[](https://docs.rs/vrp-core)
[](https://crates.io/crates/vrp-cli)
[](https://crates.io/crates/vrp-core)
[](https://codecov.io/gh/reinterpretcat/vrp)
[](https://codescene.io/projects/46594)
[](https://deps.rs/crate/vrp-cli/1.24.0)
[](https://zenodo.org/badge/latestdoi/238436117)
# Description
This project provides a way to solve multiple variations of **Vehicle Routing Problem** known as rich VRP. It provides
custom hyper- and meta-heuristic implementations, shortly described [here](https://reinterpretcat.github.io/vrp/internals/index.html).
If you use the project in academic work, please consider citing:
```
@misc{builuk_rosomaxa_2023,
author = {Ilya Builuk},
title = {{A new solver for rich Vehicle Routing Problem}},
year = 2023,
doi = {10.5281/zenodo.4624037},
publisher = {Zenodo},
url = {https://doi.org/10.5281/zenodo.4624037}
}
```
# Design goal
Although performance is constantly in focus, the main idea behind design is extensibility: the project
aims to support a wide range of VRP variations known as Rich VRP. This is achieved through various extension
points: custom constraints, objective functions, acceptance criteria, etc.
# Getting started
For general installation steps and basic usage options, please check the next sections. More detailed overview of the features
and full description of the usage is presented in [A Vehicle Routing Problem Solver Documentation](https://reinterpretcat.github.io/vrp).
Probably, the easiest way to learn how to use the solver `as is`, would be to play with [interactive tutorial](https://github.com/reinterpretcat/vrp/tree/master/examples/python-interop/tutorial.ipynb),
written as jupyter notebook.
Additionally, you can check `vrp-core/examples` to see how to use the library and extend it within a new functionality.
# Installation
You can install the latest release of the vrp solver using four different ways:
## Install with Python
The functionality of `vrp-cli` is published to [pypi.org](https://pypi.org/project/vrp-cli/), so you can just install it
using pip and use from python:
```shell
pip install vrp-cli
python examples/python-interop/example.py # run test example
```
Alternatively, you can use [maturin](https://github.com/PyO3/maturin) tool to build solver locally. You need to enable
`py_bindings` feature which is not enabled by default.
Additionally, to jupyter notebook mentioned above, you can find extra information in [python example section](https://reinterpretcat.github.io/vrp/examples/interop/python.html)
of the docs. The [full source code](./examples/python-interop/example.py) of python example is available in the repo which
contains useful model wrappers with help of `pydantic` lib (reused by tutorial as well).
## Install from Docker
Another fast way to try vrp solver on your environment is to use `docker` image (not performance optimized):
* **run public image** from `Github Container Registry`:
```bash
docker run -it -v $(pwd):/repo --name vrp-cli --rm ghcr.io/reinterpretcat/vrp/vrp-cli:1.24.0
```
* **build image locally** using `Dockerfile` provided:
```bash
docker build -t vrp_solver .
docker run -it -v $(pwd):/repo --rm vrp_solver
```
Please note that the docker image is built using `musl`, not `glibc` standard library. So there might be some performance
implications.
## Install from Cargo
You can install vrp solver `cli` tool directly with `cargo install`:
cargo install vrp-cli
Ensure that your `$PATH` is properly configured to source the crates binaries, and then run solver using the `vrp-cli` command.
## Install from source
Once pulled the source code, you can build it using `cargo`:
cargo build --release
Built binaries can be found in the `./target/release` directory and can be run using `vrp-cli` executable, e.g.:
./target/release/vrp-cli solve solomon examples/data/scientific/solomon/C101.100.txt --log
Alternatively, you can try to run the following script from the project root (with `pragmatic` format only):
./solve_problem.sh examples/data/pragmatic/objectives/berlin.default.problem.json
It will build the executable and automatically launch the solver with the specified VRP definition. Results are
stored in the folder where a problem definition is located.
Please note, that `master` branch normally contains not yet released changes.
