https://github.com/holgerbrandl/kortools

A kotlin API around OR Tools
https://github.com/holgerbrandl/kortools

constraintprogramming industrialengineering kotlin lp mip optimization

Last synced: about 1 month ago
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A kotlin API around OR Tools

• Host: GitHub
• URL: https://github.com/holgerbrandl/kortools
• Owner: holgerbrandl
• License: mit
• Created: 2023-01-28T17:49:36.000Z (almost 2 years ago)
• Default Branch: master
• Last Pushed: 2023-01-30T08:19:35.000Z (almost 2 years ago)
• Last Synced: 2024-04-24T03:15:56.956Z (8 months ago)
• Topics: constraintprogramming, industrialengineering, kotlin, lp, mip, optimization
• Language: Kotlin
• Homepage:
• Size: 85.9 KB
• Stars: 2
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGES.md
  • License: LICENSE

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README

        
# kortools

A kotlin API around [OR Tools](https://developers.google.com/optimization) 

[ ![Download](https://img.shields.io/github/v/release/holgerbrandl/kortools) ](https://github.com/holgerbrandl/kortools/releases) [![Build Status](https://github.com/holgerbrandl/kortools/workflows/build/badge.svg)](https://github.com/holgerbrandl/kortools/actions?query=workflow%3Abuild) [![slack](https://img.shields.io/badge/slack-datascience-yellowgreen)](https://kotlinlang.slack.com/messages/datascience/)

`kortools` provides a more natural API to express optimization problems on the JVM with the great OR as solver backend.

`kortools` is designed for simulation practitioners, process analysts and industrial engineers, who need to go beyond the limitations of existing simulation tools to model and optimize their business-critical use-cases.

## Example

```kotlin

// Create the linear solver with the GLOP backend.

val solver = createSolver(SolverId.GLOP) {

    // Create the variables x and y.

    val x by makeVariable(0.0, 1.0)

    val y by makeVariable(0.0, 1.0)

    // Create a linear constraint, 0 <= x + y <= 2.

    constraint("ct", 0.0 to 2.0) {

        x withCoeff 1.0

        y withCoeff 1.0

    }

    // Create the objective function, 3 * x + y.

    maximize {

        3 * x + y

    }

}

// solve the problem

solver.solve()

with(solver) {

    println("Solution:")

    println("Number of variables = " + numVariables())

    println("Number of constraints = " + numConstraints())

    println("Objective value = " + objective().value())

    println("x = " + variable("x").solutionValue())

    println(

        "y = " + variable("y").solutionValue()

    )

}

```

## Jupyter

An easy way to get started with `kortools` is with jupyter, you simply need to install the [kotlin-jupyter](https://github.com/Kotlin/kotlin-jupyter) kernel and load the library with:

```bash

%use kortools

```

See [here](misc/optimize_cvrp.ipynb) for a notebook example.

## Setup

Add the following artifact to your `gradle.build`

```

compile "com.github.holgerbrandl:kortools:0.5"

```

You can also use [JitPack with Maven or Gradle](https://jitpack.io/#holgerbrandl/kortools/-SNAPSHOT) to build the latest snapshot as a dependency in your project.

```groovy

repositories {

    maven { url 'https://jitpack.io' }

}

dependencies {

        compile 'com.github.holgerbrandl:kortools:-SNAPSHOT'

}

```

To build and install it into your local maven cache, simply clone the repo and run

```bash

./gradlew install

```

## Misc References

https://github.com/google/or-tools/tree/stable/ortools/linear_solver/samples