Peter J. Stuckey

Peter J. Stuckey

University of Melbourne
Design of the MiniZinc language, lazy clause generation solvers, modelling interesting problems, developing and teaching Modeling Discrete Optimization.

Publications on MiniZinc by Peter J. Stuckey

  • Gleb Belov, Peter J. Stuckey, Guido Tack, Mark Wallace. Improved Linearization of Constraint Programming Models. In M. Rueher, editor, Principles and Practice of Constraint Programming - 22nd International Conference, CP 2016, Toulouse, France, September 5-9, 2016, Proceedings. LNCS 9892, pp. 49-65, Springer, 2016.
  • Andrea Rendl, Tias Guns, Peter J. Stuckey, and Guido Tack. MiniSearch: a solver-independent meta-search language for MiniZinc. In Gilles Pesant, editor, CP, p. 376-392. Springer, 2015.
  • Geoffrey Chu and Peter J. Stuckey. Learning value heuristics for constraint programming. In Proceedings of Twelfth International Conference on Integration of Artificial Intelligence and Operations Research techniques in Constraint Programming (CPAIOR 2015), volume 9075 of LNCS, pages 108–123, Springer, 2015.
  • Christina Burt, Nir Lipovetzky, Adrian Pearce, and Peter J. Stuckey. Scheduling with fixed maintenance, shared resources and nonlinear feedrate constraints: a mine planning case study. In Proceedings of Twelfth International Conference on Integration of Artificial Intelligence and Operations Research techniques in Constraint Programming (CPAIOR 2015), volume 9075 of LNCS, pages 91–107, Springer, 2015.
  • Andrea Rendl, Guido Tack, and Peter J. Stuckey. Stochastic MiniZinc. In B. O’Sullivan, editor, Proceedings of the 20th International Conference on Principles and Practice of Constraint Programming, volume 8656 of LNCS, pages 636–645. Springer, 2014.
  • Christopher Mears, Andreas Schutt, Peter J. Stuckey, Guido Tack, Kim Marriott, and Mark Wallace. Modelling with option types in MiniZinc. In Proceedings of the 11th International Conference on Integration of Artificial Intelligence (AI) and Operations Research (OR) techniques in Constraint Programming, number 8451 in LNCS, pages 88–103. Springer, 2014.
  • Kathryn Francis and Peter J. Stuckey. Loop untangling. In B. O’Sullivan, editor, Proceedings of the 20th International Conference on Principles and Practice of Constraint Programming, volume 8656 of LNCS, pages 340–355, Springer, 2014.
  • Kathryn Francis and Peter J. Stuckey.. Explaining circuit propagation. Constraints, 19(1):1–29, 2014.
  • Geoffrey Chu and Peter J. Stuckey. Nested constraint programs. In B. O’Sullivan, editor, Proceedings of the 20th International Conference on Principles and Practice of Constraint Programming, volume 8656 of LNCS, pages 240–255. Springer, 2014.
  • Peter J. Stuckey, Thibaut Feydy, Andreas Schutt, Guido Tack, and Julien Fischer. The MiniZinc challenge 2008-2013. AI Magazine, 35(2):55–60, 2014, 2014. more...
    MiniZinc is a solver agnostic modeling language for defining and solver combinatorial satisfaction and optimization problems. MiniZinc provides a solver independent modeling language which is now supported by constraint programming solvers, mixed integer programming solvers, SAT and SAT modulo theory solvers, and hybrid solvers. Since 2008 we have run the MiniZinc challenge every year, which compares and contrasts the different strengths of different solvers and solving technologies on a set of MiniZinc models. Here we report on what we have learnt from running the competition for 6 years.
  • Gleb Belov, Natashia Boland, Martin W.P. Savelsbergh, and Peter J. Stuckey. Local search for a cargo assembly planning problem. In Proceedings of the 11th Interna- tional Conference on Integration of Artificial Intelligence (AI) and Operations Re- search (OR) techniques in Constraint Programming, number 8451 in LNCS, pages 159–175, Springer, 2014.
  • Roberto Amadini and Peter J. Stuckey. Sequential time splitting and bounds communication for a portfolio of optimization solvers. In B. O’Sullivan, editor, Proceedings of the 20th International Conference on Principles and Practice of Constraint Programming, volume 8656 of LNCS, pages 108–124, Springer, 2014.
