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OR-Tools
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| OR-Tools | |
|---|---|
 | |
| Original author | Laurent Perron |
| Developer | Google Optimization team[1] |
| Initial release | September 15, 2010 |
| Stable release | v9.9.3963[2]
/ March 7, 2024 |
| Written in | C++ |
| Operating system | Linux, macOS, Microsoft Windows |
| Type | Library |
| License | Apache License 2.0 |
| Website | developers |
| Repository | github |
Google OR-Tools is a free and open-source software suite developed by Google for solving linear programming (LP), mixed integer programming (MIP), constraint programming (CP), vehicle routing (VRP), and related optimization problems.[3] OR stands for operations research.
OR-Tools is a set of components written in C++ but provides wrappers for Java, .NET and Python.
It is distributed under the Apache License 2.0.[4]
History
[edit]OR-Tools was created by Laurent Perron in 2011.[5]
In 2014, Google's open source linear programming solver, GLOP, was released as part of OR-Tools.[1]
The CP-SAT solver[6] bundled with OR-Tools has been consistently winning gold medals in the MiniZinc Challenge,[7] an international constraint programming competition.
Features
[edit]The OR-Tools supports a variety of programming languages, including:
- Object-oriented interfaces for C++[8]
- A Java wrapper package[9]
- A .NET and .NET Framework wrapper package[10]
- A Python wrapper package[11][12]
OR-Tools supports a wide range of problem types,[13][3] among them:
- Assignment problem[14]
- Linear programming
- Mixed-integer programming[15]
- Constraint programming[6]
- Vehicle routing problem[14][16]
- Network flow algorithms
It supports the FlatZinc modeling language.[17]
See also
[edit]References
[edit]- ^ a b "Sudoku, Linear Optimization, and the Ten Cent Diet". ai.googleblog.com.
- ^ "Release v9.9". github.com.
- ^ a b "Google OR-Tools a guide". medium.com. February 24, 2019.
- ^ "LICENSE-2.0.txt". github.com.
- ^ Perron, Laurent (July 1, 2011). "Operations Research and Constraint Programming at Google". Lee J. (Eds) Principles and Practice of Constraint Programming – CP 2011. Lecture Notes in Computer Science. Vol. 6876. p. 2. doi:10.1007/978-3-642-23786-7_2. ISBN 978-3-642-23786-7. S2CID 38166333.
- ^ a b "The CP-SAT Primer". d-krupke.github.io. December 2, 2024.
- ^ "The MiniZinc Challenge". minizinc.org.
- ^ "Homebrew package". formulae.brew.sh.
- ^ "com.google.ortools:ortools-java". mvnrepository.com.
- ^ "Google.OrTools". nuget.org.
- ^ "ortools". pypi.org.
- ^ "Stan Store". toolsforcreators.org.
- ^ "OR-Tools introduction". Google Developers. Archived from the original on October 1, 2021. Retrieved October 1, 2021.
- ^ a b "Application of Google OR-Tools". kaggle.com.
- ^ Louat, Christophe (2009). Etude et mise en œuvre de stratégies de coupes efficaces pour des problèmes entiers mixtes 0-1 (PhD). Vol. 1. Université de Versailles Saint-Quentin-en-Yvelines. p. 144.
- ^ "Routing use case". activimetrics.com.
- ^ "Software with FlatZinc implementations". minizinc.org.
Bibliography
[edit]- Kruk, Serge (February 26, 2018). Practical Python AI Projects: Mathematical Models of Optimization Problems with Google OR-Tools (1st ed.). O'Reilly Media. ISBN 9781484234235.
- Da Col, Giacomo; Teppan, Eric C. (2019). "Google vs IBM: A Constraint Solving Challenge on the Job-Shop Scheduling Problem". Electronic Proceedings in Theoretical Computer Science. 306. Open Publishing Association: 259–265. arXiv:1909.08247. doi:10.4204/eptcs.306.30. ISSN 2075-2180. S2CID 202660711.
- Li, Mengyun; Chow, Joseph (April 2021). "School Bus Routing Problem with a Mixed Ride, Mixed Load, and Heterogeneous Fleet". Transportation Research Record: Journal of the Transportation Research Board. 2675 (7): 467–479. doi:10.1177/03611981211016860. S2CID 237618523.
