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Results
Once we've added the model constraints, we call optimize and
then output the optimal solution:
if m.status == GRB.status.OPTIMAL:
for h in commodities:
print '\nOptimal flows for', h, ':'
for i,j in arcs:
if flow[h,i,j].x > 0:
print i, '->', j, ':', flow[h,i,j].x
If you run the example (gurobi.bat netflow.py on Windows,
or gurobi.sh netflow.py on Linux and Mac), you should
see the following output:
Optimize a model with 16 rows, 12 columns and 36 nonzeros
Presolve removed 16 rows and 12 columns
Presolve time: 0.00s
Presolve: All rows and columns removed
Iteration Objective Primal Inf. Dual Inf. Time
0 5.5000000e+03 0.000000e+00 0.000000e+00 0s
Solved in 0 iterations and 0.00 seconds
Optimal objective 5.500000000e+03
Optimal flows for Pencils :
Detroit -> Boston : 50.0
Denver -> New York : 50.0
Denver -> Seattle : 10.0
Optimal flows for Pens :
Detroit -> Boston : 30.0
Detroit -> New York : 30.0
Denver -> Boston : 10.0
Denver -> Seattle : 30.0
Next: Building and running the Up: Python Dictionary Example Previous: Flow conservation constraints
