VRPSolverEasy 0.1.4

'VRPSolverEasy is a simplified modeler solving routing problems by using a Branch-Cut-and-Price approach on a solver like CLP or CPLEX'

VRPSolverEasy is a Python package which provides a simple interface for VRPSolver, 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:

• capacitated vehicles,

• customer time windows,

• heterogeneous fleet,

• multiple depots,

• open routes,

• optional customers with penalties,

• parallel links to model transition time/cost trade-off,

• incompatibilities between vehicles and customers,

• customers with alternative locations and/or time windows.

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.

License

The VRPSolverEasy package itself is open-source and free to use. It includes compiled libraries of BaPCod, 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. 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.

Accompanying paper

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, 2023.

Please cite it if you use VRPSolverEasy in your research.

Installation

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 pip

There 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:

• Download the package and extract it into a local directory

• 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.

Copy binaries

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.

Example

A simple example that shows how to use the VRPSolverEasy package:

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)

# Data
cost_per_distance = 10
begin_time = 0
end_time = 5000
nb_point = 7

# Map with names and coordinates
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 of points
demands = [0, 500, 300, 600, 658, 741, 436]

# Initialisation
model = vrpse.Model()

# Add vehicle type
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)

# Add depot
model.add_depot(id=0, name="D1", tw_begin=0, tw_end=5000)

coordinates_keys = list(coordinates.keys())
# Add customers
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)

# Add links
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)

# solve model
model.solve()
model.export()

if model.solution.is_defined():
    print(model.solution)

Documentation

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.