An Integrated Solver for Optimization Problems

Published Online:https://doi.org/10.1287/opre.1090.0733

References

  • Achterberg T., Berthold T., Koch T., Wolter K., Perron L., Trick M. A. Constraint integer programming: A new approach to integrate CP and MIP. Conf. Integration AI and OR Techniques in Constraint Programming Combin. Optim. Problems (CPAIOR 2008) (2008) 5015(Springer, Berlin) 6–20Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Ajili F., Wallace M., Milano M. Hybrid problem solving in ECLiPSe. Constraint and Integer Programming: Toward a Unified Methodology (2003) (Kluwer, Norwell, MA) 169–201Google Scholar
  • Aron I., Hooker J. N., Yunes T. H., Régin J.-C., Rueher M. SIMPL: A system for integrating optimization techniques. Conf. Integration AI and OR Techniques in Constraint Programming Combin. Optim. Problems (CPAIOR 2004) (2004) 3011(Springer, Berlin) 21–36Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Balas E., Ceria S., Cornuéjols G. A lift-and-project cutting plane algorithm for mixed 0-1 programs. Math. Programming (1993) 58:295–324CrossrefGoogle Scholar
  • Baptiste P., Le Pape C., Nuijten W.Constraint-Based Scheduling—Applying Constraint Programming to Scheduling Problems (2001) (Kluwer, Norwell, MA) CrossrefGoogle Scholar
  • Beck C., Refalo P. A hybrid approach to scheduling with earliness and tardiness costs. Ann. Oper. Res. (2003) 118:49–71CrossrefGoogle Scholar
  • Benoist T., Gaudin E., Rottembourg B., van Hentenryck P. Constraint programming contribution to Benders decomposition: A case study. Principles and Practice of Constraint Programming (CP 2002) (2002) 2470(Springer, Berlin) 603–617Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Bockmayr A., Kasper T. Branch and infer: A unifying framework for integer and finite domain constraint programming. INFORMS J. Comput. (1998) 10:287–300LinkGoogle Scholar
  • Bockmayr A., Pisaruk N. Detecting infeasibility and generating cuts for MIP using CP. 5th Internat. Workshop Integration AI and OR Techniques in Constraint Programming Combin. Optim. Problems (CPAIOR 2003) (2003) (Montréal, Quebec, Canada) Google Scholar
  • Bollapragada S., Ghattas O., Hooker J. N. Optimal design of truss structures by mixed logical and linear programming. Oper. Res. (2001) 49:42–51LinkGoogle Scholar
  • Brooke A., Kendrick D., Meeraus A.GAMS: A User's Guide (1988) (The Scientific Press, Redwood City, CA) Google Scholar
  • Cheadle A. M., Harvey W., Sadler A. J., Schimpf J., Shen K., Wallace M. G. ECLiPSe: An introduction. (2003) . Technical Report IC-Parc-03-1, IC-Parc, Imperial College, LondonGoogle Scholar
  • Colombani Y., Heipcke S. Mosel: An extensible environment for modeling and programming solutions. Internat. Workshop Integration AI and OR Techniques in Constraint Programming Combin. Optim. Problems (CPAIOR 2002) (2002) (Le Croisic, France) Google Scholar
  • Colombani Y., Heipcke S. Mosel: An overview. (2004) . White paper, Dash Optimization, Ltd., Blisworth, UKGoogle Scholar
  • Dincbas M., Van Hentenryck P., Simonis H., Aggoun A., Graf T., Berthier F. The constraint logic programming language CHIP. Proc. Internat. Conf. Fifth Generation Comput. Systems (1988) (Tokyo)693–702Google Scholar
  • Easton K., Nemhauser G., Trick M. Solving the traveling tournament problem: A combined integer programming and constraint programming approach. Proc. Internat. Conf. Practice and Theory Automated Timetabling (PATAT 2002) (2002) (Ghent, Belgium)Google Scholar
  • Floudas C., Pardalos P., Adjiman C., Esposito W., Gümüs Z., Harding S., Klepeis J., Meyer C., Schweiger C.Handbook of Test Problems in Local and Global Optimization (1999) 33(Kluwer, Dordrecht, The Netherlands) CrossrefGoogle Scholar
  • Focacci F., Lodi A., Milano M., Jaffar J. Cost-based domain filtering. Principles and Practice of Constraint Programming (CP) (1999) 1713(Springer, Berlin) 189–203Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Fourer R., Gay D. M., Kernighan B. W.AMPL—A Modeling Language for Mathematical Programming (2003) (Thomson Learning)Google Scholar
