20. [20]
    van Ryzin G., McGill J. Revenue management without forecasting or optimization: an adaptive algorithm for determining airline seat production levels, Management Science, 46: (6), 760–775, 2000.zbMATHCrossRefGoogle Scholar
  21. [21]
    Van Slyke R., Young Y. Finite horizon stochastic knapsacks with applications to yield management, Operations Research, 48:(1), 155–172, 2000.MathSciNetzbMATHCrossRefGoogle Scholar
  22. [21]
    Vinod B. Airline yield management: the significance of origin and destination inventory control, Presented at Agifors Reservations and Yield Management Study Group 11.3.1996, Zurich, Switzerland, 1–8, 1996.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • Stefano Gliozzi
    • 1
  • Alberto Maria Marchetti
    • 2
  1. 1.Business Consulting Services, CRM SolutionsIBMUSA
  2. 2.Business Consulting Services, Travel and Transportation SectorIBMUSA

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