ilp_schedule is a python interface with the cutting edge integer linerar programming optimizer SCIP developed at the Konrad Zuse Institute Berlin. The code is enabling developers to build user oriented software to solve large scale scheduling problems.

This project uses the SCIP Optimization Suite and is to be handled with the according attention to commercial usage rights. In order to install follow the installation description in the Requirements.

PySCIPOpt requires a working installation of the SCIP Optimization Suite. If SCIP is not installed in the global path you need to specify the install location using the environment variable SCIPOPTDIR:

• on Linux and OS X:

  export SCIPOPTDIR=<path_to_install_dir>
• on Windows:

  set SCIPOPTDIR=<path_to_install_dir>

SCIPOPTDIR needs to have a subdirectory lib that contains the library, e.g. libscip.so (for Linux) and a subdirectory include that contains the corresponding header files:

SCIPOPTDIR
  > lib
    > libscip.so ...
  > include
    > scip
    > lpi
    > nlpi
    > ...

If you are not using the installer packages, you need to install the SCIP Optimization Suite using CMake. The Makefile system is not compatible with PySCIPOpt!

On Windows it is highly recommended to use the Anaconda Python Platform.

pip install pyscipopt

On Windows you may need to ensure that the scip library can be found at runtime by adjusting your PATH environment variable:

• on Windows: set PATH=%PATH%;%SCIPOPTDIR%\bin

On Linux and OS X this is encoded in the generated PySCIPOpt library and therefore not necessary.

PySCIPOpt requires Cython, at least version 0.21 (pip install cython). Furthermore, you need to have the Python development files installed on your system (error message "Python.h not found"):

sudo apt-get install python-dev   # for Python 2, on Linux
sudo apt-get install python3-dev  # for Python 3, on Linux

After setting up SCIPOPTDIR as specified above, please run

python setup.py install

You may use the additional options --user or --prefix=<custom-python-path>, to build the interface locally.

To use debug information in PySCIPOpt you need to build it like this:

python setup.py install --debug

Be aware that you will need the debug library of the SCIP Optimization Suite for this to work (cmake .. -DCMAKE_BUILD_TYPE=Debug).

The software is build like a server-client application. You start the server in one command window with

python server.py

When the server is running you enter in another command window

python client.py

When the client connects to the server the computations of the optimizer will be carried out on another thread. Messages from the client are interpreted in server.py in the function

check_for_commands(sharedMem,data)

please put your breakpoints there to interactively understand the formats and commands.