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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.
Many of the examples found in this project have been converted from the examples shared by William Pettersson on figshare, which were used to benchmark the parallel processing algorithm described in (Pettersson & Ozlen, 2019).
- Free software: MIT license
pip install moiptimiser
You can also install the in-development version with:
pip install https://github.com/bayan/python-moiptimiser/archive/master.zip
https://python-moiptimiser.readthedocs.io/
Install python libraries:
pip install cmake dlib gurobipy tox twine wheel bumpversion
To run the all tests and checks run:
tox
To run the all python automated tests run:
tox -e py39
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
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
Pettersson, W., & Ozlen, M. (2019). Multiobjective Integer Programming: Synergistic Parallel Approaches. INFORMS Journal on Computing, 32(2), 461–472. https://doi.org/10.1287/ijoc.2018.0875
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