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PyMOSO: Software for Multiobjective Simulation Optimization with R-PERLE and R-MinRLE

Author

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  • Kyle Cooper

    (School of Industrial Engineering, Purdue University, West Lafayette, Indiana 47906; Tata Consultancy Services, Milford, Ohio 45150)

  • Susan R. Hunter

    (School of Industrial Engineering, Purdue University, West Lafayette, Indiana 47906;)

Abstract

We present the PyMOSO software package for (1) solving multiobjective simulation optimization (MOSO) problems on integer lattices and (2) implementing and testing new simulation optimization (SO) algorithms. First, for solving MOSO problems on integer lattices, PyMOSO implements R-PERLE, a state-of-the-art algorithm for two objectives, and R-MinRLE, a competitive benchmark algorithm for three or more objectives. Both algorithms use pseudogradients, are designed for sampling efficiency, and return solutions that, under appropriate regularity conditions, provably converge to a local efficient set with probability 1 as the simulation budget increases. PyMOSO can interface with existing simulation software and can obtain simulation replications in parallel. Second, for implementing and testing new SO algorithms, PyMOSO includes pseudorandom number stream management, implements algorithm testing with independent pseudorandom number streams run in parallel, and computes the performance of algorithms with user-defined metrics. For convenience, we also include an implementation of R-SPLINE for problems with one objective. The PyMOSO source code is available under a permissive open-source license.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orijoc:v:32:y:4:i:2020:p:1101-1108
    DOI: 10.1287/ijoc.2019.0902
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    References listed on IDEAS

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    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 & Kalyani Nagaraj, 2020. "Biobjective Simulation Optimization on Integer Lattices Using the Epsilon-Constraint Method in a Retrospective Approximation Framework," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 1080-1100, October.
    3. Lee, Loo Hay & Chew, Ek Peng & Teng, Suyan & Chen, Yankai, 2008. "Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem," European Journal of Operational Research, Elsevier, vol. 189(2), pages 476-491, September.
    4. L. Jeff Hong & Barry L. Nelson & Jie Xu, 2015. "Discrete Optimization via Simulation," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 9-44, Springer.
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    Cited by:

    1. 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.
    2. Kyle Cooper & Susan R. Hunter & Kalyani Nagaraj, 2020. "Biobjective Simulation Optimization on Integer Lattices Using the Epsilon-Constraint Method in a Retrospective Approximation Framework," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 1080-1100, October.

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