  • Ghattas O., Grossman I. MINLP and MILP strategies for discrete sizing structural optimization problems. Proc. ASCE 10th Conf. Electronic Comput (1991) (Indianapolis)197–204Google Scholar
  • Guéret C., Prins C., Sevaux M., Heipcke S.Applications of Optimization with XPRESS-MP (2002) (Dash Optimization Ltd., Blisworth, UK) Google Scholar
  • Hajian M. T., El-Sakkout H., Wallace M., Lever J. M., Richards E. B. Toward a closer integration of finite domain propagation and simplex-based algorithms. Proc. Fourth Internat. Sympos. Artificial Intelligence and Math. (1996) (Fort Lauderdale, FL)Google Scholar
  • Hooker J. N., Borning A. Logic-based methods for optimization. Principles and Practice of Constraint Programming (1994) 874(Springer, Berlin) 336–349Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Hooker J. N., Freuder E. C. Inference duality as a basis for sensitivity analysis. Principles and Practice of Constraint Programming (CP) (1996) 1118(Springer, Berlin) 224–236Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Hooker J. N., Woodruff D. L. Constraint satisfaction methods for generating valid cuts. Advances in Computational and Stochastic Optimization, Logic Programming and Heuristic Search (1997) (Kluwer, Dordrecht, The Netherlands) 1–30Google Scholar
  • Hooker J. N. Inference duality as a basis for sensitivity analysis. Constraints (1999) 4:104–112CrossrefGoogle Scholar
  • Hooker J. N.Logic-Based Methods for Optimization: Combining Optimization and Constraint Satisfaction (2000) (John Wiley & Sons, New York) CrossrefGoogle Scholar
  • Hooker J. N. Logic, optimization and constraint programming. INFORMS J. Comput. (2002) 14:295–321LinkGoogle Scholar
  • Hooker J. N., Bhargava H. K., Ye M. A framework for integrating solution methods. Computational Modeling and Problem Solving in the Networked World (Proc. ICS2003) (2003) (Kluwer)3–30CrossrefGoogle Scholar
  • Hooker J. N., Wallace M. A hybrid method for planning and scheduling. Principles and Practice of Constraint Programming (CP 2004) (2004) (Springer, 3258) 305–316Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Hooker J. N., Barták R., Milano M. A search-infer-and-relax framework for integrating solution methods. Conf. Integration AI and OR Techniques in Constraint Programming Combin. Optim. Problems (CPAIOR 2005) (2005a) 3709(Springer, Berlin) 314–327Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Hooker J. N. A hybrid method for planning and scheduling. Constraints (2005b) 10:385–401CrossrefGoogle Scholar
  • Hooker J. N., Jermann C., Neumaier A., Sam D. Convex programming methods for global optimization. Global Optimization and Constraint Satisfaction (COCOS 2003) (2005c) 3478(Springer, Berlin) 46–60Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Hooker J. N., Rossi F., van Beek P., Walsh T. Operations research methods in constraint programming. Handbook of Constraint Programming (2006) (Elsevier, Amsterdam) 525–568CrossrefGoogle Scholar
  • Hooker J. N.Integrated Methods for Optimization (2007) (Springer, New York) Google Scholar
  • Hooker J. N., Osorio M. A. Mixed logical/linear programming. Discrete Appl. Math. (1999) 96–97:395–442CrossrefGoogle Scholar
  • Hooker J. N., Yan H., Saraswat V., Van Hentenryck P. Logic circuit verification by Benders decomposition. Principles and Practice of Constraint Programming: The Newport Papers (1995) (MIT Press)267–288Google Scholar
  • Hooker J. N., Ottosson G., Thorsteinsson E. S., Kim H.-J. A scheme for unifying optimization and constraint satisfaction methods. Knowledge Engrg. Rev. (2000) 15:11–30CrossrefGoogle Scholar
  • ILOG S. A.ILOG Solver 6.0 User's Manual (2003) (ILOG, Gentilly, France) Google Scholar
  • ILOG S. A.ILOG CPLEX 11.0 User's Manual (2007) (ILOG, Gentilly, France) Google Scholar
