Resource-based vs. task-based approaches for scheduling problems

  • V. Brusoni
  • P. Terenziani
  • E. Lamma
  • P. Mello
  • L. Console
  • M. Milano
Communications Session 3B Knowledge Representation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1079)


Scheduling deals with the allocation of resources to activities over time, and can be interpreted as a Constraint Satisfaction Problem. Two main types of approaches, both based on constraint satisfaction, can be adopted to formulate a scheduling problem. The first type is resource-based: resources are associated with temporal domains of feasibility which describe and maintain the evolution of their state; the second type is task-based: the assignments of resources to activities are considered as temporally constrained events. This paper shows the use of the two approaches to a case study, the “Train Scheduling Problem”. The two approaches are then compared.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    J. Allen, “Maintaining Knowledge About Temporal Intervals”, in Communications of the ACM, vol. 26, 1983, pp. 832–843.Google Scholar
  2. 2.
    J.F.Allen, J.A.Koomen, “Planning using a Temporal World Model”, in Proc. of IJCAI83, 1983, pp. 741–747.Google Scholar
  3. 3.
    P.Baptiste, B.Legeard, M.A.Manier, C.Varnier, “A Scheduling Problem Optimization Solved with Constraint Logic Programming”, in Proc. Practical Applications of Prolog PAP94, pp. 47–66, London (UK), 1994.Google Scholar
  4. 4.
    S.Breitinger, H.C.R.Lock, “Modelling and Scheduling in CLP(FD)”, in Proc. Practical Applications of Prolog PAP94, pp. 95–110, London (UK), 1994.Google Scholar
  5. 5.
    Y.Caseau, F.Laburthe, “Improved CLP Scheduling with Task Intervals” in ICLP94, pp 369–383, S.Margherita (IT), 1994.Google Scholar
  6. 6.
    V. Brusoni, L. Console, B. Pernici, P. Terenziani, “Later: an efficient general purpose manager of temporal information”, to appear in IEEE Expert, 1996.Google Scholar
  7. 7.
    V. Brusoni, L. Console, P. Terenziani, “On the computational complexity of querying bounds on differences constraints”, in Artificial Intelligence, vol. 74, 1995, pp. 367–379.Google Scholar
  8. 8.
    N.Christodoulou, E.Stefanitsis, E.Kaltsas, V.Assimakopoulos, “A Constraint Logic Programming Approach to the Vehicle-Fleet Scheduling Problem”, in Proc. Practical Applications of Prolog PAP94, pp. 137–148, London (UK), 1994.Google Scholar
  9. 9.
    W.J.Cullyer, W.Wise, “Application of Formal Methods to Railway Signalling”, in Proc. of the Safety and Reliability Society Symposium, pp. 11–28, Bath (UK), 1989.Google Scholar
  10. 10.
    A.Dalfiume, E.Lamma, P.Mello, M.Milano, “A Constraint Logic Programming Application to a Distributed Train Scheduling Problem”, in Proc. Practical Applications of Prolog PAP95, Paris (FR),1995.Google Scholar
  11. 11.
    R.Dechter, I.Meiri, J.Pearl, “Temporal Constraint Networks”, in Artificial Intelligence, vol. 49, 1991, pp. 61–95.Google Scholar
  12. 12.
    ECL'PSe User Manual Release 3.3, ECRC 1992.Google Scholar
  13. 13.
    M.S.Fox, N.Sadeh, “Why is Scheduling Difficult? A CSP Perspective”, in Proc. of ECAI 90, pp.754–67, 1990.Google Scholar
  14. 14.
    E.C. Freuder, A.K. Mackworth, “Special Issue on Constraint Based Reasoning”, Artificial Intelligence, vol. 58, 1992.Google Scholar
  15. 15.
    J.Jaffar, J.L.Lassez, “Constraint Logic Programming”, in Proc. of the Conference on Principle of Programming Languages, Munich 1987.Google Scholar
  16. 16.
    J.Jaffar, M.J.Maher, “Constraint Logic Programming: a Survey”, in Journal of Logic Programming on 10 years of Logic Programming, 1994.Google Scholar
  17. 17.
    V. Kumar, “Algorithms for Constraint-Satisfaction Problems: A Survey”, in The AI Magazine, vol. 13, 1992, pp. 32–44.Google Scholar
  18. 18.
    E.Lamma, P.Mello, M.Milano, “A Meta Constraint Logic Programming Architecture for Qualitative and Quantitative Temporal Reasoning”, Technical Report DEIS-LIA-95-001, 1995.Google Scholar
  19. 19.
    A.Mascis, A.Sassano, “Job-Shop, No-Wait in process with Blocking Models for the Ordering of Trains within Big Railway Stations”, (in Italian), 1st Nat. Conf. C.N.R. “Progetto Finalizzato Trasporti II”, Vol. 4, pp. 2075–2094, Rome 1993.Google Scholar
  20. 20.
    C.Meng, M.Sullivan, “LOGOS: A Constraint-Directed Reasoning Shell for Operations Management”, IEEE Expert 6, 1, pp.20–28, 1991.Google Scholar
  21. 21.
    J.F.Rit, “Propagating Temporal Constraint for Scheduling”, in Proc. AAAI86, Philadelphia (PA), pp.383–388, August 1986.Google Scholar
  22. 22.
    S.F.Smith, P.S.Ow, “The Use of Multiple Problem Decomposition in Time Constrained Planning Tasks”, in Proc. of IJCAI85, pp.1013–1015.Google Scholar
  23. 23.
    S.F.Smith, “A Constraint-Based Framework for Reactive Management of Factory Schedules” in Intelligent Manufacturing, M.D.Oliff, Ed., Benjamin Cummins Publisher, 1987.Google Scholar
  24. 24.
    S.F.Smith, “Knowledge-based Production Management: Approaches, Results and Prospects”, in Production Planning and Control Journal, 1992.Google Scholar
  25. 25.
    P.Van Hentenryck, “Constraint Satisfaction in Logic Programming”, MIT Press, 1989.Google Scholar
  26. 26.
    D.S. Weld, “An Introduction to Least Commitment Planning”, in AI Magazine, vol. 15, 1994, pp. 27–61.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • V. Brusoni
    • 1
  • P. Terenziani
    • 1
  • E. Lamma
    • 2
  • P. Mello
    • 3
  • L. Console
    • 1
  • M. Milano
    • 2
  1. 1.Dip. InformaticaUniv. TorinoTorinoItaly
  2. 2.DEISUniv. BolognaBolognaItaly
  3. 3.Dip. IngegneriaUniv. FerraraFerraraItaly

Personalised recommendations