The Future of the
systems is a much harder task than
comparing other kinds of solvers, be-
cause of the wide variety of features in
a constraint-programming system.
MiniZinc overcomes some of the ob-
stacles by handling global constraints
and defining a simple but expressive
search language. Still, any comparison
of CP systems is by definition incom-
plete, and indeed even the slowest
solver in the competition is capable of
creating highly effective commercial
solutions to complex real-world com-
binatorial optimization problems. We
believe the MiniZinc Challenge is im-
portant. We are excited by the poten-
tial of MiniZinc to unify the diverse re-
search fields interested in com–
binatorial optimization and to provide
a valuable tool for those who are tack-
ling these problems.
We would like to thank Mark Brown,
Sebastian Brand, and Mark Wallace for
their help in running various instances
of the MiniZinc Challenge. We would
like to thank Jimmy Lee, Barry O’Sullivan, and Roland Yap, who have served
as judges for many iterations of the
challenge. We would like to thank all
those who have submitted problems
and instances for use in the challenge
for helping us make it possible to run.
Finally we would like to thank all the
systems developers who enter, without
which of course the challenge would
1. See www.minizinc.org.
2. See www.gecode.org.
3. See www.sics.se/sisctus.
4. See jacop.osolpro.com.
5. See www.probp.com.
6. See homepages.laas.fr/ehebrard/Soft-ware.html.
7. See code.google.com/p/or-tools.
8. See picat-lang.org.
9. See www.opturion.com/cpx.html.
10. See scip.zib.de
11. See www-01.ibm.com/software/com-
12. See www.gurobi.com.
13. See projects.coin-or.org/Cbc.
14. See dtai.cs.kuleuven.be/krr/software/
15. See the unpublished draft of Purse-Based
Scoring for Comparison of Exponential-Time Programs by A. Van Gelder, D. Le
Berre, A. Biere, O. Kullmann, and L. Simon
Apt, K., and Wallace, M. 2007. Constraint
Logic Programming Using ECLiPSe. Cam-
bridge, UK: Cambridge University Press.
Bofill, M.; Palahí, M.; Suy, J.; and Villaret, M.
2012. Solving Constraint Satisfaction Problems with SAT modulo Theories. Constraints
17( 3): 273–303.
Feydy, T., and Stuckey, P. J. 2009. Lazy
Clause Generation Reengineered. In
Proceedings of the 15th International Conference
on Principles and Practice of Constraint Programming, volume 5732, Lecture Notes in
Computer Science, ed. I. Gent. Berlin:
Huang, J. 2008. Universal Booleanization of
Constraint Models. In Proceedings of the 14th
International Conference on Principles and
Practice of Constraint Programming, volume
5202, Lecture Notes in Computer Science,
144–158. Berlin: Springer.
Laburthe, F. 2000. CHOCO: Implementing
a CP Kernel. Paper presented at the Techniques for Implementing Constraint Programming Systems Workshop (TRICS
2000), Singapore, September.
Metodi, A.; Codish, M.; and Stuckey, P. J.
2013. Boolean Equi-Propagation for Concise and Efficient SAT Encodings of Combinatorial Problems. Journal of Artificial Intelligence Research 46: 303–341.
Nethercote, N.; Stuckey, P. J.; Becket, R.;
Brand, S.; Duck, G. J.; and Tack, G. 2007.
Minizinc: Towards a Standard CP Modelling
Language. In Proceedings of the 13th International Conference on Principles and Practice of Constraint Programming, volume
4741, Lecture Notes in Computer Science,
ed. C. Bessiere, editor, 529–543. Berlin:
Nieuwenhuis, R.; Oliveras, A.; and Tinelli,
C. 2006. Solving SAT and SAT Modulo Theories: From an Abstract Davis-Putnam-Lo-gemann-Loveland Procedure to DPLL(T).
Journal of the ACM 53( 6): 937–977.
Ohrimenko, O.; Stuckey, P. J.; and Codish,
M. C. 2009. Propagation Via Lazy Clause
Generation. Constraints 14( 3): 357–391.
Peter J. Stuckey is a professor in the Department of Computing and Information
Systems at the University of Melbourne and
project leader in NICTA’s Optimization Research Group. He received his PhD from
60 AI MAGAZINE
Monash University in 1988 and has pub-
lished more than 250 research articles. His
research includes constraint programming
—where he is a pioneer involved from its
very inception, logic programming, pro-
gram analysis, visualization, bioinformatics,
and optimization. His current research fo-
cus is developing the next generation of
technology for modelling and solving com-
plex combinatorial problems.
Thibaut Feydy is a researcher at NICTA and
a member of its Optimization Research
Group. He received his PhD from the University of Melbourne in 2010. His research
includes interval analysis, constraint programming, modeling, and optimization.
His present research focuses on the development of the next generation of constraint
optimisation technology. He is the author
of the state of the art lazy clause generation
solving systems CPX.
Andreas Schutt is a researcher at NICTA’s
Optimisation Research Group and an adjunct research fellow in the Department of
Computing and Informations Systems at
the University of Melbourne. He received
his PhD from the University of Melbourne
in 2011. Schutt’s research interests include
constraint programming, scheduling, packing, and combinatorial optimization. His
current research focuses on the development of the next-generation solving technology for complex combinatorial scheduling and packing problems.
Guido Tack is a lecturer and Monash
Larkins Fellow at the Faculty of Information
Technology, Monash University and a
member of the NICTA Optimisation Research Group in Melbourne, Australia. He
received his doctoral degree in 2009 from
the Department of Computer Science, Saarland University, Germany. Before joining
Monash University in 2012, he worked as a
post-doctoral researcher at NICTA Victoria
Laboratory (Australia), Saarland University
(Germany), and K.U. Leuven (Belgium).
Tack’s research focuses on combinatorial
optimisation, in particular architecture and
implementation techniques for constraint
solvers, translation of constraint modeling
languages, and industrial applications.
Julien Fischer is a software architect at Opturion, a Melbourne based startup that develops optimization software. Prior to that
he was a research engineer with NICTA’s
Optimization Research Group. His interests
include declarative programming, compiler
construction, program analysis and optimization. He currently heads development
of Opturion’s optimization platform. In addition, he is also one of the main developers of the Mercury logic programming system.