  • Jain V., Grossmann I. E. Algorithms for hybrid MILP/CP models for a class of optimization problems. INFORMS J. Comput. (2001) 13:258–276LinkGoogle Scholar
  • Junker U., Karisch S. E., Kohl N., Vaaben B., Fahle T., Sellmann M., Jaffar J. A framework for constraint programming based column generation. Principles and Practice of Constraint Programming (CP) (1999) 1713(Springer, Berlin) 261–274Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Milano M.Constraint and Integer Programming: Toward a Unified Methodology (2003) (Kluwer, Norwell, MA) Google Scholar
  • Ottosson G., Thorsteinsson E., Hooker J. N. Mixed global constraints and inference in hybrid IP-CLP solvers. CP99 Post-Conf. Workshop Large-Scale Combin. Optim. Constraints (1999) (Alexandria, VA)57–78 http://www.dash.co.uk/wscp99Google Scholar
  • Ottosson G., Thorsteinsson E. S., Hooker J. N. Mixed global constraints and inference in hybrid CLP-IP solvers. Ann. Math. Artificial Intelligence (2002) 34:271–290CrossrefGoogle Scholar
  • Pintér J. D. LGO—A model development system for continuous global optimization. User's Guide (2005) (Pintér Consulting Services, Inc., Halifax, Nova Scotia, Canada) Google Scholar
  • Rasmussen R., Trick M. A Benders approach for the minimum break scheduling problem. (2005) INFORMS 2005San Francisco(INFORMS, Hanover, MD) Google Scholar
  • Refalo P., Jaffar J. Tight cooperation and its application in piecewise linear optimization. Principles and Practice of Constraint Programming (CP 1999) (1999) 1713(Springer, Berlin) 375–389Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Rodošek R., Wallace M., Hajian M. T. A new approach to integrating mixed integer programming and constraint logic programming. Ann. Oper. Res. (1999) 86:63–87CrossrefGoogle Scholar
  • Sellmann M., Fahle T. Constraint programming based Lagrangian relaxation for a multimedia application. Third Internat. Workshop Integration AI and OR Techniques (CPAIOR) (2001) (London)Google Scholar
  • Tawarmalani M., Sahinidis N. V. Global optimization of mixed-integer nonlinear programs: A theoretical and computational study. Math. Programming (2004) 99(3):563–591CrossrefGoogle Scholar
  • Thorsteinsson E. S., Walsh T. Branch-and-check: A hybrid framework integrating mixed integer programming and constraint logic programming. Principles and Practice of Constraint Programming (CP 2001) (2001) 2239(Springer, Berlin) 16–30Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Thorsteinsson E. S., Ottosson G. Linear relaxations and reduced-cost based propagation of continuous variable subscripts. Ann. Oper. Res. (2001) 115:15–29CrossrefGoogle Scholar
  • Timpe C. Solving planning and scheduling problems with combined integer and constraint programming. OR Spectrum (2002) 24:431–448CrossrefGoogle Scholar
  • Van Hentenryck P., Lustig I., Michel L., Puget J. F.The OPL Optimization Programming Language (1999) (MIT Press, Cambridge, MA) Google Scholar
  • Van Hoeve W. J., Rossi F. A hybrid constraint programming and semidefinite programming approach for the stable set problem. Principles and Practice of Constraint Programming (CP 2003) (2003) 2833(Springer, Berlin) 407–421Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Williams H. P., Yan H. Representations of the all_different predicate of constraint satisfaction in integer programming. INFORMS J. Comput. (2001) 13(2):96–103LinkGoogle Scholar
  • Yan H., Hooker J. N. Tight representation of logical constraints as cardinality rules. Math. Programming (1999) 85:363–377CrossrefGoogle Scholar
  • Yunes T. H., Moura A. V., de Souza C. C. Exact solutions for real world crew scheduling problems. (1999) INFORMS Annual Meeting, Philadelphia(INFORMS, Hanover, MD) Google Scholar
  • Yunes T. H., Moura A. V., de Souza C. C. Hybrid column generation approaches for urban transit crew management problems. Transportation Sci. (2005) 39(2):273–288LinkGoogle Scholar
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