Solving the model

Prerequisites

Get the solution

The resulting output

For better understanding we visualized the output below. The optimal solution opens warehouses in Paderborn and New York.
New York is serving São Paulo and San Francisco, while Paderborn is serving Moscow.

Optimizing the Model...
Optimize a model with 9 rows, 15 columns and 30 nonzeros
Coefficient statistics:
  Matrix range    [1e+00, 2e+02]
  Objective range [2e+03, 2e+06]
  Bounds range    [1e+00, 1e+02]
  RHS range       [1e+02, 1e+02]
Found heuristic solution: objective 6.95e+06
Presolve removed 3 rows and 3 columns
Presolve time: 0.00s
Presolved: 6 rows, 12 columns, 21 nonzeros
Variable types: 9 continuous, 3 integer (3 binary)

Root relaxation: objective 4.110000e+06, 5 iterations, 0.02 seconds

    Nodes    |    Current Node    |     Objective Bounds      |     Work
 Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time

*    0     0               0    4110000.0000 4110000.00  0.00%     -    0s

Explored 0 nodes (5 simplex iterations) in 0.18 seconds
Thread count was 4 (of 4 available processors)

Optimal solution found (tolerance 0.00e+00)
Best objective 4.110000000000e+06, best bound 4.110000000000e+06, gap 0.0%
[sum of all cost, 4110000]
Supply flowing from Paderborn to São Paulo_B: 0
Supply flowing from New York to São Paulo_C: 100
Supply flowing from Beijing to São Paulo_D: 0
Supply flowing from Paderborn to San Francisco_E: 0
Supply flowing from New York to San Francisco_F: 100
Supply flowing from Beijing to San Francisco_G: 0
Supply flowing from Paderborn to Moscow_H: 100
Supply flowing from New York to Moscow_I: 0
Supply flowing from Beijing to Moscow_J: 0
Paderborn status:_K                 : 1
New York status:_L                  : 1
Beijing status:_M                   : 0
São Paulo status:_N                 : 0
San Francisco status:_O             : 0
Moscow status:_P                    : 0

Next Step

  • Here you can find even more Models