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Using Constraint Programming for the Urban Transit Crew Rescheduling Problem

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Principles and Practice of Constraint Programming (CP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9892))

Abstract

Scheduling urban and trans-urban transportation is an important issue for industrial societies. The Urban Transit Crew Scheduling Problem is one of the most important optimization problem related to this issue. It mainly relies on scheduling bus drivers’ workday respecting both collective agreements and the bus schedule needs. If this problem has been intensively studied from a tactical point of view, its operational aspect has been neglected while the problem becomes more and more complex and more and more prone to disruptions. In this way, this paper presents how the constraint programming technologies are able to recover the tactical plans at the operational level in order to efficiently help in answering regulation needs after disruptions.

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Notes

  1. 1.

    http://www.trapezegroup.com.

  2. 2.

    http://www.giro.ca.

  3. 3.

    http://www.goalsystems.com.

  4. 4.

    http://www.eurodecision.fr.

References

  1. Beldiceanu, N., Carlsson, M., Flener, P., Lorca, X., Pearson, J., Petit, T., Prud’Homme, C.: A modelling pearl with sortedness constraints. In: Gottlob, G., Sutcliffe, G., Voronkov, A., (eds.), GCAI 2015, Global Conference on Artificial Intelligence, vol. 36. EPiC Series in Computing, pp. 27–41. EasyChair (2015)

    Google Scholar 

  2. Caprara, A., Toth, P., Fischetti, M.: Algorithms for the set covering problem. Ann. Oper. Res. 98(1–4), 353–371 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  3. Chen, S., Shen, Y.: An improved column generation algorithm for crew scheduling problems. J. Inf. Comput. Sci. 10(1), 175–183 (2013)

    Google Scholar 

  4. Desaulniers, G., Desrosiers, J., Solomon, M.M. (eds.): Column Generation, vol. 5. Springer Science & Business Media, Heidelberg (2006)

    MATH  Google Scholar 

  5. Desrochers, M., Soumis, F.: A column generation approach to the urban transit crew scheduling problem. Transp. Sci. 23(1), 1–13 (1989)

    Article  MATH  Google Scholar 

  6. Feillet, D., Dejax, P., Gendreau, M., Gueguen, C.: An exact algorithm for the elementary shortest path problem with resource constraints: application to some vehicle routing problems. Networks 44(3), 216–229 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  7. Forsyth, P., Wren, A.: An ant system for bus driver scheduling (1997)

    Google Scholar 

  8. Franck, B., Neumann, K., Schwindt, C.: Truncated branch-and-bound, schedule-construction, and schedule-improvement procedures for resource-constrained project scheduling. OR-Spektrum 23(3), 297–324 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  9. Gent, I.P., Jefferson, C., Miguel, I., Nightingale, P.: Data structures for generalised arc consistency for extensional constraints. In: Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 22–26 July 2007, Vancouver, British Columbia, Canada, pp. 191–197. AAAI Press (2007)

    Google Scholar 

  10. Irnich, S., Desaulniers, G., et al.: Shortest path problems with resource constraints. Column Gener. 6730, 33–65 (2005)

    Article  MATH  Google Scholar 

  11. Jacquet-Lagrèzel, É.: Horaires de chauffeurs de bus. Gestion de production et ressources humaines: méthodes deplanification dans les systèmes productifs, p. 287 (2005)

    Google Scholar 

  12. Lourenço, H.R., Paixão, J.P., Portugal, R.: Multiobjective metaheuristics for the bus driver scheduling problem. Transportation Sci. 35(3), 331–343 (2001)

    Article  MATH  Google Scholar 

  13. Perron, L.: Fast restart policies and large neighborhood search. In: Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (2003)

    Google Scholar 

  14. Pesant, G.: A regular language membership constraint for finite sequences of variables. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 482–495. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  15. Prud’homme, C., Fages, J.-G., Lorca, X.: Choco Documentation. TASC, INRIA Rennes, LINA CNRS UMR 6241, COSLING S.A.S (2015)

    Google Scholar 

  16. Régin, J.-C.: A filtering algorithm for constraints of difference in CSPs. In: Proceedings of the 12th National Conference on Artificial Intelligence, Seattle, WA, USA, July 31 - August 4, 1994, vol. 1, pp. 362–367. AAAI Press/The MIT Press (1994)

    Google Scholar 

  17. Régin, J.-C.: Generalized arc consistency for global cardinality constraint. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, AAAI 1996, IAAI 1996, Portland, Oregon, 4-8 August 1996, vol. 1, pp. 209–215. AAAI Press/The MIT Press (1996)

    Google Scholar 

  18. Shaw, Paul: Using constraint programming and local search methods to solve vehicle routing problems. In: Maher, Michael J., Puget, Jean-François (eds.) CP 1998. LNCS, vol. 1520, pp. 417–431. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  19. Shen, Y., Kwan, R.S.K.: Tabu search for driver scheduling. In: Voß, S., Daduna, J.R. (eds.) Computer-Aided Scheduling of Public Transport, vol. 505, pp. 121–135. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  20. Silva, G.P., Reis, A.F.S.: A study of different metaheuristics to solve the urban transit crew scheduling problem. J. Transport Lit. 8(4), 227–251 (2014)

    Article  Google Scholar 

  21. Yunes, T.H., Moura, A.V., de Souza, C.C.: A hybrid approach for solving large scale crew scheduling problems. In: Pontelli, E., Santos Costa, V. (eds.) PADL 2000. LNCS, vol. 1753, pp. 293–307. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

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Acknowledgements

The authors are supported by the French common-laboratory grant “TransOp” involving the TASC team and EURODECISION.

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Correspondence to Xavier Lorca .

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Lorca, X., Prud’homme, C., Questel, A., Rottembourg, B. (2016). Using Constraint Programming for the Urban Transit Crew Rescheduling Problem. In: Rueher, M. (eds) Principles and Practice of Constraint Programming. CP 2016. Lecture Notes in Computer Science(), vol 9892. Springer, Cham. https://doi.org/10.1007/978-3-319-44953-1_40

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  • DOI: https://doi.org/10.1007/978-3-319-44953-1_40

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