PyMOSO: Software for multi-objective simulation optimization with R-PERLE and R-MinRLE. Cooper, K. & Hunter, S. R. *INFORMS Journal on Computing*, 32(4):1101–1108, 2020.

Paper doi abstract bibtex 26 downloads

Paper doi abstract bibtex 26 downloads

We present the PyMOSO software package for (1) solving multi-objective 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 employ pseudo-gradients, are designed for sampling efficiency, and return solutions that, under appropriate regularity conditions, provably converge to a local efficient set with probability one 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 pseudo-random number stream management, implements algorithm testing with independent pseudo-random 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.

@article{2020coohun, Year = {2020}, Author = {K. Cooper and S. R. Hunter}, Title = {{PyMOSO}: {S}oftware for multi-objective simulation optimization with {R-PERLE} and {R-MinRLE}}, journal = {INFORMS Journal on Computing}, volume = {32}, number = {4}, pages = {1101--1108}, doi = {10.1287/ijoc.2019.0902}, url_Paper = {http://web.ics.purdue.edu/~hunter63/PAPERS/pre2019coohun.pdf}, abstract = {We present the PyMOSO software package for (1) solving multi-objective 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 employ pseudo-gradients, are designed for sampling efficiency, and return solutions that, under appropriate regularity conditions, provably converge to a local efficient set with probability one 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 pseudo-random number stream management, implements algorithm testing with independent pseudo-random 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.}, keywords = {all software}}

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R."],"bibdata":{"bibtype":"article","type":"article","year":"2020","author":[{"firstnames":["K."],"propositions":[],"lastnames":["Cooper"],"suffixes":[]},{"firstnames":["S.","R."],"propositions":[],"lastnames":["Hunter"],"suffixes":[]}],"title":"PyMOSO: Software for multi-objective simulation optimization with R-PERLE and R-MinRLE","journal":"INFORMS Journal on Computing","volume":"32","number":"4","pages":"1101–1108","doi":"10.1287/ijoc.2019.0902","url_paper":"http://web.ics.purdue.edu/~hunter63/PAPERS/pre2019coohun.pdf","abstract":"We present the PyMOSO software package for (1) solving multi-objective 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 employ pseudo-gradients, are designed for sampling efficiency, and return solutions that, under appropriate regularity conditions, provably converge to a local efficient set with probability one 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 pseudo-random number stream management, implements algorithm testing with independent pseudo-random 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.","keywords":"all software","bibtex":"@article{2020coohun,\n\tYear = {2020},\n\tAuthor = {K. Cooper and S. R. Hunter},\n\tTitle = {{PyMOSO}: {S}oftware for multi-objective simulation optimization with {R-PERLE} and {R-MinRLE}},\n\tjournal = {INFORMS Journal on Computing},\n\tvolume = {32},\n\tnumber = {4},\n\tpages = {1101--1108},\n\tdoi = {10.1287/ijoc.2019.0902},\n\turl_Paper = {http://web.ics.purdue.edu/~hunter63/PAPERS/pre2019coohun.pdf},\n\tabstract = {We present the PyMOSO software package for (1) solving multi-objective 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 employ pseudo-gradients, are designed for sampling efficiency, and return solutions that, under appropriate regularity conditions, provably converge to a local efficient set with probability one 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 pseudo-random number stream management, implements algorithm testing with independent pseudo-random 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.},\n\tkeywords = {all software}}\n\n","author_short":["Cooper, K.","Hunter, S. 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