Description
This project provides the way to solve multiple variations of Vehicle Routing Problem known as rich VRP. It provides custom hyper- and meta-heuristic implementations, shortly described here.
If you use the project in academic work, please consider citing:
@misc{builuk_rosomaxa_2022,
author = {Ilya Builuk},
title = {{A new solver for rich Vehicle Routing Problem}},
year = 2022,
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 next sections. More detailed overview of features and full description of the usage is presented in A Vehicle Routing Problem Solver Documentation.
Installation
You can install vrp solver using four different ways:
Install with Python
The functionality of vrp-cli
is published to pypi.org, so you can just install it
using pip and use from python:
pip install vrp-cli
python examples/python-interop/example.py # run test example
Alternatively, you can use maturin tool to build solver locally. You need to enable
py_bindings
feature which is not enabled by default.
You can find extra information in python example section
of the docs. The full source code of python example is available in the repo which
contains useful model wrappers with help of pydantic
lib.
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
:
docker run -it -v $(pwd):/repo --name vrp-cli --rm ghcr.io/reinterpretcat/vrp/vrp-cli:1.21.1
- build image locally using
Dockerfile
provided:
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.
Alternatively, you can try to run the following script from the project root:
./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.
Usage
You can use vrp solver either from command line or from code:
Use 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 section in the documentation for more details.
Use from code
If you're using rust, then you can simply use vrp-scientific
, vrp-pragmatic
crates to solve VRP problem
defined in pragmatic
or scientific
format using default metaheuristic. For more complex scenarios, please refer to
vrp-core
documentation.
If you're using some other language, e.g. java, kotlin, javascript, python, please check interop section in documentation examples to see how to call the library from it.
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 or discussion if you have a MR proposal.
Status
Experimental.