Advertisement

A Comparative Study of MIP and CP Formulations for the B2B Scheduling Optimization Problem

  • Gilles PesantEmail author
  • Gregory Rix
  • Louis-Martin Rousseau
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9075)

Abstract

The Business-to-Business Meeting Scheduling Problem was recently introduced to this community. It consists of scheduling meetings between given pairs of participants to an event while taking into account participant availability and accommodation capacity. The challenging aspect of this problem is that breaks in a participant’s schedule should be avoided as much as possible. In an earlier paper, starting from two generic CP and Pseudo-Boolean formulations, several solving approaches such as CP, ILP, SMT, and lazy clause generation were compared on real-life instances. In this paper we use this challenging problem to study different formulations adapted either for MIP or CP solving, showing that the cost_regular global constraint can be quite useful, both in MIP and CP, in capturing the problem structure.

Keywords

Time Slot Feasibility Problem Large Neighbourhood Search Fairness Constraint Backtrack Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Balas, E., Saltzman, M.J.: An Algorithm for the Three-Index Assignment Problem. Operations Research 39(1), 150–161 (1991)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Bofill, M., Espasa, J., Garcia, M., Palahí, M., Suy, J., Villaret, M.: Scheduling B2B Meetings. In: O’Sullivan, B. (ed.) CP 2014. LNCS, vol. 8656, pp. 781–796. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  3. 3.
    Côté, M.-C., Gendron, B., Rousseau, L.-M.: Modeling the Regular Constraint with Integer Programming. In: Van Hentenryck, P., Wolsey, L.A. (eds.) CPAIOR 2007. LNCS, vol. 4510, pp. 29–43. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  4. 4.
    Demassey, S., Pesant, G., Rousseau, L.-M.: A Cost-Regular Based Hybrid Column Generation Approach. Constraints 11(4), 315–333 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Harvey, W.D., Ginsberg, M.L.: Limited Discrepancy Search. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI 1995, Montréal Québec, Canada, August 20–25, 1995, vol. 2, pp. 607–615. Morgan Kaufmann (1995)Google Scholar
  6. 6.
    Pesant, G., Quimper, C.-G., Zanarini, A.: Counting-Based Search: Branching Heuristics for Constraint Satisfaction Problems. J. Artif. Intell. Res. (JAIR) 43, 173–210 (2012)zbMATHMathSciNetGoogle Scholar
  7. 7.
    Refalo, P.: Impact-Based Search Strategies for Constraint Programming. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 557–571. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  8. 8.
    Régin, J.-C.: Generalized Arc Consistency for Global Cardinality Constraint. In: Proceedings of the Thirteenth National/Eighth Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence, AAAI-98/IAAI-98, vol. 1, pp. 209–215 (1996)Google Scholar
  9. 9.
    Shaw, P.: Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems. In: Maher, M.J., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 417–431. Springer, Heidelberg (1998) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Gilles Pesant
    • 1
    • 2
    Email author
  • Gregory Rix
    • 1
    • 2
  • Louis-Martin Rousseau
    • 1
    • 2
  1. 1.École Polytechnique de MontréalMontréalCanada
  2. 2.Interuniversity Research Centre on Enterprise Networks, Logistics and TransportationMontréalCanada

Personalised recommendations