# Quick Start¶

Get started and solve a problem with the Flowty's network optimization solver.

## Install¶

Install the python package

pip install flowty

conda install -c flowty flowty


If you run on Linux make sure that your dependencies are installed.

For visualization install

pip install networkx matplotlib

conda install networkx matplotlib


## Model¶

Let's solve the vehicle routing problem with time windows.

The objective is to minimize the total cost of routing vehicles from a central depot to a set of customers. Each customer must be visited exactly once within a specified time window to deliver their required demand, each customer has a service time it takes to unload the vehicle, and each vehicle has a maximum capacity of goods to deliver. If a vehicle arrives early it is allowed to wait for the customer's time window to start.

The network model can be formulated as

\begin{aligned} \min{ } & \sum_{(i,j) \in E} c_{ij} x_{ij} \\ \text{s.t. } & \sum_{(i,j) \in \delta^+(i)} x_{ij} = 1 & & i \in C \\ & x_{ij} = \sum_{p \in P} \Big ( \sum_{ s \in p: s = (i,j)} \mathbf{1} \Big ) \lambda_p & & (i,j) \in E \\ & 1 \leq \sum_{p \in P} \lambda_p \leq |C| \\ & \lambda_p \in \mathbb{B} && p \in P \\ & x_{i,j} \in \mathbb{B} && (i,j) \in E \end{aligned}

where $C$ is the set of customers, the depot is split in two such that the vertices are $V = \{0,|C|+1\} \cup C$, and $p \in P$ are paths in a graph $G(V,E)$ subject to time and demand constraints modeled as disposable resources. Demand with vertex consumption $d_i$ and bounds $[0,Q]$ for all $i \in V$ and time with edge consumption $t_{ij}$ for all $(i,j) \in E$ and vertex bounds $[a_i,b_i]$ for all $i \in V$.

Since the $\lambda$'s are induced from the graph relation we need only to consider the set partitioning constraints in the model

\begin{aligned} \min{ } & \sum_{(i,j) \in E} c_{ij} x_{ij} \\ \text{s.t. } & \sum_{(i,j) \in \delta^+(i)} x_{ij} = 1 & & i \in C \\ & x_{i,j} \in \mathbb{B} && (i,j) \in E \end{aligned}

with the graph and resource constraints described above.

## Code¶

Import libraries and data, and initialize the model

# Vehicle Routing Problem with Time Windows

from flowty import Model, xsum
from flowty.datasets import vrp_rep

bunch = vrp_rep.fetch_vrp_rep("solomon-1987-r1", instance="R102_025")
name, n, es, c, d, Q, t, a, b, x, y = bunch["instance"]

m = Model()


Add the graph and resource constraints.

# one graph, it is identical for all vehicles
g = m.addGraph(obj=c, edges=es, source=0, sink=n - 1, L=1, U=n - 2, type="B")

# adds resources variables to the graph.
# demand and capacity
graph=g, consumptionType="V", weight=d, boundsType="V", lb=0, ub=Q, name="d"
)

# travel time and customer tine windows
graph=g, consumptionType="E", weight=t, boundsType="V", lb=a, ub=b, name="t"
)


# set partition constriants
for i in range(n)[1:-1]:
m += xsum(x * 1 for x in g.vars if i == x.source) == 1


Add packing sets to help the algorithm. Packing sets are induced by the user and are helpful for the algorithm for path generation and branching decision.

# packing set
for i in range(n)[1:-1]:
m.addPackingSet([x for x in g.vars if i == x.source])


Optimize and get back the result.

status = m.optimize()
print(f"ObjectiveValue {m.objectiveValue}")

# get the variable values
for var in m.vars:
if var.x > 0:
print(f"{var.name} = {var.x}")


## Visualization¶

Visualize the solution

import math
import networkx
import matplotlib
import matplotlib.pyplot as plt

edges = [x.edge for x in g.vars if not math.isclose(x.x, 0, abs_tol=0.001)]
gx = networkx.DiGraph()