Package: simoptlib
Welcome to SimOpt!SimOpt is a testbed of simulation-optimization problems and solvers. Its purpose is to encourage the development and constructive comparison of simulation-optimization (SO) solvers (algorithms). We are particularly interested in the finite-time performance of solvers, rather than the asymptotic results that one often finds in related literature.The most-up-to-date publication about this library isEckman et al. (2021).For the purposes of this project, we define simulation as a very general technique for estimating statistical measures of complex systems. A system is modeled as if the probability distributions of the underlying random variables were known. Realizations of these random variables are then drawn randomly from these distributions. Each replication gives one observation of the system response, i.e., an evaluation of the objective function or stochastic constraints. By simulating a system in this fashion for multiple replications and aggregating the responses, one can compute statistics and use them for evaluation and design.Several papers have discussed the development of SimOpt and experiments run on the testbed:Pasupathy and Henderson (2006)explains the original motivation for the testbed.Pasupathy and Henderson (2011)describes an earlier interface for MATLAB implementations of problems and solvers.Dong et al. (2017)conducts an experimental comparison of several solvers in SimOpt and analyzes their relative performance.Eckman et al. (2019)describes in detail changes to the architecture of the MATLAB version of SimOpt and the control of random number streams.Eckman et al. (2021)introduces the design of experiments for comparing solvers; this design has been implemented in the latest Python version of SimOpt. For detailed description of the terminology used in the library, e.g., factors, macroreplications, post-processing, solvability plots, etc., see this paper.CodeThemasterbranch contains the source code for the latest version of the testbed, which is written in Python.Thematlabbranch contains a previous stable version of the testbed written in MATLAB.DocumentationFull documentation for the source code can be foundhere.Getting StartedThe most straightforward way to interact with the library is toclonethis repository.(If you anticipate making improvements or contributions to SimOpt, you should firstforkthe repository so that you can later request your changes be integrated via GitHub's pull request feature.)Alternatively, you can choose to install the library as a Python package; see the sections titledPackageandBasic Examplebelow for more details about this option.Download a copy of the cloned repository to your local machine and navigate to thesimoptfolder in your preferred integrated development environment (IDE). You will need to make sure that you have the following dependencies installed: Python 3,numpy,scipy,matplotlib,pandas,seaborn, andmrg32k3a. Run the commandpython -m pip install numpy scipy matplotlib pandas seaborn mrg32k3ato install them from the terminal.Thedemofolder contains a handful of useful scripts that can be easily modified, as directed in the comments.demo_model.py: Run multiple replications of a simulation model and report its responses.demo_problem.py: Run multiple replications of a given solution for an SO problem and report its objective function values and left-hand sides of stochastic constraints.demo_problem_solver.py: Run multiple macroreplications of a solver on a problem, save the outputs to a .pickle file in theexperiments/outputsfolder, and save plots of the results to .png files in theexperiments/plotsfolder.demo_problems_solvers.py: Run multiple macroreplications of groups of problem-solver pairs and save the outputs and plots.demo_data_farming_model.py: Create a design over model factors, run multiple replications at each design point, and save the results to a comma separated value (.csv) file in thedata_farming_experimentsfolder.demo_san-sscont-ironorecont_experiment: Run multiple solvers on multiple versions of (s, S) inventory, iron ore, and stochastic activiy network problems and produce plots.Graphical User Interface (GUI) - User GuideInstallationTo start up the GUI, navigate to the outersimoptdirectory and run the commandpython3 -m simopt.GUIfrom the terminal. The GUI depends on Python 3,numpy,scipy,matplotlib,Pillow, andtkinter. Run the commandpython3 -m pip install numpy scipy matplotlib Pillow tkinterto install them from the terminal.OverviewFrom the GUI, you can create a specifiedproblem-solver pairor aproblem-solver group, run macroreplications, and generate plots. The main page provides ways to create or continue working with experiments:Create an individualproblem-solver pairwith customized problem and solver factors.Load a .pickle file of a previously createdproblem-solver pair.Create aproblem-solver group.At the bottom of the main page, there is a workspace containing allproblem-solver pairs andproblem-solver groups. The first tab lists theproblem-solver pairs ready to be run or post-replicated, the second tab lists theproblem-solver groups made from the cross-design or by generating aproblem-solver groupfrom partial set ofproblem-solver pairin the first tab, and the third tab lists thoseproblem-solver pairs that are ready to be post-normalized and prepared for plotting.1. Creating aproblem-solver pairThis is the main way to addproblem-solver pairs to the queue in the workspace.First, select a solver from the "Solver" dropdown list. Each of the solvers has an abbreviation for the type of problems the solver can handle. Once a solver is selected, the "Problem" list will be sorted and show only the problems that work with the selected solver.Change factors associated with the solver as necessary. The first factor is a customizable name for the solver that use can specify.All solvers with unique combinations of factors must have unique names, i.e., no two solvers can have the same name, but different factors. If you want to use the same solver twice for a problem but with different solver factors, make sure you change the name of the solver accordingly. For example, if you want to create twoproblem-solver pairs with the same problem and solver but with or without CRN for the solver, you can change the name of the solver of choice for each pair to reflect that. This name will appear in the queue within the workspace below.Select a problem from the "Problem" dropdown list.
