Software:OR-Tools
 | |
| Original author(s) | Laurent Perron |
|---|---|
| Developer(s) | Google Optimization team[1] |
| Initial release | September 15, 2010 |
| Stable release | v9.9.3963[2]
/ March 7, 2024 |
| Repository | github |
| Written in | C++ |
| Operating system | Linux, macOS, Microsoft Windows |
| Type | Library |
| License | Apache License 2.0 |
| Website | developers |
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-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
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
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
References
- ↑ 1.0 1.1 "Sudoku, Linear Optimization, and the Ten Cent Diet". https://ai.googleblog.com/2014/09/sudoku-linear-optimization-and-ten-cent.html.
- ↑ "Release v9.9". https://github.com/google/or-tools/releases/tag/v9.9.
- ↑ 3.0 3.1 "Google OR-Tools a guide". February 24, 2019. https://medium.com/google-or-tools/google-or-tools-a-guide-39f439a5cd0f.
- ↑ "LICENSE-2.0.txt". https://github.com/google/or-tools/blob/stable/LICENSE-2.0.txt.
- ↑ Perron, Laurent (2011-07-01). "Operations Research and Constraint Programming at Google". Lee J. (Eds) Principles and Practice of Constraint Programming – CP 2011. Lecture Notes in Computer Science. 6876. pp. 2. doi:10.1007/978-3-642-23786-7_2. ISBN 978-3-642-23786-7.
- ↑ 6.0 6.1 "The CP-SAT Primer". Dec 2, 2024. https://d-krupke.github.io/cpsat-primer/.
- ↑ "The MiniZinc Challenge". https://www.minizinc.org/challenge.html.
- ↑ "Homebrew package". https://formulae.brew.sh/formula/or-tools.
- ↑ "com.google.ortools:ortools-java". https://mvnrepository.com/artifact/com.google.ortools/ortools-java.
- ↑ "Google.OrTools". https://www.nuget.org/packages/Google.OrTools/.
- ↑ "ortools". https://pypi.org/project/ortools/.
- ↑ "Stan Store". toolsforcreators.org. https://toolsforcreators.org/stan-store-review-features-setup-ease-pricing-breakdown/.
- ↑ "OR-Tools introduction". https://developers.google.com/optimization/introduction/overview.
- ↑ 14.0 14.1 "Application of Google OR-Tools". https://www.kaggle.com/jpmiller/application-of-google-or-tools.
- ↑ 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". https://activimetrics.com/blog/ortools/multiday_tsp.
- ↑ "Software with FlatZinc implementations". https://www.minizinc.org/software.html#flatzinc.
Bibliography
- 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. https://www.oreilly.com/library/view/practical-python-ai/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 (Open Publishing Association) 306: 259–265. doi:10.4204/eptcs.306.30. ISSN 2075-2180.
- 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.
External links
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