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An Integrated Solver for Optimization Problems

Author

Listed:
  • Tallys Yunes

    (Department of Management Science, School of Business Administration, University of Miami, Coral Gables, Florida 33124)

  • Ionuţ D. Aron

    (WorldQuant LLC, New York, New York 10103)

  • J. N. Hooker

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

One of the central trends in the optimization community over the past several years has been the steady improvement of general-purpose solvers. A logical next step in this evolution is to combine mixed-integer linear programming, constraint programming, and global optimization in a single system. Recent research in the area of integrated problem solving suggests that the right combination of different technologies can simplify modeling and speed up computation substantially. Nevertheless, integration often requires special-purpose coding, which is time consuming and error prone. We present a general-purpose solver, SIMPL, that allows its user to replicate (and sometimes improve on) the results of custom implementations with concise models written in a high-level language. We apply SIMPL to production planning, product configuration, machine scheduling, and truss structure design problems on which customized integrated methods have shown significant computational advantage. We obtain results that either match or surpass the original codes at a fraction of the implementation effort.

Suggested Citation

  • Tallys Yunes & Ionuţ D. Aron & J. N. Hooker, 2010. "An Integrated Solver for Optimization Problems," Operations Research, INFORMS, vol. 58(2), pages 342-356, April.
    • Handle: RePEc:inm:oropre:v:58:y:2010:i:2:p:342-356
    DOI: 10.1287/opre.1090.0733
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    References listed on IDEAS

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    Cited by:

    1. Qin, Tianbao & Du, Yuquan & Sha, Mei, 2016. "Evaluating the solution performance of IP and CP for berth allocation with time-varying water depth," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 87(C), pages 167-185.
    2. Andre A. Cire & John N. Hooker & Tallys Yunes, 2016. "Modeling with Metaconstraints and Semantic Typing of Variables," INFORMS Journal on Computing, INFORMS, vol. 28(1), pages 1-13, February.

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