Journal of Intelligent Manufacturing

, Volume 21, Issue 1, pp 5–15 | Cite as

Constraint satisfaction techniques in planning and scheduling

  • Roman Barták
  • Miguel A. Salido
  • Francesca Rossi
Article

Abstract

Over the last few years constraint satisfaction, planning, and scheduling have received increased attention, and substantial effort has been invested in exploiting constraint satisfaction techniques when solving real life planning and scheduling problems. Constraint satisfaction is the process of finding a solution to a set of constraints. Planning is the process of finding a sequence of actions that transfer the world from some initial state to a desired state. Scheduling is the problem of assigning a set of tasks to a set of resources subject to a set of constraints. In this paper, we introduce the main definitions and techniques of constraint satisfaction, planning and scheduling from the Artificial Intelligence point of view.

Keywords

Constraint satisfaction Planning Scheduling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Apt K.R. (2003). Principles of constraint programming. Cambridge University Press, Cambridge CrossRefGoogle Scholar
  2. Baptiste P., Laborie P., Le Pape C., Nuijten W. (2006). Constraint-based scheduling and planning. In Handbook of Constraint Programming (pp. 761–799). Amsterdam: Elsevier.Google Scholar
  3. Baptiste, P., & Le Pape, C. (1996). Edge-finding constraint propagation algorithms for disjunctive and cumulative scheduling. In Proceedings of the 15th Workshop of the UK Planning and Scheduling Special Interest Group, Liverpool, UK.Google Scholar
  4. Baptiste, P., Le Pape, C., & Nuijten, W. (1995). Constraint-based optimization and approximation for job-shop scheduling. In Proceedings of the AAAI-SIGMAN Workshop on Intelligent Manufacturing Systems, IJCAI-95, Montreal, Canada.Google Scholar
  5. Baptiste, P., Le Pape, C., & Nuijten, W. (2001). Constraint-based scheduling. Springer.Google Scholar
  6. Barták, R. (1998). On-line guide to constraint programming. http://kti.mff.cuni.cz/~bartak/constraints/index.html.
  7. Barták, R. (2000). Toward mixed planning and scheduling. In Proceedings of CPDC’00 Workshop (Invited Talk), Stenungsund, Sweden.Google Scholar
  8. Barták R. (2005). Constraint satisfaction for planning and scheduling. In: Vlahavas, I. and Vrakas, D. (eds) Intelligent techniques for planning, pp 320–353. Idea Group, Hershey, PA Google Scholar
  9. Barták, R., & Toropila, D. (2008). Reformulating constraint models for classical planning. In Proceedings of the 21st International Florida AI Research Society Conference (FLAIRS 2008), Florida, USA, pp. 525–530.Google Scholar
  10. Bitner J.R. and Reingold E.M. (1975). Backtracking programming techniques. Communications of the ACM 18: 651–655 CrossRefGoogle Scholar
  11. Blum A. and Furst M. (1997). Fast planning through planning graph analysis. Artificial Intelligence 90: 281–300 CrossRefGoogle Scholar
  12. Dechter R. (2003). Constraint processing. Morgan Kaufmann, San Mateo, CA Google Scholar
  13. Dechter R., Meiri I. and Pearl J. (1991). Temporal constraint network. Artificial Intelligence 49: 61–95 CrossRefGoogle Scholar
  14. Edelkamp, S., Jabar, S., & Nazih, M. (2006). Large-scale optimal PDDL3 planning with MIPS-XXL. In 5th International Planning Competition Booklet (IPC-2006), Lake District, England, pp. 28–30.Google Scholar
  15. Frost, D., & Dechter, R. (1994). Dead-end driven learning. In Proceedings of the National Conference on Artificial Intelligence, Seattle, USA, pp. 294–300.Google Scholar
  16. Garrido, A., Salido, M. A., & Barber, F. (2000). Scheduling in a planning environment. In Proceedings of ECAI-2000 Workshop on New Results in Planning, Scheduling and Design (PUK-2000), Berlin, Germany, pp. 36–43.Google Scholar
  17. Gaschnig, J. (1977). A general backtrack algorithm that eliminates most redundant tests. In Procceedings of IJCAI, Cambridge, MA, USA, pp. 457.Google Scholar
  18. Gaschnig, J. (1979). Performance measurement and analysis of certain search algorithms. Technical Report CMU-CS-79-124, Carnegie-Mellon University.Google Scholar
  19. Gerevini A., Saetti A. and Serina I. (2006). An approach to temporal planning and scheduling in domains with predictable exogenous events. Journal of Artificial Intelligence Research 25: 187–231 Google Scholar
  20. Gerevini, A., & Serina, I. (2000). Fast plan adaptation through planning graphs: Local and systematic search techniques. In Proceedings of the Fifth International Conference on Artificial Intelligence Planning Systems, Breckenridge, CO, USA, pp. 112–121.Google Scholar
  21. Ghallab M., Nau D. and Traverso P. (2004). Automated planning: Theory and practice. Morgan Kaufmann, San Francisco, CA Google Scholar
  22. Graham R.L., Lawler E.L., Lenstra J.K. and Rinnooy-Kan A.H.G. (1979). Optimization and approximation in deterministic sequencing and scheduling: A survey. Annals of Discrete Mathematics 5: 287–326 CrossRefGoogle Scholar
  23. Halsey, K., Long, D., & Foz, M. (2004). CRIKEY—A temporal planner looking at the integration of planning and scheduling. In Proceedings on the ICAPS 2004 Workshop on Integrating Planning and Scheduling, Whistler, Canada, pp. 46–52.Google Scholar