Each problem has an abbreviation indicating which types of solver is compatible to solve it. The letters in the abbreviation stand for:ObjectiveConstraintVariableDirect Gradient ObservationsSingle (S)Unconstrained (U)Discrete (D)Available (G)Multiple (M)Box (B)Continuous (C)Not Available (N)Deterministic (D)Mixed (M)Stochastic (S)Change factors associated with the problem and model as necessary.All problems with unique factors must have unique names, i.e., no two problems can have the same name, but different factors. If you want to use the same problem twice for a solver but with different problem or model factors, make sure you change the name of the problem accordingly. This name will appear in the queue within the workspace below.The number of macroreplications can be modified in the top-left corner. The default is 10.Select the "Addproblem-solver pair" button, which only appears when a solver and problem is selected. Theproblem-solver pairwill be added in the "Queue ofproblem-solver pairs."2. Loading aproblem-solver pairfrom a fileInstead of creating aproblem-solver pairfrom scratch, you can load one from a *.pickle file:In the top left corner, click "Load aproblem-solver pair". Your file system will pop up, and you can navigate to and select an appropriate *.pickle file. The GUI will throw an error if the selected file is not a *.pickle file.Once aproblem-solver pairobject is loaded, it will be added to the "Queue ofproblem-solver pairs".The Run and Post-Process buttons will be updated to accurately reflect whether theproblem-solver pairhas already been run and/or post-processed.3. Creating aproblem-solver groupCurrently,problem-solver groups can only be created within the GUI or command line; they cannot be loaded from a file.You can create aproblem-solver groupand add a new item to the "Queue ofproblem-solver groups" in two ways. The first is a quick grouping of problems and solvers that are compatible with their default factors:
of problems and solvers with their default factors.Click the "Create aproblem-solver group" button.Check the compatibility of the Problems and Solvers being selected. Note that solvers with deterministic constraint type can not handle problems with stochastic constraints (e.g., ASTRO-DF cannot be run on FACSIZE-2).Specify the number of macroreplications - the default is 10.Click "Confirm Cross-Designproblem-solver group."The pop-up window will disappear, and theproblem-solver pairs frame will automatically switch to the "Queue ofproblem-solver groups".To exit out of theproblem-solver grouppop-up without creating aproblem-solver group, click the red "x" in the top-left corner of the window.The second is converting a list ofproblem-solver pairs into aproblem-solver groupby a cross-design:Select theproblem-solver pairs of interest from the "Queue ofproblem-solver pairs".Clicking the "Convert to aproblem-solver group" button. This will complete the cross-design for the partial list and create a new row in the "Queue ofproblem-solver groups".Running aproblem-solver pairor aproblem-solver groupTo run aproblem-solver pairor aproblem-solver group, click the "Run" button in the "Queue ofproblem-solver pairs" or "Queue ofproblem-solver groups". Once theproblem-solver pairorproblem-solver grouphas been run, the "Run" button becomes disabled.Note:Running aproblem-solver paircan take anywhere from a couple of seconds to a couple of minutes depending on theproblem-solver pairand the number of macroreplications.Post-Processing and Post-NormalizationPost-processing happens before post-normalizing and after the run is complete. You can specify the number of post-replications and the (proxy) optimal solution or function value.  After post-normalization is complete, the Plotting window appears.