  • Peter J. Stuckey and Guido Tack. MiniZinc with functions. In Proceedings of the 10th International Conference on Integration of Artificial Intelligence (AI) and Operations Research (OR) techniques in Constraint Programming, number 7874 in LNCS, pages 268–283. Springer, 2013.
  • Andreas Schutt, Thibaut Feydy, and Peter J. Stuckey. Explaining time-table-edge-finding propagation for the cumulative resource constraint. In Proceedings of the 10th International Conference on Integration of Artificial Intelligence (AI) and Operations Research (OR) techniques in Constraint Programming, number 7874 in LNCS, pages 234–250, Springer, 2013.
  • Andreas Schutt, Thibaut Feydy, and Peter J. Stuckey. Scheduling optional tasks with explanation. In C. Schulte, editor, Proceedings of the 19th International Conference on Principles and Practice of Constraint Programming, volume 8124 of LNCS, pages 628–644, Springer, 2013.
  • Andreas Schutt, Thibaut Feydy, Peter J. Stuckey, and Mark Wallace.. Solving RCPSP/max by lazy clause generation. Journal of Scheduling, 16(3):273–289, 2013.
  • T. Schrijvers, G. Tack, P. Wuille, H. Samulowitz, and P.J. Stuckey. Search combinators. Constraints, 18(2):269–305, 2013.
  • Kathryn Francis, Jorge Navas, and Peter J. Stuckey. Modelling destructive assignments. In C. Schulte, editor, Proceedings of the 19th International Conference on Principles and Practice of Constraint Programming, volume 8124 of LNCS, pages 315–330, Springer, 2013.
  • Geoffrey Chu and Peter J. Stuckey. Dominance driven search. In C. Schulte, editor, Proceedings of the 19th International Conference on Principles and Practice of Constraint Programming, volume 8124 of LNCS, pages 217–229, Springer, 2013.
  • Rafael Caballero, Peter J. Stuckey, and Antonio Tenorio-Fornes. Finite type extensions in constraint programming. In T. Schrijvers, editor, Proceedings of the 15th International Symposium on Principles and Practice of Declarative Programming, pages 217–228, ACM Press, 2013.
  • Rehan Abdul Aziz, Peter J. Stuckey, and Zoltan Somogyi. Inductive definitions in constraint programming. In Proceedings of the Thirty-Sixth Australasian Computer Science Conference (ACSC 2013), pages 41–50, 2013.
  • Rehan Abdul Aziz, Geoffrey Chu, and Peter J. Stuckey.. Stable model semantics for founded bounds.. Theory and Practice of Logic Programming, 13(4–5):517–532, 2013. Proceedings of the 29th International Conference on Logic Programming, 2013.
  • Kathryn Francis, Sebastian Brand, and Peter J. Stuckey. Optimization modelling for software developers. In M. Milano, editor, Proceedings of the 18th International Conference on Principles and Practice of Constraint Programming, number 7514 in LNCS, pages 274–289, Springer, 2012.
  • N. Downing, T. Feydy, and P.J. Stuckey. Explaining alldifferent. In M. Reynolds and B. Thomas, editors, Proceedings of the Australasian Computer Science Conference (ACSC 2012), volume 122 of CRPIT, pages 115–124, Melbourne, Australia, ACS, 2012.
  • N. Downing, T. Feydy, and P.J. Stuckey. Explaining flow-based propagation. In N. Beldiceanu, N. Jussien, and E. Pinson, editors, International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR), number 7298 in LNCS, pages 146–162, Springer, 2012.
  • G. Chu and P.J. Stuckey. A generic method for systematically identifying and exploiting dominance relations. In M. Milano, editor, Proceedings of the 18th International Conference on Principles and Practice of Constraint Programming, number 7514 in LNCS, pages 6–22, Springer, 2012.
  • G. Chu and P.J. Stuckey. Inter-problem nogood learning in constraint programming. In M. Milano, editor, Proceedings of the 18th International Conference on Principles and Practice of Constraint Programming, number 7514 in LNCS, pages 238–247, Springer, 2012.
  • G. Chu, M. Garcia de la Banda, and P.J. Stuckey. Exploiting subproblem dominance in constraint programming. Constraints, 17(1):1–38, 2012.