# Usage
## Using from code
If you're using rust, you have multiple options for how the project can be used:
### Use customization capabilities
The `vrp-core` provides API to compose a VRP formulation from various building blocks and even add your own. Start with
basic `vrp-core/examples`, then check the user documentation and code for more details.
### Use built-in formats
You can use `vrp-scientific`, `vrp-pragmatic` crates to solve a VRP problem defined in `pragmatic` or `scientific`
format using default metaheuristic. Or you can use CLI interface for that (see below).
If you're using some other language, e.g. java, kotlin, javascript, python, please check
[interop](https://reinterpretcat.github.io/vrp/examples/interop/index.html) section in documentation examples to see how
to call the library from it (currently, limited to `pragmatic` format).
## Using from command line
`vrp-cli` crate is designed to use on problems defined in scientific or custom json (aka `pragmatic`) format:
vrp-cli solve pragmatic problem_definition.json -m routing_matrix.json --max-time=120
Please refer to [getting started](https://reinterpretcat.github.io/vrp/getting-started/index.html) section in
the documentation for more details.
# Contribution policy
*open source, limited contribution*
The goal is to reduce burnout by limiting the maintenance overhead of reviewing and validating third-party code.
Please submit an [issue](https://github.com/reinterpretcat/vrp/issues/new) or [discussion](https://github.com/reinterpretcat/vrp/discussions/new/choose)
if you have ideas for improvement.
# Status
Permanently experimental. This is my pet project, and I'm not paid for it, so expect a very limited support.
Raw data
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It provides\ncustom hyper- and meta-heuristic implementations, shortly described [here](https://reinterpretcat.github.io/vrp/internals/index.html).\n\nIf you use the project in academic work, please consider citing:\n\n```\n@misc{builuk_rosomaxa_2023,\n author = {Ilya Builuk},\n title = {{A new solver for rich Vehicle Routing Problem}},\n year = 2023,\n doi = {10.5281/zenodo.4624037},\n publisher = {Zenodo},\n url = {https://doi.org/10.5281/zenodo.4624037}\n}\n```\n\n# Design goal\n\nAlthough performance is constantly in focus, the main idea behind design is extensibility: the project\naims to support a wide range of VRP variations known as Rich VRP. This is achieved through various extension\npoints: custom constraints, objective functions, acceptance criteria, etc.\n\n\n# Getting started\n\nFor general installation steps and basic usage options, please check the next sections. More detailed overview of the features\nand full description of the usage is presented in [A Vehicle Routing Problem Solver Documentation](https://reinterpretcat.github.io/vrp).\n\nProbably, the easiest way to learn how to use the solver `as is`, would be to play with [interactive tutorial](https://github.com/reinterpretcat/vrp/tree/master/examples/python-interop/tutorial.ipynb),\nwritten as jupyter notebook.\n\nAdditionally, you can check `vrp-core/examples` to see how to use the library and extend it within a new functionality.\n\n# Installation\n\nYou can install the latest release of the vrp solver using four different ways:\n\n## Install with Python\n\nThe functionality of `vrp-cli` is published to [pypi.org](https://pypi.org/project/vrp-cli/), so you can just install it\nusing pip and use from python:\n\n```shell\npip install vrp-cli\npython examples/python-interop/example.py # run test example\n```\n\nAlternatively, you can use [maturin](https://github.com/PyO3/maturin) tool to build solver locally. 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The [full source code](./examples/python-interop/example.py) of python example is available in the repo which\ncontains useful model wrappers with help of `pydantic` lib (reused by tutorial as well).\n\n\n## Install from Docker\n\nAnother fast way to try vrp solver on your environment is to use `docker` image (not performance optimized):\n\n* **run public image** from `Github Container Registry`:\n\n```bash\n docker run -it -v $(pwd):/repo --name vrp-cli --rm ghcr.io/reinterpretcat/vrp/vrp-cli:1.24.0\n```\n\n* **build image locally** using `Dockerfile` provided:\n\n```bash\ndocker build -t vrp_solver .\ndocker run -it -v $(pwd):/repo --rm vrp_solver\n```\n\nPlease note that the docker image is built using `musl`, not `glibc` standard library. 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