Python Vehicle Routing Open-source Optimization Machine
Good solution, fast… in Python.
Pyvroom is an Python wrapper to the excellent VROOM optimization engine for solving vehicle routing problems.
The library aims to solve several well-known types of vehicle routing problems, including:
Travelling salesman.
Capacitated vehicle routing.
Routing with time windows.
Multi-depot heterogeneous vehicle.
Pickup-and-delivery.
VROOM can also solve any mix of the above problem types.
Basic usage
>>> import vroom
>>> problem_instance = vroom.Input()
>>> problem_instance.set_durations_matrix(
... profile="car",
... matrix_input=[[0, 2104, 197, 1299],
... [2103, 0, 2255, 3152],
... [197, 2256, 0, 1102],
... [1299, 3153, 1102, 0]],
... )
>>> problem_instance.add_vehicle([vroom.Vehicle(47, start=0, end=0),
... vroom.Vehicle(48, start=2, end=2)])
>>> problem_instance.add_job([vroom.Job(1414, location=0),
... vroom.Job(1515, location=1),
... vroom.Job(1616, location=2),
... vroom.Job(1717, location=3)])
>>> solution = problem_instance.solve(exploration_level=5, nb_threads=4)
>>> solution.summary.cost
6411
>>> solution.routes.columns
Index(['vehicle_id', 'type', 'arrival', 'duration', 'setup', 'service',
'waiting_time', 'location_index', 'id', 'description'],
dtype='object')
>>> groups = solution.routes[solution.routes.type == "job"].groupby("vehicle_id")["id"].apply(lambda x: set(x.dropna().astype(int)))
>>> groups[47] == {1414, 1515} and groups[48] == {1616, 1717}
TrueUsage with a routing engine
>>> import vroom
>>> problem_instance = vroom.Input(
... servers={"auto": "valhalla1.openstreetmap.de:443"},
... router=vroom._vroom.ROUTER.VALHALLA
... )
>>> problem_instance.add_vehicle(vroom.Vehicle(1, start=(2.44, 48.81), profile="auto"))
>>> problem_instance.add_job([
... vroom.Job(1, location=(2.44, 48.81)),
... vroom.Job(2, location=(2.46, 48.7)),
... vroom.Job(3, location=(2.42, 48.6)),
... ])
>>> sol = problem_instance.solve(exploration_level=5, nb_threads=4)
>>> 3800 < sol.summary.duration < 4200
TrueInstallation
Pyvroom currently makes binaries for on macOS and Linux. There is also a Windows build that can be used, but it is somewhat experimental.
Installation of the pre-compiled releases should be as simple as:
pip install pyvroomThe current minimal requirements are as follows:
Python at least version 3.10.
Intel MacOS (or Rosetta2) at least version 15.0.
Apple Silicon MacOS at least version 15.0.
Windows on AMD64.
Linux on x86_64 and Aarch64 given glibc at least version 2.34.
Outside this it might be possible to build your own binaries.
Building from source
Building the source distributions requires:
Download the Pyvroom repository on you local machine:
git clone --recurse-submodules https://github.com/VROOM-Project/pyvroomInstall the Python dependencies:
pip install -r pyvroom/build-requirements.txtInstall asio headers, and openssl and crypto libraries and headers. For mac, this would be:
brew install openssl@1.1 brew install asio
For RHEL:
yum module enable mariadb-devel:10.3 yum install -y openssl-devel asio
For Musllinux:
apk add asio-dev apk add openssl-dev
The installation can then be done with:
pip install pyvroom/
Alternatively it is also possible to install the package from source using Conan. This is also likely the only option if installing on Windows.
To install using Conan, do the following:
cd pyvroom/
conan install --build=openssl --install-folder conan_build .Documentation
The code is currently only documented with Pydoc. This means that the best way to learn Pyvroom for now is to either look at the source code or use dir() and help() to navigate the interface.
It is also useful to take a look at the VROOM API documentation. The interface there is mostly the same.
1 maintainer
Metadata
1 maintainer
Release files for pyvroom 1.15.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pyvroom-1.15.2.tar.gz | 2.5 MB | Details |
Built distributions (wheels)
Total release size: 99.2 MB
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
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Release files / pyvroom-1.15.2-cp310-cp310-macosx_15_0_x86_64.whl
| Download URL | pyvroom-1.15.2-cp310-cp310-macosx_15_0_x86_64.whl |
|---|---|
| Size | 4.3 MB |
| Tags | CPython 3.10 macOS 15.0+ x86-64 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Release files / pyvroom-1.15.2-cp310-cp310-macosx_15_0_arm64.whl
| Download URL | pyvroom-1.15.2-cp310-cp310-macosx_15_0_arm64.whl |
|---|---|
| Size | 3.9 MB |
| Tags | CPython 3.10 macOS 15.0+ ARM64 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
