Multi-Objective Integer Programming with Gurobi and Python
Project description
Multi-Objective Integer Programming with Gurobi and Python
The optimisation software from Gurobi now supports multi-objective programming.
Since there are multiple objectives, there may be many solutions, each of which may optimise the objectives with a different set of priorities. Finding all such solutions can be algorithmically costly, so Gurobi’s solver only returns a single result.
However, due to the importance of such functionality, much research has been invested into finding better algorithms that can efficiently find all solutions.
This python package extends Gurobi’s multi-objective functionality by using the algorithms developed by (Ozlen et al., 2014) and (Tamby & Vanderpooten, 2020). It provides a module that can be used in python programs, as well as a command line tool that can read multi-objective LP files.
Free software: MIT license
Installation
pip install moiptimiser
You can also install the in-development version with:
pip install https://github.com/bayan/python-moiptimiser/archive/master.zip
Documentation
Development
Install python libraries:
pip install cmake dlib gurobipy tox twine wheel bumpversion
To run the all tests run:
tox
To create a new patch and upload to github:
bumpversion patch git push -u origin master git push -u origin master vX.X.X
To package and deploy to PyPI:
python setup.py clean --all sdist bdist_wheel twine upload --skip-existing dist/*.whl dist/*.gz
To run as a script from the command line:
cd src/ python3 -m moiptimiser /path/to/example.lp
References
Ozlen, M., Burton, B.A., MacRae, C.A.G., 2014. Multi-Objective Integer Programming: An Improved Recursive Algorithm. J Optim Theory Appl 160, 470–482. https://doi.org/10.1007/s10957-013-0364-y
Tamby, S., & Vanderpooten, D. (2020). Enumeration of the Nondominated Set of Multiobjective Discrete Optimization Problems. INFORMS Journal on Computing, 33(1), 72–85. https://doi.org/10.1287/ijoc.2020.0953
Changelog
0.0.0 (2020-05-13)
First release on PyPI.
0.0.1 (2020-05-19)
First working version, using the (Ozlen et al., 2014) algorithm, for minimisation problems.
0.0.2 (2020-05-19)
Implemented maximisation problem solving.
Improved documentation.
Improved testing suite.
0.0.3 (2021-11-24)
New state of the art algorithm - two stage approach from (Tamby & Vanderpooten, 2020) - implemented and set as the default for the command line executable.
0.0.4 (2021-11-24)
Documentation and changelog changes that were missed in previous release.
0.0.5 (2021-11-24)
Specify python version to prevent failing documentation build on https://readthedocs.org/
0.0.6 (2021-12-05)
Specify which algorithm to use from the command line.
Keep track of the number of solver calls and report in output.
Bug fixes to (Tamby & Vanderpooten, 2020) implementation.
0.0.7 (2021-12-05)
Provide feasible solutions to solver for (Tamby & Vanderpooten, 2020) implementation.
Lower required python version number from 3.9 to 3.7 to get online docs compiling.
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