Linny-R is an executable graphical specification language for mixed integer linear programming (MILP) problems, especially unit commitment problems (UCP) and generation expansion planning (GEP).

The graphical language and WYSIWYG model editor are developed by Pieter Bots at Delft University of Technology.

Originally implemented in Delphi Pascal, Linny-R is now developed in HTML+CSS+JavaScript so as to be platform-independent and 100% transparent open source (under the MIT license). The software comprises a server that runs on Node.js, and a graphical user interface (GUI) that runs in any modern browser.

These instruction videos published on YouTube give an idea of what Linny-R can do.

User documentation for Linny-R is still scant, but it is growing. You can contribute yourself (in "wiki fashion") via the official user documentation site https://linny-r.info. Technical documentation will be developed on GitHub: https://github.com/pwgbots/linny-r/wiki

Installing Node.js

Linny-R is developed as a JavaScript package, and requires that Node.js is installed on your computer. This software can be downloaded from https://nodejs.org. Make sure that you choose the correct installer for your computer. Linny-R is developed using the current release. Presently (November 2023) this is 21.2.0.

Run the installer and accept the default settings. There is no need to install the optional Tools for Native Modules.

Open the Command Line Interface (CLI) of your computer. On macOS, this will be Terminal, on Windows Command Prompt. Verify the installation by typing:

node --version

The response should be the version number of Node.js, for example: v21.2.0.

Installing Linny-R

It is advisable to install Linny-R in a directory on your computer, not in a cloud. In this installation guide, the path to this directory is denoted by Linny-R, so in all commands you should replace this with the actual directory path. On a Windows machine the suggested path is C:\Users\(your user name)\Documents\Linny-R, and on a macOS machine /Users/(your user name)/Linny-R.

To install Linny-R in this directory, first change to the parent directory like so:

cd /Users/(your user name)

Then create the Linny-R directory:

mkdir Linny-R

then change to it:

cd Linny-R

and then type at the command line prompt:

npm install --prefix . linny-r

[!IMPORTANT] The spacing around the dot is essential. Type the command in lower case.

After installation has completed, Linny-R should have this directory tree structure:

Linny-R should contain two JSON files package.json and package-lock.json that should not be removed, or you will have to re-install Linny-R. It should also contain a script file to facilitate (single click) launch: on a macOS machine the shell script linny-r.command, on a Windows machine the batch script linny-r.bat. By default, this script file contains two commands: first change to the Linny-R directory and then tell Node.js to launch the start the Linny-R server.

[!NOTE] When configuring Linny-R for a network environment where individual users each have their personal work space (e.g., a virtual drive U:), you must edit this script file, adding the argument workspace=path/to/workspace to the node command. This will instruct Linny-R to create the user directory in this workspace directory instead of the Linny-R directory.

The linny-r directory should contain this file README.md, the files server.js and console.js that will be run by Node.js, and the sub-directory static. This static directory should contain three HTML files:

• index.html (the browser-based GUI)
• show-png.html (to render SVG diagrams as PNG images)
• show-diff.html (to display differences betwee two Linny-R models)

It should also contain the style sheet linny-r.css required by the GUI.

The sub-directories of static contain files that are served to the browser by the script server.js when it is running in Node.js.

Installing and using an earlier version of Linny-R

By default, npm will install the latest release of the Linny-R software. As this software is developed as part of academic research, new features are added without rigorous testing. Although much effort is dedicated to maintaining upward and downward compatibility, you may find that the latest version does not work as well for you as some earlier version. To re-install an earlier release, for example version 1.4.0, open the CLI, change to your Linny-R directory, and then type:

npm install [email protected]

[!NOTE] This will overwrite the contents of the node_modules directory, but it will not affect the files in your user space.

If you prefer to have different versions of Linny-R on your computer, you can create a separate directory for a specific version, then change to this directory and type:

npm install --prefix . [email protected]

[!NOTE] To run a specific version in your browser, you must start the server from the directory where you installed this version. Should you wish to run two different versions concurrently, you must use the port=[number] option when you start the server for the second version.

Configuring the MILP solver

Linny-R presently supports five MILP solvers: Gurobi, MOSEK, CPLEX, SCIP and LP_solve. Gurobi, MOSEK and CPLEX are considerably more powerful than the open source solvers SCIP and LP_solve, but they require a license. Academic licenses can be obtained by students and staff of eligible institutions.

