netflow.py example
Our example solves a multi-commodity flow model on a small network. In the example, two commodities (Pencils and Pens) are produced in two cities (Detroit and Denver), and must be shipped to warehouses in three cities (Boston, New York, and Seattle) to satisfy given demand. Each arc in the transportation network has a per-unit cost associated with it, as well as a maximum total shipping capacity.
This is the complete source code for our example (also available in
#！/usr/bin/env python3.7 # 2022年版权,GurobiOptimization, LLC # Solve a multi-commodity flow problem. Two products ('Pencils' and 'Pens') # are produced in 2 cities ('Detroit' and 'Denver') and must be sent to # warehouses in 3 cities ('Boston', 'New York', and 'Seattle') to # satisfy demand ('inflow[h,i]'). # # Flows on the transportation network must respect arc capacity constraints # ('capacity[i,j]'). The objective is to minimize the sum of the arc # transportation costs ('cost[i,j]'). import gurobipy as gp from gurobipy import GRB # Base data commodities = ['Pencils', 'Pens'] nodes = ['Detroit', 'Denver', 'Boston', 'New York', 'Seattle'] arcs, capacity = gp.multidict({ ('Detroit', 'Boston'): 100, ('Detroit', 'New York'): 80, ('Detroit', 'Seattle'): 120, ('Denver', 'Boston'): 120, ('Denver', 'New York'): 120, ('Denver', 'Seattle'): 120}) # Cost for triplets commodity-source-destination cost = { ('Pencils', 'Detroit', 'Boston'): 10, ('Pencils', 'Detroit', 'New York'): 20, ('Pencils', 'Detroit', 'Seattle'): 60, ('Pencils', 'Denver', 'Boston'): 40, ('Pencils', 'Denver', 'New York'): 40, ('Pencils', 'Denver', 'Seattle'): 30, ('Pens', 'Detroit', 'Boston'): 20, ('Pens', 'Detroit', 'New York'): 20, ('Pens', 'Detroit', 'Seattle'): 80, ('Pens', 'Denver', 'Boston'): 60, ('Pens', 'Denver', 'New York'): 70, ('Pens', 'Denver', 'Seattle'): 30} # Demand for pairs of commodity-city inflow = { ('Pencils', 'Detroit'): 50, ('Pencils', 'Denver'): 60, ('Pencils', 'Boston'): -50, ('Pencils', 'New York'): -50, ('Pencils', 'Seattle'): -10, ('Pens', 'Detroit'): 60, ('Pens', 'Denver'): 40, ('Pens', 'Boston'): -40, ('Pens', 'New York'): -30, ('Pens', 'Seattle'): -30} # Create optimization model m = gp.Model('netflow') # Create variables flow = m.addVars(commodities, arcs, obj=cost, name="flow") # Arc-capacity constraints m.addConstrs( (flow.sum('*', i, j) <= capacity[i, j] for i, j in arcs), "cap") # Equivalent version using Python looping # for i, j in arcs: # m.addConstr(sum(flow[h, i, j] for h in commodities) <= capacity[i, j], # "cap[%s, %s]" % (i, j)) # Flow-conservation constraints m.addConstrs( (flow.sum(h, '*', j) + inflow[h, j] == flow.sum(h, j, '*') for h in commodities for j in nodes), "node") # Alternate version: # m.addConstrs( # (gp.quicksum(flow[h, i, j] for i, j in arcs.select('*', j)) + inflow[h, j] == # gp.quicksum(flow[h, j, k] for j, k in arcs.select(j, '*')) # for h in commodities for j in nodes), "node") # Compute optimal solution m.optimize() # Print solution if m.Status == GRB.OPTIMAL: solution = m.getAttr('X', flow) for h in commodities: print('\nOptimal flows for %s:' % h) for i, j in arcs: if solution[h, i, j] > 0: print('%s -> %s: %g' % (i, j, solution[h, i, j]))
