@article{eckhensha22,
	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; includes an implementation of the MRG32k3a random-number generator that supports streams, substreams and subsubstreams; supports the automated use of common random numbers for 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.},
	author = {David J. Eckman and Shane G. Henderson and Sara Shashaani},
	date-added = {2020-09-19 11:50:09 -0400},
	date-modified = {2023-07-14 11:27:08 -0400},
	journal = {{INFORMS} Journal on Computing},
	number = {2},
	pages = {495-508},
	title = {{SimOpt}: A testbed for simulation-optimization experiments},
	url = {https://doi.org/10.1287/ijoc.2023.1273},
	url_paper = {pubs/comparingsimopt.pdf},
	volume = {35},
	year = {2023},
	bdsk-url-1 = {https://doi.org/10.1287/ijoc.2023.1273}}

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