[!IMPORTANT] When installing a solver, it is advisable to accept the default file locations that are proposed by the installer. After installation, do not move files to some other directory, as this is bound to cause problems.

Installing Gurobi

The software you need to install is Gurobi Optimizer. More information on how to obtain a license, and instructions for installing Gurobi on your computer can be obtained via this URL: https://www.gurobi.com/academia/academic-program-and-licenses/

When running a model, Linny-R will try to execute the command line application gurobi_cl. It will look for this application in the directory specified in the environment variable PATH on your computer.

Installing CPLEX

The software you need to install is CPLEX. More information on how to obtain a license, and instructions for installing CPLEX on your computer can be obtained via this URL: https://www.ibm.com/products/ilog-cplex-optimization-studio

When running a model, Linny-R will try to execute the command line application cplex. It will look for this application in the directory specified in the environment variable PATH or more specifically in the environment variable CPLEX_STUDIO_BINARIESnnnn (where nnnn denotes the CPLEX version number) on your computer.

Installing MOSEK

The software you need to install is MOSEK. More information on how to obtain a license, and instructions for installing MOSEK on your computer can be obtained via this URL: https://www.mosek.com/resources/getting-started/

When running a model, Linny-R will try to execute the command line application mosek. It will look for this application in the directory specified in the environment variable PATH on your computer.

Installing SCIP

The SCIP software is open source. Instructions for installation can be found via this URL: https://scipopt.org/doc/html/INSTALL.php

When running a model, Linny-R will try to execute the command line application scip. It will look for this application in the directory specified in the environment variable PATH on your computer.

Installing LP_solve

The LP_solve software is open source and can be downloaded via this URL: https://sourceforge.net/projects/lpsolve

To facilitate installation, the executable files for Windows and macOS can be downloaded from the Linny-R website at Delft University of Technology: https://sysmod.tbm.tudelft.nl/linny-r/lp_solve

There you will find links to download LP_solve applications that have been compiled for different platforms. If you do not know which platform to choose, run Linny-R as described below, and the platform will be listed in its output. If no matching LP_solve version is listed, you can try to compile the software from its source. How to do this is explained on the page "Installing LP_solve on a Mac" on the Linny-R documentation site: https://linny-r.info

When you have downloaded the file (just lp_solve for macOS, lp_solve.exe for Windows), you must copy or move this file to your Linny-R directory, as this is where Linny-R will look for it when it does not find one of the other solvers.

On a macOS machine, you must then make the file lp_solve executable. Open Terminal and change to your Linny-R directory, and then type:

chmod +x lp_solve

When you then type:

./lp_solve -h

a window may appear that warns you that the software may be malicious. To allow running LP_solve, you must then go to Security & Privacy (via System Preferences) and there click the Open Anyway button in the General pane to confirm that you wish to use LP_solve. Then return to Terminal and once more type ./lp_solve -h. The response should then be a listing of all the command line options of LP_solve. If you reach this stage, Linny-R will be able to run LP_solve.

Running Linny-R

Open the Command Line Interface (CLI) of your computer, change to your Linny-R directory and type:

node node_modules/linny-r/server launch

This response should be something similar to:

Meanwhile, your default web browser should have opened a tab for the local server URL, which by default will be http://127.0.0.1:5050. The Linny-R GUI should show in your browser window, while in the CLI you should see a long series of server log messages like:

[!IMPORTANT] Do not close the CLI. If you do, the Linny-R GUI may still be visible in your browser, but you will be warned that it cannot connect to the server (at 127.0.0.1:5050). This means that you have to restart Linny-R from a new CLI.

After loading into the browser, Linny-R will try to connect to the solver. If successful, a notification (blue background) will appear on the status bar at the bottom of the window, stating the name of the solver.

You can then test the GUI by creating a simple model. Make one that has at least one process that outputs a product, and this product must have a price or a set lower bound, otherwise the model will have no objective function. Then click on the Solve button at the bottom of the left-hand tool bar. The Linny-R icon in the upper left corner should start rotating, while the status bar at the bottom should display:

For a small test model, this message should appear only very briefly, and then the diagram will be updated to reflect the obtained solution. Meanwhile, in the CLI, you should see a server log message like:

To end a modeling session, you can shut down the server by clicking on the local host icon in the upper right corner of the Linny-R GUI in your browser, confirming that you want to leave, and then closing your browser (tab). If you do not shut down the server from the browser, you can also stop the server by repeatedly pressing Ctrl+C in the CLI.

