Skip to main content
SpringerLink
Log in

Parallelizing Constraint Solvers for Hard RCPSP Instances

  • Conference paper
  • First Online:
Learning and Intelligent Optimization (LION 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10079))

Included in the following conference series:

Abstract

The Resource-Constrained Project Scheduling Problem (RCPSP) is a well-known scheduling problem aimed at minimizing the makespan of a project subject to temporal and resource constraints. In this paper we show that hard RCPSPs can be efficiently tackled by a portfolio approach that combines the strengths of different constraint solvers Our approach seeks to predict and run in parallel the best solvers for a new, unseen RCPSP instance by enabling the bound communication between them. This on-average allows to outperform the oracle solver that always chooses the best available solver for any given instance.

Supported by the EU project FP7-644298 HyVar: Scalable Hybrid Variability for Distributed, Evolving Software Systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Available at https://github.com/MiniZinc/minizinc-benchmarks/blob/master/rcpsp/rcpsp.mzn.

  2. 2.

    For more details about sunny-cp, we refer the reader to [3].

References

  1. Amadini, R., Gabbrielli, M., Mauro, J.: Portfolio approaches for constraint optimization problems. In: Pardalos, P.M., Resende, M.G.C., Vogiatzis, C., Walteros, J.L. (eds.) LION 2014. LNCS, vol. 8426, pp. 21–35. Springer, Heidelberg (2014). doi:10.1007/978-3-319-09584-4_3

    Google Scholar 

  2. Amadini, R., Gabbrielli, M., Mauro, J.: SUNNY: a Lazy portfolio approach for constraint solving. TPLP 4–5, 509–524 (2014)

    MATH  Google Scholar 

  3. Amadini, R., Gabbrielli, M., Mauro, J.: A multicore tool for constraint solving. In: IJCAI, pp. 232–238 (2015)

    Google Scholar 

  4. Amadini, R., Gabbrielli, M., Mauro, J.: SUNNY-CP: a sequential CP portfolio solver. In: SAC, pp. 1861–1867 (2015)

    Google Scholar 

  5. Amadini, R., Gabbrielli, M., Mauro, J.: Why CP portfolio solvers are (under)utilized? Issues and challenges. In: Falaschi, M. (ed.) LOPSTR 2015. LNCS, vol. 9527, pp. 349–364. Springer, Heidelberg (2015). doi:10.1007/978-3-319-27436-2_21

    Chapter  Google Scholar 

  6. Amadini, R., Stuckey, P.J.: Sequential time splitting and bounds communication for a portfolio of optimization solvers. In: O’Sullivan, B. (ed.) CP 2014. LNCS, vol. 8656, pp. 108–124. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10428-7_11

    Google Scholar 

  7. Arlot, S., Celisse, A.: A survey of cross-validation procedures for model selection. Statist. Surv. 4, 40–79 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  8. Christopher Beck, J., Davenport, A.J., Sitarski, E.M., Fox, M.S.: Texture-based heuristics for scheduling revisited. In: AAAI, pp. 241–248 (1997)

    Google Scholar 

  9. Blazewicz, J., Lenstra, J.K., Rinnooy Kan, A.H.G.: Scheduling subject to resource constraints: classification and complexity. Discret. Appl. Math. 5(1), 11–24 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  10. Brucker, P., Drexl, A., Mohring, R.H., Neumann, K., Pesch, E.: Resource-constrained project scheduling: notation, classification, models, and methods. Eur. J. Oper. Res. 1, 3–41 (1999)

    Article  MATH  Google Scholar 

  11. Gomes, C.P., Selman, B.: Algorithm portfolios. Artif. Intell. 1–2, 43–62 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  12. Hartmann, S., Briskorn, D.: A survey of variants and extensions of the resource-constrained project scheduling problem. Eur. J. Oper. Res. 1, 1–14 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  13. Herroelen, W., De Reyck, B., Demeulemeester, E.: Resource-constrained project scheduling: a survey of recent developments. Comput. OR 4, 279–302 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  14. Kolisch, R., Hartmann, S.: Experimental investigation of heuristics for resource-constrained project scheduling: an update. Eur. J. Oper. Res. 1, 23–37 (2006)

    Article  MATH  Google Scholar 

  15. Kreter, S., Schutt, A., Stuckey, P.J.: Modeling and solving project scheduling with calendars. In: Pesant, G. (ed.) CP 2015. LNCS, vol. 9255, pp. 262–278. Springer, Heidelberg (2015). doi:10.1007/978-3-319-23219-5_19

    Google Scholar 

  16. Nethercote, N., Stuckey, P.J., Becket, R., Brand, S., Duck, G.J., Tack, G.: MiniZinc: towards a standard CP modelling language. In: CP, pp. 529–543 (2007)

    Google Scholar 

  17. Ohrimenko, O., Stuckey, P.J., Michael, C.: Propagation via lazy clause generation. Constraints 3, 357–391 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  18. Rossi, F., van Beek, P., Walsh, T. (eds.): Handbook of Constraint Programming (2006)

    Google Scholar 

  19. Schutt, A., Feydy, T., Stuckey, P.J., Wallace, M.G.: Explaining the cumulative propagator. Constraints 3, 250–282 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  20. Schutt, A., Feydy, T., Stuckey, P.J., Wallace, M.G.: Solving RCPSP/max by lazy clause generation. J. Sched. 3, 273–289 (2013)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computing and Information Systems, University of Melbourne, Melbourne, Australia

    Roberto Amadini

  2. DISI, University of Bologna, Italy/FOCUS Research Team, INRIA, Rocquencourt, France

    Maurizio Gabbrielli

  3. Department of Informatics, University of Oslo, Oslo, Norway

    Jacopo Mauro

Authors
  1. Roberto Amadini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maurizio Gabbrielli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jacopo Mauro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jacopo Mauro .

Editor information

Editors and Affiliations

  1. Univ of Naples Federico II, Naples, Italy

    Paola Festa

  2. Thomas J. Wastson Research Center, Yorktown Heights, New York, USA

    Meinolf Sellmann

  3. Eindhoven University of Technology, Eindhoven, The Netherlands

    Joaquin Vanschoren

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Amadini, R., Gabbrielli, M., Mauro, J. (2016). Parallelizing Constraint Solvers for Hard RCPSP Instances. In: Festa, P., Sellmann, M., Vanschoren, J. (eds) Learning and Intelligent Optimization. LION 2016. Lecture Notes in Computer Science(), vol 10079. Springer, Cham. https://doi.org/10.1007/978-3-319-50349-3_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50349-3_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50348-6

  • Online ISBN: 978-3-319-50349-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation