.. image:: https://github.com/inria-UFF/VRPSolverEasy/actions/workflows/python-package.yml/badge.svg :target: https://github.com/inria-UFF/VRPSolverEasy/actions/workflows/python-package.yml
VRPSolverEasy is a Python package which provides a simple interface
for VRPSolver <https://vrpsolver.math.u-bordeaux.fr/>__, which is a
state-of-the-art Branch-Cut-and-Price exact solver for vehicle routing
problems (VRPs). The simplified interface is accessible for users
without operations research background, i.e., you do not need to know
how to model your problem as an Integer Programming problem. As a price
to pay for the simplicity, this interface is restricted to some standard
VRP variants, which involve the following features and their
combinations:
To our knowledge, VRPSolver is the most efficient exact solver available for VRPs. Its particularity is to focus on finding and improving a lower bound on the optimal solution value of your instance. It is less efficient in finding feasible solutions but still can be better than available heuristic solvers for non-classic VRP variants. One can expect to find provably optimal solutions for most instances with up to 100 customers. A significant number of instances in the range of 101-200 customers may be solved too. A few even larger instances may be solved, but usually, this requires very long runs. The performance of VRPSolver improves when very good initial upper bounds, obtained by an external heuristic solver, are provided.
VRPSolver is based on a research proof-of-concept code prone to issues. Use it only for research, teaching, testing, and R&D purposes at your own risk. It is not suited for use in production. Please use Issues section in this repository to report bugs and issues, and to give suggestions.
The VRPSolverEasy package itself is open-source and free to use. It
includes compiled libraries of
BaPCod <https://bapcod.math.u-bordeaux.fr/>__, its VRPSolver
extension, and COIN-OR CLP solver. These libraries are also free to use.
For better performance, it is possible to use VRPSolverEasy together
with CPLEX MIP solver. This combination called academic version
requires an access to the source code of BaPCod available with an
academic-use-only license <https://bapcod.math.u-bordeaux.fr/#licence>__. The academic
version of VRPSolverEasy additionally includes a MIP-based (slow)
heuristic which is useful for finding feasible solutions in the absence
of an external heuristic solver.
The paper presents the motivation to create VRPSolverEasy, the interface of the package, the solution approach (optional to read), the computational results for the three classic VRP variants (CVRP, VRPTW, HFVRP), and possible future extensions of the model. For the moment, the paper is available as a preprint :
\N. Errami, E. Queiroga, R. Sadykov, E. Uchoa. "VRPSolverEasy: a Python
library for the exact solution of a rich vehicle routing problem",
`Technical report HAL-04057985 <https://hal.inria.fr/hal-04057985/document>`__, 2023.Please cite it if you use VRPSolverEasy in your research.
.. image:: https://upload.wikimedia.org/wikipedia/commons/c/c3/Python-logo-notext.svg
VRPSolverEasy requires a version of python >= 3.6
.. warning:: Before starting the installation, we invite you to update your version of pip by running this command: ::
python -m pip install --upgrade pipThere is two different way to install VRPSolverEasy :
The first way is to install it with pip::
python -m pip install VRPSolverEasy
The second way is to follow these steps:
Move to this local directory and enter : ::
python pip install .
Installation instructions for Mac computers with Apple ARM processors, as well as for the academic version, are given in the documentation.
Once the package is installed you will need to request the Bapcod distribution here: https://bapcod.math.u-bordeaux.fr/
Once you have downloaded the distribution. You just have to go to the VRPSolverEasy folder and copy the system folder corresponding to your computer and copy it into the lib folder of the VRPSolverEasy python package.
For example if your computer is a Mac you will copy and replace the Darwin folder, you will then have VRPSolverEasy/lib/Darwin.
A simple example that shows how to use the VRPSolverEasy package:
.. code:: python
import VRPSolverEasy as vrpse import math
def compute_euclidean_distance(x_i, x_j, y_i, y_j): """compute the euclidean distance between 2 points from graph""" return round(math.sqrt((x_i - x_j)2 + (y_i - y_j)2), 3)
cost_per_distance = 10 begin_time = 0 end_time = 5000 nb_point = 7
coordinates = {"Wisconsin, USA": (44.50, -89.50), # depot "West Virginia, USA": (39.000000, -80.500000), "Vermont, USA": (44.000000, -72.699997), "Texas, the USA": (31.000000, -100.000000), "South Dakota, the US": (44.500000, -100.000000), "Rhode Island, the US": (41.742325, -71.742332), "Oregon, the US": (44.000000, -120.500000) }
demands = [0, 500, 300, 600, 658, 741, 436]
model = vrpse.Model()
model.add_vehicle_type( id=1, start_point_id=0, end_point_id=0, name="VEH1", capacity=1100, max_number=6, var_cost_dist=cost_per_distance, tw_end=5000)
model.add_depot(id=0, name="D1", tw_begin=0, tw_end=5000)
coordinates_keys = list(coordinates.keys())
for i in range(1, nb_point): model.add_customer( id=i, name=coordinates_keys[i], demand=demands[i], tw_begin=begin_time, tw_end=end_time)
coordinates_values = list(coordinates.values()) for i in range(0, 7): for j in range(i + 1, 7): dist = compute_euclidean_distance(coordinates_values[i][0], coordinates_values[j][0], coordinates_values[i][1], coordinates_values[j][1]) model.add_link( start_point_id=i, end_point_id=j, distance=dist, time=dist)
model.solve() model.export()
if model.solution.is_defined(): print(model.solution)
Documentation, explanation of demos (CVRP, VRPTW, HFVRP, and MDVRP), and the solver API are accessible here: https://vrpsolvereasy.readthedocs.io/en/latest/.
You can also build the documentation locally by following this instructions from the source folder : ::
cd docs python -m pip install -r requirements.txt cd .. make html
The HTML pages will be in the folder build\html.