Abstract
The global cumulative constraint was proposed for modelling cumulative resources in scheduling problems for finite domain (FD) propagation. Since that time a great deal of research has investigated new stronger and faster filtering techniques for cumulative, but still most of these techniques only pay off in limited cases or are not scalable. Recently, the “lazy clause generation” hybrid solving approach has been devised which allows a finite domain propagation engine possible to take advantage of advanced SAT technology, by “lazily” creating a SAT model of an FD problem as computation progresses. This allows the solver to make use of SAT nogood learning and autonomous search capabilities. In this paper we show that using lazy clause generation where we model cumulative constraint by decomposition gives a very competitive implementation of cumulative resource problems. We are able to close a number of open problems from the well-established PSPlib benchmark library of resource-constrained project scheduling problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
PSPLib — project scheduling problem library, http://129.187.106.231/psplib/ (23.04.2009)
Aggoun, A., Beldiceanu, N.: Extending CHIP in order to solve complex scheduling and placement problems. Mathematical and Computer Modelling 17(7), 57–73 (1993)
Baptiste, P., Le Pape, C.: Constraint propagation and decomposition techniques for highly disjunctive and highly cumulative project scheduling problems. Constraints 5(1-2), 119–139 (2000)
Carlier, J., Pinson, E.: Jackson’s pseudo-preemptive schedule and cumulative scheduling problems. Discrete Applied Mathematics 145(1), 80–94 (2004)
Caseau, Y., Laburthe, F.: Cumulative scheduling with task intervals. In: Procs. of the 1996 Joint International Conference and Symposium on Logic Programming, pp. 363–377. MIT Press, Cambridge (1996)
Demeulemeester, E.L., Herroelen, W.S.: New benchmark results for the resource-constrained project scheduling problem. Management Science 43(11), 1485–1492 (1997)
El-Kholy, A.O.: Resource Feasibility in Planning. PhD thesis, Imperial College, University of London (1996)
Hartmann, S., Kolisch, R.: Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem. EJOR 127(2), 394–407 (2000)
Jussien, N., Barichard, V.: The PaLM system: explanation-based constraint programming. In: Proceedings of Techniques foR Implementing Constraint programming Systems, pp. 118–133 (2000)
Kolisch, R.: Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation. EJOR 90(2), 320–333 (1996)
Kolisch, R., Hartmann, S.: Experimental investigation of heuristics for resource-constrained project scheduling: An update. EJOR 174(1), 23–37 (2006)
Laborie, P.: Complete MCS-based search: Application to resource constrained project scheduling. In: Kaelbling, L.P., Saffiotti, A. (eds.) Proceedings IJCAI 2005, pp. 181–186. Professional Book Center (2005)
Liess, O., Michelon, P.: A constraint programming approach for the resource-constrained project scheduling problem. Annals of Operations Research 157(1), 25–36 (2008)
Marriott, K., Nethercote, N., Rafeh, R., Stuckey, P.J., Garcia de la Banda, M., Wallace, M.G.: The design of the Zinc modelling language. Constraints 13(3), 229–267 (2008)
Moskewicz, M.W., Madigan, C.F., Zhao, Y., Zhang, L., Malik, S.: Chaff: Engineering an efficient SAT solver. In: Design Automation Conference, pp. 530–535. ACM, New York (2001)
Nuijten, W.P.M.: Time and Resource Constrained Scheduling. PhD thesis, Eindhoven University of Technology (1994)
Ohrimenko, O., Stuckey, P.J., Codish, M.: Propagation = lazy clause generation. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 544–558. Springer, Heidelberg (2007)
Vilím, P.: Computing explanations for the unary resource constraint. In: Barták, R., Milano, M. (eds.) CPAIOR 2005. LNCS, vol. 3524, pp. 396–409. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schutt, A., Feydy, T., Stuckey, P.J., Wallace, M.G. (2009). Why Cumulative Decomposition Is Not as Bad as It Sounds. In: Gent, I.P. (eds) Principles and Practice of Constraint Programming - CP 2009. CP 2009. Lecture Notes in Computer Science, vol 5732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04244-7_58
Download citation
DOI: https://doi.org/10.1007/978-3-642-04244-7_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04243-0
Online ISBN: 978-3-642-04244-7
eBook Packages: Computer ScienceComputer Science (R0)