  • G. Chu and P.J. Stuckey. A complete solution to the maximum density still life problem. Artificial Intelligence, 184–185:1–16, 2012.
  • A. Schutt, T. Feydy, P.J. Stuckey, and M. Wallace. Explaining the cumulative propagator. Constraints, 16(3):250–282, 2011.
  • T. Schrijvers, G. Tack, P. Wuille, H. Samulowitz, and P.J. Stuckey. Search combinators. In J.H.M. Lee, editor, Proceedings of the 17th International Conference on Principles and Practice of Constraint Programming, volume 6876 of LNCS, pages 774–788. Springer, 2011.
  • J. Puchinger, P.J. Stuckey, M. Wallace, and S. Brand. Dantzig-wolfe decomposition and branch-and-price solving in G12. Constraints, 16(1):77–99, 2011.
  • T. Feydy, Z. Somogyi, and P.J. Stuckey. Half-reification and flattening. In J.H.M. Lee, editor, Proceedings of the 17th International Conference on Principles and Practice of Constraint Programming, volume 6876 of LNCS, pages 286–301. Springer, 2011.
  • P.J. Stuckey, R. Becket, and J. Fischer. Philosophy of the MiniZinc challenge. Constraints, 15(3):307–316, 2010.
  • G. Chu, M. Garcia de la Banda, and P.J. Stuckey. Automatically exploiting subproblem equivalence in constraint programming. In Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, volume 6140 of LNCS, pages 71–86, Springer, 2010.
  • A. Schutt, T. Feydy, P.J. Stuckey, and M. Wallace. Why cumultive decomposition is not as bad as it sounds. In I. Gent, editor, Proceedings of the 15th International Conference on Principles and Practice of Constraint Programming, volume 5732 of LNCS, pages 746–761, Springer-Verlag, 2009.
  • Alan Frisch and Peter J. Stuckey. The proper treatment of undefinedness in constraint languages. In I. Gent, editor, Proceedings of the 15th International Conference on Principles and Practice of Constraint Programming, volume 5732 of LNCS, pages 367–382. Springer-Verlag, 2009.
  • T. Feydy and P.J. Stuckey. Lazy clause generation reengineered. In I. Gent, editor, Proceedings of the 15th International Conference on Principles and Practice of Constraint Programming, volume 5732 of LNCS, pages 352–366, Springer-Verlag, 2009.
  • Kim Marriott, Nicholas Nethercote, R. Rafeh, Peter J. Stuckey, Maria Garcia de la Banda, and Mark Wallace. The design of the Zinc modelling language. Constraints, 13(3):229–267, 2008.
  • Sebastian Brand, Gregory J. Duck, Jakob Puchinger, and Peter J. Stuckey. Flexible, rule-based constraint model linearisation. In P. Hudak and D.S. Warren, editors, Proceedings of Tenth International Symposium on Practical Aspects of Declarative Languages, number 4902 in LNCS, pages 68–83. Springer-Verlag, 2008.
  • N. Nethercote, P.J. Stuckey, R. Becket, S. Brand, G.J. Duck, and G. Tack. MiniZinc: Towards a standard CP modelling language. In C. Bessiere, editor, Proceedings of the 13th International Conference on Principles and Practice of Constraint Programming, volume 4741 of LNCS, pages 529–543. Springer, 2007. more...
    There is no standard modelling language for constraint programming (CP) problems. Most solvers have their own modelling language. This makes it difficult for modellers to experiment with different solvers for a problem. In this paper we present MiniZinc, a simple but expressive CP modelling language which is suitable for modelling problems for a range of solvers and provides a reasonable compromise between many design possibilities. Equally importantly, we also propose a low-level solver-input language called FlatZinc, and a straightforward translation from MiniZinc to FlatZinc that preserves all solver-supported global constraints. This lets a solver writer support MiniZinc with a minimum of effort - they only need to provide a simple FlatZinc front-end to their solver, and then combine it with an existing MiniZinc-to-FlatZinc translator. Such a front-end may then serve as a stepping stone towards a full MiniZinc implementation that is more tailored to the particular solver. A standard language for modelling CP problems will encourage experimentation with and comparisons between different solvers. Although MiniZinc is not perfect - no standard modelling language will be - we believe its simplicity, expressiveness, and ease of implementation make it a practical choice for a standard language.