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SimOpt: A Testbed for Simulation-Optimization Experiments

Author

Listed:
  • David J. Eckman

    (Wm Michael Barnes ’64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, Texas 77843)

  • Shane G. Henderson

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853)

  • Sara Shashaani

    (Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina 27695)

Abstract

This paper introduces a major redesign of SimOpt, a testbed of simulation-optimization (SO) problems and solvers. The testbed promotes the empirical evaluation and comparison of solvers and aims to accelerate their development. Relative to previous versions of SimOpt, the redesign ports the code to an object-oriented architecture in Python; uses an implementation of the MRG32k3a random number generator that supports streams, substreams, and subsubstreams; supports the automated use of common random numbers for ease and efficiency; includes a powerful suite of plotting tools for visualizing experiment results; uses bootstrapping to obtain error estimates; accommodates the use of data farming to explore simulation models and optimization solvers as their input parameters vary; and provides a graphical user interface. The SimOpt source code is available on a GitHub repository under a permissive open-source license and as a Python package.

Suggested Citation

  • David J. Eckman & Shane G. Henderson & Sara Shashaani, 2023. "SimOpt: A Testbed for Simulation-Optimization Experiments," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 495-508, March.
    • Handle: RePEc:inm:orijoc:v:35:y:2023:i:2:p:495-508
    DOI: 10.1287/ijoc.2023.1273
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    References listed on IDEAS

    as
    1. Pierre L'Ecuyer & Richard Simard & E. Jack Chen & W. David Kelton, 2002. "An Object-Oriented Random-Number Package with Many Long Streams and Substreams," Operations Research, INFORMS, vol. 50(6), pages 1073-1075, December.
    2. Kyle Cooper & Susan R. Hunter, 2020. "PyMOSO: Software for Multiobjective Simulation Optimization with R-PERLE and R-MinRLE," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 1101-1108, October.
    3. David J. Eckman & Shane G. Henderson & Sara Shashaani, 2023. "Diagnostic Tools for Evaluating and Comparing Simulation-Optimization Algorithms," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 350-367, March.
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