November 12, 2020
Ørsted, the global leader in offshore wind power production and ranked the most sustainable company in the world, has chosen Flowty’s Network Optimization Solver ...
Get running fast with intuitive modelling of constrained network problems.
Use callbacks to customize the algorithm for your business domain.
Enjoy specialized algorithms to leverage network structures fully.
Solve network optimization problems like
for graphs with paths subject to resource constraints .
Install it
pip install flowty
and solve a time-constrained fixed-charge multi-commodity flow problem
from flowty import Model, xsum
# ... data setup
m = Model()
# create graphs per commodity
g = [
m.addGraph(obj=obj, edges=edges, source=o[k], sink=d[k], L=D[k], U=D[k], type="C")
for k in range(K)
]
# add time constraints to graphs
for k in range(K):
m.addResourceDisposable(graph=g[k], consumptionType="E", weight=time, lb=0, ub=T[k])
# demand constraints
for k in range(K):
m.addConstr(xsum(x for x in g[k].vars if x.source == g[k].source) == D[k])
# design variables
y = [
m.addVar(lb=0, ub=1, obj=f[e], type="B", name=f"y_{e}")
for e, edge in enumerate(edges)
]
# capacity constraints
for e, edge in enumerate(edges):
m.addConstr(
xsum(x for k in range(K) for x in g[k].vars if x.edge == edge) <= u[e] * y[e]
)
status = m.optimize()
Flowty was founded by Simon Spoorendonk, PhD, with the aim to bring easy-to-use network optimization with excellent performance to the masses.
Professor Stefan Røpke, DTU Management, advices Flowty on scientific matters.