Using VRPy¶

In order to use the VRPy package, first, one has to create a directed graph which represents the underlying network.

To do so, we make use of the well-known NetworkX package, with the following input requirements:

  • Input graphs must be of type networkx.DiGraph;

  • Input graphs must have a single Source and Sink nodes with no incoming or outgoing edges respectively;

  • There must be at least one path from Source to Sink;

  • Edges in the input graph must have a cost attribute (of type float).

For example the following simple network fulfills the requirements listed above:

>>> from networkx import DiGraph
>>> G = DiGraph()
>>> G.add_edge("Source", 1, cost=1)
>>> G.add_edge("Source", 2, cost=2)
>>> G.add_edge(1, "Sink", cost=0)
>>> G.add_edge(2, "Sink", cost=2)
>>> G.add_edge(1, 2, cost=1)
>>> G.add_edge(2, 1, cost=1)

The customer demands are set as demand attributes (of type float) on each node:

>>> G.nodes[1]["demand"] = 5
>>> G.nodes[2]["demand"] = 4

To solve your routing problem, create a VehicleRoutingProblem instance, specify the problem constraints (e.g., the load_capacity of each truck), and call solve.

>>> from vrpy import VehicleRoutingProblem
>>> prob = VehicleRoutingProblem(G, load_capacity=10)
>>> prob.solve()

Once the problem is solved, we can query useful attributes as:

>>> prob.best_value
>>> prob.best_routes
{1: ["Source", 2, 1, "Sink"]}
>>> prob.best_routes_load
{1: 9}

prob.best_value is the overall cost of the solution, prob.best_routes is a dict object where keys represent the route ID, while the values are the corresponding path from Source to Sink. And prob.best_routes is a dict object where the same keys point to the accumulated load on the vehicle.

Different options and constraints are detailed in the Vehicle Routing Problems section, and other attributes can be queried depending on the nature of the VRP (see section API).