Warm_starting_CG_for_MIO_ML

Goals ⚽

The aim of this repository is to provide some details of the data sets used in paper [1], as well as the code used to obtain the results. This article has been developed by some members of the OASYS group thanks to the funding of the project Flexanalytics. We suggest you to visit the related links to know more our research 😉

How can I download the data? ⬇

Please, click at this link.

How can I download the code? ⬇

Please, click at this link

Summary 🧮📊📖

Three cases of study have been analyzed in [1]: a toy example, a synthetic MILP, and the real-world application, namely the Unit Commitment problem. The data from the toy example can be seen in Section 4 of the paper. In addition, the files of the two large-size data sets are given below:

1. Synthetic MILP:

   • Objetive function coefficients, c.
   • Matrix coefficients, a.
   • Lower bounds continuous variables, l.
   • Upper bounds continuous variables, u.
   • Independent term, b.

2. Unit Commitment:

   • Generators info.
   • Lines info.
   • PTDF info.
   • Demand info.

References 📚

[1] Jiménez-Cordero, A., Morales, J.M., & Pineda, S. (2022). Warm-starting constraint generation for mixed-integer optimization: A Machine Learning approach. . Knowledge-Based Systems, 253, 109570, https://doi.org/10.1016/j.knosys.2022.109570.

[2] OASYS, Warm_starting_CG_for_MIO_ML, Github repository (https://github.com/groupoasys/Warm_starting_CG_for_MIO_ML), 2022.

How to cite the repo and the paper? 📝

If you want to cite paper [1] or this repo [2], please use the following bib entry:

• Article:

@article{jimenezcordero2022warm,
title = {Warm-starting constraint generation for mixed-integer optimization: A Machine Learning approach},
journal = {Knowledge-Based Systems},
volume = {253},
pages = {109570},
year = {2022},
doi = {https://doi.org/10.1016/j.knosys.2022.109570},
url = {https://www.sciencedirect.com/science/article/pii/S0950705122007894},
author = {Asunción Jiménez-Cordero and Juan Miguel Morales and Salvador Pineda}
}

• Repository:

@misc{OASYS2022warm,
author={OASYS},
  year={2022},
  title = {{Warm\_starting\_CG\_for\_MIO\_ML}},
  howpublished = {\url{https://github.com/groupoasys/Warm_starting_CG_for_MIO_ML}}
}

Do you want to contribute? 🙋‍♀️🙋‍♂️

Please, do it 😋 Any feedback is welcome 🤗 so feel free to ask or comment anything you want via a Pull Request in this repo. If you need extra help, you can ask Asunción Jiménez-Cordero (asuncionjc@uma.es), Juan Miguel Morales (juan.morales@uma.es) or Salvador Pineda (spinedamorente@gmail.com).

Contributors 🌬☀

• OASYS group - groupoasys@gmail.com

Developed by 👩‍💻👨‍💻👨‍💻

• Asunción Jiménez Cordero - asuncionjc@uma.es
• Juan Miguel Morales - juan.morales@uma.es
• Salvador Pineda - spinedamorente@gmail.com

License 📝

Copyright 2021 Optimization and Analytics for Sustainable energY Systems (OASYS)

Licensed under the GNU General Public License, Version 3 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.gnu.org/licenses/gpl-3.0.en.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.