A Constraint Model for State Transitions in Disjunctive Resources

  • Roman Barták
  • Ondřej Čepek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4651)

Abstract

Traditional resources in scheduling are simple machines where the limited capacity is the main restriction. However, in practice there frequently appear resources with more complex behaviour that is described using state transition diagrams. This paper presents new filtering rules for constraints modelling the state transition diagrams. These rules are based on the idea of extending traditional precedence graphs by direct precedence relations. The proposed model also assumes optional activities and it can be used as an open model accepting new activities during the solving process.

Keywords

constraint domain filtering disjunctive resource state transition 

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References

  1. 1.
    Baptiste, P., Le Pape, C., Nuijten, W.: Constraint-Based Scheduling: Applying Constraint Programming to Scheduling Problems. Kluwer Academic Publishers, Dordrecht (2001)MATHGoogle Scholar
  2. 2.
    Barták, R.: Incremental Propagation of Time Windows on Disjunctive Resources. In: FLAIRS 2006. Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference, pp. 25–30. AAAI Press, Stanford (2006)Google Scholar
  3. 3.
    Beck, J.C., Fox, M.S.: Scheduling Alternative Activities. In: Proceedings of AAAI 1999, pp. 680–687. AAAI Press, USA (1999)Google Scholar
  4. 4.
    Cesta, A., Stella, C.: A Time and Resource Problem for Planning Architectures. In: Steel, S. (ed.) ECP 1997. LNCS, vol. 1348, pp. 117–129. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  5. 5.
    Fages, F.: CLP versus LS on log-based reconciliation problems for nomadic applications. In: Proceedings of ERCIM/CompulogNet Workshop on Constraints, Praha (2001)Google Scholar
  6. 6.
    Focacci, F., Laborie, P., Nuijten, W.: Solving Scheduling Problems with Setup Times and Alternative Resources. In: Proceedings of AIPS 2000, AAAI Press, Stanford (2000)Google Scholar
  7. 7.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, San Francisco (1979)MATHGoogle Scholar
  8. 8.
    Laborie, P.: Algorithms for propagating resource constraints in AI planning and scheduling: Existing approaches and new results. Artificial Intelligence 143, 151–188 (2003)MATHCrossRefGoogle Scholar
  9. 9.
    Laborie, P.: Resource temporal networks: Definition and complexity. In: IJCAI 2003. Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 948–953 (2003)Google Scholar
  10. 10.
    Moffitt, M.D., Peintner, B., Pollack, M.E.: Augmenting Disjunctive Temporal Problems with Finite-Domain Constraints. In: AAAI 2005. Proceedings of the 20th National Conference on Artificial Intelligence, pp. 1187–1192. AAAI Press, Stanford (2005)Google Scholar
  11. 11.
    Stergiou, K., Koubarakis, M.: Backtracking algorithms for disjunctions of temporal constraints. In: AAAI 1998. Proceedings of the 15th National Conference on Artificial Intelligence, pp. 248–253. AAAI Press, Stanford (1998)Google Scholar
  12. 12.
    Pardalos, P.M., Qian, T., Resende, M.G.: A greedy randomized adaptive search procedure for the feedback vertex set problem. Journal of Combinatorial Optimization 2, 399–412 (1999)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Roman Barták
    • 1
  • Ondřej Čepek
    • 1
    • 2
  1. 1.Charles University in Prague, Faculty of Mathematics and Physics, Malostranské nám. 2/25, 118 00 Praha 1Czech Republic
  2. 2.Institute of Finance and Administration, Estonská 500, 101 00 Praha 10Czech Republic

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