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A Technique for the Solution of Massive Set Covering Problems, with Application to Airline Crew Scheduling

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  • Jerrold Rubin

    (Philadelphia Scientific Center, IBM Corp., Philadelphia, Pennsylvania)

Abstract

Recent set covering algorithms have been able to solve problems for which the constraint matrix has as many as 10 columns. Unfortunately, in certain applications, the number of columns is combinatorially dependent on the number of rows, and can reach many orders of magnitude greater, for 500--1,000 rows. For these problems, the constraint matrix cannot be generated, much less solved, unless severe ad hoc limitations are imposed. It stems clear that we must reluctantly abandon the search for the true mathematical optimum in such cases. One method of attack is to use a set covering algorithm repeatedly on much smaller matrices extracted from the overall problem, generating columns as needed. Such an approach has been used on an airline crew-scheduling problem, with excellent practical success on test cases involving close to 1,000 rows. It utilizes some techniques that are more generally applicable, and some that make use of the structure of the crew-scheduling problem, and that therefore are specific to it.

Suggested Citation

  • Jerrold Rubin, 1973. "A Technique for the Solution of Massive Set Covering Problems, with Application to Airline Crew Scheduling," Transportation Science, INFORMS, vol. 7(1), pages 34-48, February.
    • Handle: RePEc:inm:ortrsc:v:7:y:1973:i:1:p:34-48
    DOI: 10.1287/trsc.7.1.34
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    Cited by:

    1. Desaulniers, G. & Desrosiers, J. & Dumas, Y. & Marc, S. & Rioux, B. & Solomon, M. M. & Soumis, F., 1997. "Crew pairing at Air France," European Journal of Operational Research, Elsevier, vol. 97(2), pages 245-259, March.
    2. Balaji Gopalakrishnan & Ellis. Johnson, 2005. "Airline Crew Scheduling: State-of-the-Art," Annals of Operations Research, Springer, vol. 140(1), pages 305-337, November.
    3. Sarin, Subhash C. & Aggarwal, Sanjay, 2001. "Modeling and algorithmic development of a staff scheduling problem," European Journal of Operational Research, Elsevier, vol. 128(3), pages 558-569, February.
    4. Lucic, Panta & Teodorovic, Dusan, 1999. "Simulated annealing for the multi-objective aircrew rostering problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(1), pages 19-45, January.
    5. Beasley, J. E. & Cao, B., 1996. "A tree search algorithm for the crew scheduling problem," European Journal of Operational Research, Elsevier, vol. 94(3), pages 517-526, November.
    6. E. Rod Butchers & Paul R. Day & Andrew P. Goldie & Stephen Miller & Jeff A. Meyer & David M. Ryan & Amanda C. Scott & Chris A. Wallace, 2001. "Optimized Crew Scheduling at Air New Zealand," Interfaces, INFORMS, vol. 31(1), pages 30-56, February.
    7. Wark, Peter & Holt, John & Ronnqvist, Mikael & Ryan, David, 1997. "Aircrew schedule generation using repeated matching," European Journal of Operational Research, Elsevier, vol. 102(1), pages 21-35, October.
    8. Monique Guignard & Ellis Johnson & Kurt Spielberg, 2005. "Logical Processing for Integer Programming," Annals of Operations Research, Springer, vol. 140(1), pages 263-304, November.
    9. Yan, Shangyao & Chang, Jei-Chi, 2002. "Airline cockpit crew scheduling," European Journal of Operational Research, Elsevier, vol. 136(3), pages 501-511, February.
    10. Joyce W. Yen & John R. Birge, 2006. "A Stochastic Programming Approach to the Airline Crew Scheduling Problem," Transportation Science, INFORMS, vol. 40(1), pages 3-14, February.

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