To exit out of the Post-Process/Normalize pop-up without post-processing or post-normalizing, click the red "x" in the top-left corner of the window.-problem-solver pairproblem-solver pairs can be post-processed from the "Queue ofproblem-solver pairs" tab by clicking "Post-Process." Adjust Post-Processing factors as necessary. Onlyproblem-solver pairs that have already been run can be post-processed. After post-processing, click the "Post-Normalize by Problem" tab to select whichproblem-solver pairs to post-normalize together.Onlyproblem-solver pairs with the same problem can be post-normalized together.Once allproblem-solver pairs of interest are selected, click the "Post-Normalize Selected" button at the bottom of the GUI (this button only appears when in the Post-Normalize tab).In the new pop-up form, update any values necessary and click "Post-Normalize" when theproblem-solver pairs are ready to be post-normalized.-problem-solver groupproblem-solver groups are post-processed and post-normalized at the same time. In the "Queue ofproblem-solver groups" tab, click the "Post-Process" button for the specificproblem-solver group, then change any values necessary, then click "Post-Process".PlottingThe Plotting page is identical for bothproblem-solver pairs andproblem-solver groups. Currently, multipleproblem-solver pairs with the same problem can be plotted together, and any problem-solver pair from a singleproblem-solver groupcan be plotted together:On the left side, select one or more problems from the problem list.Select solvers from the solver list.On the right side, select a plot type and adjust plot parameters and settings.
There are 5 settings common to most plot types: Confidence Intervals, Number of Bootstrap Samples, Confidence Level, Plot Together, and Print Max HW.
The type of plots that are currently available in the GUI are: Mean Progress Curve, Quantile Progress Curve, Solve Time CDF, Scatter Plot, CDF Solvability, Quantile Solvability, CDF Difference Plot, Quantile Difference Plot, Terminal Box/Violin, and Terminal Scatter.Click "Add."All plots will show in the plotting queue, along with information about their parameters and where the file is saved.To view one plot, click "View Plot." All plots can be viewed together by clicking "See All Plots" at the bottom of the page.To return to the main page, click the red "x" in the top-left corner of the window.ContributingYou can contribute problems and solver to SimOpt by usingpull requestsin GitHub or corresponding with the developers.PackageThesimoptlibpackage is available to download through the Python Packaging Index (PyPI) and can be installed from the terminal with the following command:python -m pip install simoptlibBasic ExampleAfter installingsimoptlib, the package's main modules can be imported from the Python console (or in code):import simopt
from simopt import models, solvers, experiment_baseThe following snippet of code will run 10 macroreplications of the Random Search solver ("RNDSRCH") on the Continuous Newsvendor problem ("CNTNEWS-1"):myexperiment = simopt.experiment_base.ProblemSolver("RNDSRCH", "CNTNEWS-1")
myexperiment.run(n_macroreps=10)The results will be saved to a .pickle file in a folder calledexperiments/outputs. To post-process the results, by taking, for example 200 postreplications at each recommended solution, run the following:myexperiment.post_replicate(n_postreps=200)
simopt.experiment_base.post_normalize([myexperiment], n_postreps_init_opt=200)A .txt file summarizing the progress of the solver on each macroreplication can be produced:myexperiment.log_experiment_results()A .txt file calledRNDSRCH_on_CNTNEWS-1_experiment_results.txtwill be saved in a folder calledexperiments/logs.One can then plot the mean progress curve of the solver (with confidence intervals) with the objective function values shown on the y-axis:simopt.experiment_base.plot_progress_curves(experiments=[myexperiment], plot_type="mean", normalize=False)The Python scripts in thedemofolder provide more guidance on how to run common experiments using the library.One can also use the SimOpt graphical user interface by running the following from the terminal:python -m simopt.GUIAuthorsThe core development team currently consists ofDavid Eckman(Texas A&M University)Sara Shashaani(North Carolina State University)Shane Henderson(Cornell University)CitationTo cite this work, please use@misc{simoptgithub,
  author = {D. J. Eckman and S. G. Henderson and S. Shashaani and R. Pasupathy},
  title = {{SimOpt}},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/simopt-admin/simopt}},
  commit = {eda24b9f6a5885a37321ad7f8534bf10dec22480}
}AcknowledgmentsAn earlier website for SimOpt,http://www.simopt.org, was developed through work supported by the National Science Foundation under grant nos. DMI-0400287, CMMI-0800688 and CMMI-1200315.
Recent work on the development of SimOpt has been supported by the National Science Foundation under grant nos. DGE-1650441, CMMI-1537394, CMMI-1254298, CMMI-1536895, CMMI-2206972, IIS-1247696, and TRIPODS+X DMS-1839346, by the Air Force Office of Scientific Research under grant nos. FA9550-12-1-0200, FA9550-15-1-0038, and FA9550-16-1-0046, and by the Army Research Office under grant no. W911NF-17-1-0094.
Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation (NSF).