A Scenario Aggregation–Based Approach for Determining a Robust Airline Fleet Composition for Dynamic Capacity Allocation

Published Online:https://doi.org/10.1287/trsc.1040.0097

References

  • Abara J. Applying integer linear programming to the fleet assignment problem. Interfaces (1989) 19(4):20–28LinkGoogle Scholar
  • Airbus Industry (2002) . www.airbus.comGoogle Scholar
  • Anbil R., Barahona F., Ladanyi L., Rushmeier R., Snowdon J. Airline optimization. ORMS Today (1999) 26(6):26–29Google Scholar
  • Barnhart C., Kniker T. S., Lohatepanont M. Itinerary-based airline fleet assignment. Transportation Sci. (2002) 36(2):199–217LinkGoogle Scholar
  • Barnhart C., Boland N., Clarke L., Johnson E. L., Nemhauser G. L., Shenoi R. G. Flight string models for aircraft fleeting and routing. Transportation Sci. (1998) 32(3):208–220LinkGoogle Scholar
  • Berge M. E., Hopperstad C. A. Demand driven dispatch: A method for dynamic aircraft capacity assignment, models and algorithms. Operations Res. (1993) 41(1):153–168LinkGoogle Scholar
  • Feldman J. M. Matching planes to people. Air Transport World (2002) 39(12):31–33Google Scholar
  • Ferguson A. R., Dantzig G. B. The allocation of aircraft to routes—An example of linear programming under uncertain demand. Management Sci. (1956) 3:45–73LinkGoogle Scholar
  • Gu Z., Johnson E. L., Nemhauser G. L., Wang Y. Some properties of the fleet assignment problem. Operations Res. Lett. (1994) 15(2):59–71CrossrefGoogle Scholar
  • Hane C. A., Barnhart C., Johnson E. L., Marsten R. E., Nemhauser G. L., Sigismondi G. The fleet assignment problem: Solving a large-scale integer program. Math. Programming (1995) 70(2):211–232CrossrefGoogle Scholar
  • ILOGILOG CPLEX 7.0 User’s Manual (2000) (Mountain View, CA) . http://www.ilog.comGoogle Scholar
  • Jönsson H., Silver E. A. Some insights regarding selecting sets of scenarios in combinatorial stochastic programs. Internat. J. Production Econom. (1996) 45(1–3):463–472CrossrefGoogle Scholar
  • Kleywegt A., Shapiro A., Homem-de-Mello T. The sample average approximation method for stochastic discrete optimization. SIAM J. Optim. (2001) 12:479–502CrossrefGoogle Scholar
  • Linderoth J., Shapiro A., Wright S. The empirical behavior of sampling methods for stochastic programming. (2002) . Technical Report 02-01, Computer Science Department, University of Wisconsin-Madison, Madison, WI (published electronically in Optimization Online, www.optimization-online.orgGoogle Scholar
  • Mulvey J. M., Vladimirou H. Applying the progressive hedging algorithm to stochastic generalized networks. Ann. Oper. Res. (1991) 31:399–424CrossrefGoogle Scholar
  • ORTEC (2002) . OR TEC. Gouda, The Netherlands http://www.ortec.comGoogle Scholar
  • Rockafellar R. T., Wets R. J. B. Scenarios and policy aggregation in optimization under uncertainty. Math. Oper. Res. (1991) 16(1):119–147LinkGoogle Scholar
  • Rosenberger J. M., Johnson E. L., Nemhauser G. L. A robust fleet assignment model with hub isolation and short cycles. (2001) . Technical Report TLI-LEC 01-12, Georgia Institute of Technology, Atlanta, GA, http://www.isye.gatech.edu/simair/Google Scholar
  • Rushmeier R. A., Kontogiorgis S. A. Advances in the optimization of airline fleet assignment. Transportation Sci. (1997) 31(2):159–169LinkGoogle Scholar
  • Saliby E. Descriptive sampling: A better approach to Monte Carlo simulation. J. Oper. Res. Soc. (1990) 41(12):1133–1142Google Scholar
  • Shapiro A., Homem-de-Mello T. On the rate of convergence of optimal solutions of monte carlo approximations of stochastic programs. SIAM J. Optim. (2000) 11:70–86CrossrefGoogle Scholar
  • Subramanian R., Scheff R. P., Quillinan J. D., Wiper D. S., Marsten R. E. Coldstart: Fleet assignment at Delta Air Lines. Interfaces (1994) 24(1):104–120LinkGoogle Scholar
  • Talluri K. T. Swapping applications in a daily airline fleet assignment. Transportation Sci. (1996) 30(3):237–248LinkGoogle Scholar
  • Wets R. J. B., Wallace S. W. The aggregation principle in scenario analysis and stochastic optimization. Algorithms and Model Formulations in Mathematical Programming (1989) 51(Springer-Verlag, Berlin, Germany) 91–113NATO ASI Series F: Computer and Systems SciencesCrossrefGoogle Scholar
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