Command line options

Optionally, you can add more arguments to the node command:

Click-start for Linny-R

To facilitate start-up, you can create a shortcut icon for Linny-R on your desktop.

On a Windows machine, open the File Explorer, select your Linny-R folder, right-click on the batch file linny-r.bat, and select the Create shortcut option. Then right-click on the shortcut file to edit its properties, and click the Change Icon button. The dialog that then appears will allow you to go to the sub-folder node_modules\linny-r\static\images, where you should select the file linny-r.ico. Finally, rename the shortcut to Linny-R and move or copy it to your desktop.

On a macOS machine, open Terminal and change to your Linny-R directory, and then type:

chmod +x linny-r.command

to make the script file executable. To set the icon, use Finder to open the folder that contains the file linny-r.command, click on its icon (which still is plain) and open the Info dialog by pressing Cmd+I. Then open your Linny-R folder in Finder, change to the sub-folder node_modules/linny-r/static/images, and from there drag/drop the file linny-r.icns on the icon shown in the top left corner of the Info dialog.

User workspace

The user workspace is created when the server is run for the first time. The sub-directories of this directory user are used by Linny-R to store files.

• autosave will contain models that have been auto-saved
• channel and callback will be used to interact with Linny-R via its Receiver
• data will be used by the Dataset Manager to locate datasets for which a path has been specified
• diagrams will be used to render Scalable Vector Graphics (SVG) files as Portable Network Graphics (PNG) using Inkscape (if installed)
• modules will contain models stored in the local host repository
• reports will contain text files with time series data and statistics in tab-separated format that can be imported or copy/pasted into Excel
• solver will contain the files that are exchanged with the Mixed Integer Linear Programming (MILP) solver (the names of the files that will appear in this directory may vary, depending on the MILP-solver you use)

[!NOTE] By default, the user directory is created in your Linny-R directory. You can overrule this by starting the server with the workspace=[path] option. This will create a new, empty workspace (the directories listed above) in the specified path. It will not affect or duplicate information from existing workspaces.

Installing Inkscape

Linny-R creates its diagrams and charts as SVG images. When you download a diagram, it will be saved as a .svg file. These files can be viewed and edited using Inkscape, an open source vector graphics editor.

As it may be tedious to first save a diagram as SVG and then render it manually as a bitmap image, Linny-R features a Render diagram as bitmap button on the top toolbar, and on the bottom toolbar of the Chart manager. When you click it, Linny-R will send the image as SVG to the server. The server script will save the SVG in the user/diagrams sub-directory, and then try to execute an Inkscape command that will convert this SVG to a PNG image file in the same directory. The file name will be diagram-(date and time).png. Meanwhile, the browser will have opened a new tab that will be "waiting" for this PNG image to become available. If rendering was successful, the image will appear in this browser tab; if rendering failed, the original SVG image will be shown.

To install Inkscape, please look here: https://inkscape.org/release

Linny-R will automatically detect whether Inkscape is installed by searching for it in the environment variable PATH on your computer. On a macOS computer, Linny-R will look for Inkscape in /Applications/Inkscape.app/Contents/MacOS.

[!NOTE] The installation wizard for Inkscape (version 1.3) may not add the application to the PATH variable. Please check whether you need to do this yourself.

Using Linny-R console

The console-only version of Linny-R allows you to run a Linny-R model without a web browser. This may be useful when you want run models from a script (shell script, Python, ...). If you open a CLI box, change to your Linny-R directory, and then type:

node node_modules/linny-r/console (on Windows, use backslashes)

you will see the command line options that allow you to run models in various ways.

[!NOTE] The console-only version is still in development, and does not provide all functions yet.

Troubleshooting problems

If during any of the steps above you encounter problems, please try to diagnose them and resolve them yourself. You can find a lot of useful information on the Linny-R user documentation website: https://linny-r.info.

[!IMPORTANT] To diagnose a problem, always look in the CLI box where Node.js is running, as informative server-side error messages will appear there.

Then also look at the console window of your browser. Most browsers offer a Web Developer Tools option via their application menu. This will allow you to view the browser console, which will display JavaScript errors in red font.

If you've tried hard, but failed, you can try to contact Pieter Bots at [email protected]