  24. Haralick R. and Elliot G. (1980). Increasing tree efficiency for constraint satisfaction problems. Artificial Intelligence 14: 263–314 CrossRefGoogle Scholar
  25. Kautz, H., & Selman, B. (1992). Planning as satisfiability. In Proceedings of ECAI, Vienna, Austria, pp. 359–363.Google Scholar
  26. Laborie P. (2003). Algorithms for propagating resource constraints in AI planning and scheduling: Existing approaches and new results. Artificial Intelligence 143: 151–188 CrossRefGoogle Scholar
  27. Leung J.Y.T. (2004). Handbook of scheduling: Algorithms, models and performance analysis. Chapman & Hall, Boca Raton, FL Google Scholar
  28. Lhomme, O. (1993). Consistency techniques for numeric CSPs. In Proceedings of 13th International Joint Conference on Artificial Intelligence, Chambéry, France, pp. 232–238.Google Scholar
  29. Lopez, A., & Bacchus, F. (2003). Generalizing GraphPlan by formulating planning as a CSP. In Proceedings of IJCAI, Acapulco, Mexico, pp. 954–960.Google Scholar
  30. McGann, C., Py, F., Rajan, K., Ryan, J., & Henthorn, R. (2008). Adaptive control for autonomous underwater vehicles. In Proceedings of AAAI’08, Chicago, USA, pp. 1319–1324.Google Scholar
  31. Michalewicz Z. and Fogel D.B. (2000). How to solve it: Modern heuristics. Springer, Berlin Google Scholar
  32. Muscettola, N. (1993). HSTS: Integrating planning and scheduling. Technical Report CMU-RI-TR-93-05, Robotics Institute, Carnegie Mellon University.Google Scholar
  33. Muscettola N., Nayak P., Pell B. and Williams B. (1998). Remote agent: To boldly go where no AI system has gone before. Artificial Intelligence 103: 5–47 CrossRefGoogle Scholar
  34. Planken, L., de Weerdt, M., & Van der Krogt, R. (2008). P 3 C: A new algorithm for the simple temporal problem. In Proceedings of ICAPS-2008, Sydney, Australia, pp. 256–263.Google Scholar
  35. Prosser P. (1993). Hybrid algorithm for the constraint satisfaction problem. Computational Intelligence 9: 268–299 CrossRefGoogle Scholar
  36. Reiter R. (2001). Knowledge in action: Logical foundations for specifying and implementing dynamic systems. MIT Press, Cambridge, MA Google Scholar
  37. Rossi F., Walsh T. and Van Beek P. (2006). Handbook of constraint programming. Elsevier, Amsterdam Google Scholar
  38. Ruml, W., Do, M. B., & Fromherz, M. (2005). On-line planning and scheduling for high-speed manufacturing. In Proceedings of ICAPS’05, Monterey, USA, pp. 30–39.Google Scholar
  39. Ruttkay, Z. (1998). Constraint satisfaction—A survey. CWI Quarterly, 11(2&3), 123–162.Google Scholar
  40. Smith D.E., Frank J. and Jonsson A.K. (2000). Bridging the gap between planning and scheduling. Knowledge Enginering Review 15: 47–83 CrossRefGoogle Scholar
  41. Smith, S. F., & Cheng, Ch.-Ch. (1993). Slack-based heuristics for constraint satisfaction scheduling. In Proceedings of the National Conference on Artificial Intelligence (AAAI), Washington, USA, pp. 139–144.Google Scholar
  42. Smith, S. F., Lassila, O., & Becker, M. (1996). Configurable, mixed-initiative systems for planning and scheduling. In A. Tate (Ed.), Advanced planning (pp. 235–241). AAAI Press.Google Scholar
  43. Srivastava, B., & Kambhampati, S. (1999a). Efficient planning through separate resource scheduling. AAAI Spring Symposium on Search Techniques for Problem Solving under Uncertainty and Incomplete Information, Orlando, USA.Google Scholar
  44. Srivastava, B., & Kambhampati, S. (1999b). Scaling up planning by teasing out resource scheduling. In Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning, Durham, UK, pp. 172–186.Google Scholar
  45. Torres P. and Lopez P. (2000). On Not-First/Not-Last conditions in disjunctive scheduling. European Journal of Operational Research 127: 332–343 CrossRefGoogle Scholar
  46. Tsang E. (1993). Foundation of constraint satisfaction. Academic Press, London Google Scholar
  47. van Beek, P., & Chen, X. (1999). CPlan: A constraint programming approach to planning. In Proceedings of AAAI-99, Orlando, USA, pp. 585–590.Google Scholar
  48. Vidal, V., & Geffner, H. (2004). Branching and pruning: An optimal temporal POCL planner based on constraint programming. In Proceedings of AAAI-04, San Jose, USA, pp. 570–577.Google Scholar
  49. Vidal V. and Geffner H. (2006). Branching and prunning: An optimal temporal pocl planner based on constraint programming. Artificial Intelligence 170: 298–335 CrossRefGoogle Scholar
  50. Vilím, P. (2004). O(n log n) Filtering algorithms for unary resource constraint. In Proceedings of CPAIOR, Nice, France, pp. 335–347.Google Scholar
  51. Vilím P., Barták R. and Cepek O. (2005). Extension of O(n log n) filtering algorithms for the unary resource constraint to optional activities. Constraints 10(4): 403–425 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Roman Barták
    • 1
  • Miguel A. Salido
    • 2
  • Francesca Rossi
    • 3
  1. 1.Charles UniversityPragueCzech Republic
  2. 2.Universidad Politécnica de ValenciaValenciaSpain
  3. 3.University of PadovaPaduaItaly

Personalised recommendations