OR-Tools

Short description: Open source software suite by Google

OR-Tools

Original author(s)| Laurent Perron
Developer(s)| Google Optimization team[1]
Initial release| September 15, 2010; 13 years ago (2010-09-15)
Stable release|

v9.4.1874[2] / August 12, 2022; 23 months ago (2022-08-12)

Repository| github.com/google/or-tools
Written in| C++
Operating system| Linux, macOS, Microsoft Windows
Type| Library
License| Apache License 2.0
Website| developers.google.com/optimization/

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][4]

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.[5]

## Contents

* 1 History
* 2 Features
* 3 See also
* 4 References
* 5 Bibliography
* 6 External links

## History

OR-Tools was created by Laurent Perron in 2011.[6]

In 2014, Google's open source linear programming solver, GLOP, was released as part of OR-Tools.[1]

The CP-SAT solver[7] bundled with OR-Tools won a total of eleven gold medals between 2018 and 2020 in the MiniZinc Challenge,[8] an international constraint programming competition.

## Features

The OR-Tools supports a variety of programming languages, including:

* Object-oriented interfaces for C++[9]
* A Java wrapper package[10]
* A .NET and .NET Framework wrapper package[11]
* A Python wrapper package[12]

OR-Tools supports a wide range of problem types,[13][3] among them:

* Assignment problem[14][15]
* Linear programming
* Mixed-integer programming[16]
* Constraint programming[7]
* Vehicle routing problem[14][17]
* Network flow algorithms

It supports the FlatZinc modeling language.[18]

## See also

* COIN-OR
* CPLEX
* GLPK
* SCIP (optimization software)
* FICO Xpress
* MOSEK

## References

1. ↑ 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.
2. ↑ "Release v9.4". https://github.com/google/or-tools/releases/tag/v9.4.
3. ↑ 3.0 3.1 "Google OR-Tools a guide". February 24, 2019. https://medium.com/google-or-tools/google-or-tools-a-guide-39f439a5cd0f.
4. ↑ "We help you implement OR-tools technology". https://www.solvice.io/technology/or-tools.
5. ↑ "LICENSE-2.0.txt". https://github.com/google/or-tools/blob/stable/LICENSE-2.0.txt.
6. ↑ 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: 2. doi:10.1007/978-3-642-23786-7_2. ISBN 978-3-642-23786-7.
7. ↑ 7.0 7.1 "How the CP-SAT solver works". April 25, 2020. https://www.xiang.dev/explaining-cp-sat/.
8. ↑ "The MiniZinc Challenge". https://www.minizinc.org/challenge.html.
9. ↑ "Homebrew package". https://formulae.brew.sh/formula/or-tools.
10. ↑ "com.google.ortools:ortools-java". https://mvnrepository.com/artifact/com.google.ortools/ortools-java.
11. ↑ "Google.OrTools". https://www.nuget.org/packages/Google.OrTools/.
12. ↑ "ortools". https://pypi.org/project/ortools/.
13. ↑ "OR-Tools introduction". https://developers.google.com/optimization/introduction/overview.
14. ↑ 14.0 14.1 "Application of Google OR-Tools". https://www.kaggle.com/jpmiller/application-of-google-or-tools.
15. ↑ "Google OR-Tools. Business value and potential". https://freshcodeit.com/google-or-tools.
16. ↑ Louat, Christophe (2009). Etude et mise en œuvre de stratégies de coupes efficaces pour des problèmes entiers mixtes 0-1 (PhD). 1. Université de Versailles Saint-Quentin-en-Yvelines. p. 144.
17. ↑ "Routing use case". https://activimetrics.com/blog/ortools/multiday_tsp.
18. ↑ "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. http://dx.doi.org/10.4204/EPTCS.306.30.
* 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

* Source code
* Video introduction to OR-Tools

* v
* t
* e

Mathematical optimization software

Data formats|

* Mathematica
* MPS
* nl
* sol

Modeling tools|

* AIMMS
* AMPL
* APMonitor
* ECLiPSe-CLP
* GEKKO
* GAMS
* GNU MathProg
* JuMP
* LINDO
* OPL
* Mathematica
* OptimJ
* PuLP
* Pyomo
* TOMLAB
* Xpress-Mosel
* ZIMPL

LP, MILP∗ solvers|

* APOPT∗
* Artelys Knitro∗
* BCP∗
* CLP
* CBC∗
* CPLEX∗
* FortMP∗
* GCG∗
* GLPK/GLPSOL∗
* Gurobi∗
* LINDO∗
* Lp_solve
* LOQO
* Mathematica
* MINOS
* MINTO∗
* MOSEK∗
* SCIP∗
* SoPlex
* SYMPHONY∗
* Xpress-Optimizer∗

QP, MIQP∗ solvers|

* APOPT∗
* Artelys Knitro∗
* CBC∗
* CLP
* CPLEX∗
* FortMP∗
* Gurobi∗
* IPOPT
* LINDO∗
* Mathematica
* MINOS
* MOSEK∗
* SCIP∗
* Xpress-Optimizer∗

QCP, MIQCP∗ solvers|

* APOPT∗
* Artelys Knitro∗
* CPLEX∗
* Gurobi∗
* IPOPT
* LINDO∗
* Mathematica
* MINOS
* MOSEK∗
* SCIP∗
* Xpress-Optimizer∗
* Xpress-SLP∗

SOCP, MISOCP∗ solvers|

* Artelys Knitro∗
* CPLEX∗
* Gurobi∗
* LINDO∗
* LOQO
* Mathematica
* MOSEK∗
* SCIP∗
* Xpress-Optimizer∗

SDP, MISDP∗ solvers|

* Mathematica
* MOSEK

NLP, MINLP∗ solvers|

* AOA∗
* APOPT∗
* Artelys Knitro∗
* BARON∗
* Couenne∗
* Galahad library
* IPOPT
* LINDO∗
* LOQO
* MIDACO∗
* MINOS
* NLPQLP
* NPSOL
* SCIP∗
* SNOPT∗
* WORHP
* Xpress-SLP∗

GO solvers|

* BARON
* Couenne∗
* Mathematica
* LINDO
* SCIP

CP solvers|

* Comet
* CPLEX CP Optimizer
* Gecode
* Mathematica
* JaCoP

Metaheuristic solvers|

* OptaPlanner

* List of optimization software
* Comparison of optimization software

* v
* t
* e

Google free and open-source software (FOSS)

Software|

| Applications|

* Chromium
* OpenRefine
* Tesseract

|

Programming languages|

* Dart
* Go
* Sawzall

Frameworks and
development tools|

* AMP
* Angular
* AngularJS
* Beam
* Blockly
* Closure Tools
* Cpplint
* FlatBuffers
* Flutter
* Gears
* Gerrit
* Guava
* gRPC
* Gson
* Guetzli
* Guice
* gVisor
* Kubernetes
* LevelDB
* libvpx
* Polymer
* TensorFlow

Operating systems|

* AOSP
* Chromium OS
* Fuchsia
* gLinux
* Goobuntu

Related|

* Code-in
* Open Source Security Foundation
* Summer of Code

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