zahlio / vrp

A Vehicle Routing Problem solver

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.

Status

Experimental.