Results obtained using Gurobi for solving the Lot Sizing Problem, using the models described in Mathematical Optimization: Solving Problems using Python and Gurobi. Benchmark instances were generated with Trigeiro's method. CPU time limited to 3600 seconds. (Click on values for selecting data to display.)
Performance data | Factor: low | Factor: med | Factor: high |
CPU time required | [select] | [select] | [select] |
Number of solution failures | [select] | [select] | [select] |
Solutions | [select] | [select] | [select] |
Label | Description |
std | standard model |
cut | standard model with cutting planes (single item lot sizing cuts; callback on MIPSOL and MIPNODE) |
fl | facility location formulation |
Instance | Size | Periods | Products | std | cut | fl |
lsp_15_6_low_0 | 90 | 15 | 6 | 0.01 | 0.03 | 0.01 |
lsp_15_6_low_1 | 90 | 15 | 6 | 0.02 | 0.15 | 0.03 |
lsp_15_6_low_2 | 90 | 15 | 6 | 0.02 | 0.24 | 0.02 |
lsp_15_6_low_3 | 90 | 15 | 6 | 0.02 | 0.10 | 0.02 |
lsp_15_6_low_4 | 90 | 15 | 6 | 0.06 | 1.16 | 0.07 |
lsp_15_6_low_5 | 90 | 15 | 6 | 0.02 | 0.44 | 0.02 |
lsp_15_6_low_6 | 90 | 15 | 6 | 0.03 | 0.26 | 0.03 |
lsp_15_6_low_7 | 90 | 15 | 6 | 0.03 | 0.19 | 0.03 |
lsp_15_6_low_8 | 90 | 15 | 6 | 0.03 | 0.35 | 0.05 |
lsp_15_6_low_9 | 90 | 15 | 6 | 0.01 | 0.10 | 0.01 |
lsp_15_12_low_0 | 180 | 15 | 12 | 0.05 | 0.43 | 0.07 |
lsp_15_12_low_1 | 180 | 15 | 12 | 0.04 | 0.37 | 0.04 |
lsp_15_12_low_2 | 180 | 15 | 12 | 0.08 | 0.49 | 0.07 |
lsp_15_12_low_3 | 180 | 15 | 12 | 0.05 | 0.56 | 0.10 |
lsp_15_12_low_4 | 180 | 15 | 12 | 0.06 | 0.78 | 0.10 |
lsp_15_12_low_5 | 180 | 15 | 12 | 0.05 | 0.51 | 0.07 |
lsp_15_12_low_6 | 180 | 15 | 12 | 0.06 | 0.52 | 0.04 |
lsp_15_12_low_7 | 180 | 15 | 12 | 0.10 | 1.75 | 0.12 |
lsp_15_12_low_8 | 180 | 15 | 12 | 0.05 | 0.16 | 0.09 |
lsp_15_12_low_9 | 180 | 15 | 12 | 0.04 | 0.37 | 0.04 |
lsp_15_24_low_0 | 360 | 15 | 24 | 0.14 | 2.37 | 0.14 |
lsp_15_24_low_1 | 360 | 15 | 24 | 0.07 | 0.19 | 0.04 |
lsp_15_24_low_2 | 360 | 15 | 24 | 0.09 | 0.64 | 0.15 |
lsp_15_24_low_3 | 360 | 15 | 24 | 0.06 | 0.20 | 0.04 |
lsp_15_24_low_4 | 360 | 15 | 24 | 0.13 | 0.45 | 0.05 |
lsp_15_24_low_5 | 360 | 15 | 24 | 0.13 | 0.91 | 0.12 |
lsp_15_24_low_6 | 360 | 15 | 24 | 0.10 | 0.82 | 0.06 |
lsp_15_24_low_7 | 360 | 15 | 24 | 0.08 | 0.25 | 0.03 |
lsp_15_24_low_8 | 360 | 15 | 24 | 0.09 | 0.55 | 0.09 |
lsp_15_24_low_9 | 360 | 15 | 24 | 0.09 | 0.26 | 0.05 |
lsp_30_6_low_0 | 180 | 30 | 6 | 0.05 | 0.32 | 0.09 |
lsp_30_6_low_1 | 180 | 30 | 6 | 0.09 | 2.31 | 0.14 |
lsp_30_6_low_2 | 180 | 30 | 6 | 0.09 | 1.68 | 0.11 |
lsp_30_6_low_3 | 180 | 30 | 6 | 0.12 | 5.03 | 0.17 |
lsp_30_6_low_4 | 180 | 30 | 6 | 0.37 | 12.26 | 0.31 |
lsp_30_6_low_5 | 180 | 30 | 6 | 0.19 | 5.96 | 0.19 |
lsp_30_6_low_6 | 180 | 30 | 6 | 0.07 | 0.68 | 0.18 |
lsp_30_6_low_7 | 180 | 30 | 6 | 0.07 | 1.52 | 0.20 |
lsp_30_6_low_8 | 180 | 30 | 6 | 0.23 | 13.59 | 0.31 |
lsp_30_6_low_9 | 180 | 30 | 6 | 0.12 | 2.91 | 0.31 |
lsp_30_12_low_0 | 360 | 30 | 12 | 0.10 | 1.45 | 0.20 |
lsp_30_12_low_1 | 360 | 30 | 12 | 0.22 | 4.22 | 0.24 |
lsp_30_12_low_2 | 360 | 30 | 12 | 0.09 | 0.34 | 0.09 |
lsp_30_12_low_3 | 360 | 30 | 12 | 0.23 | 9.98 | 0.40 |
lsp_30_12_low_4 | 360 | 30 | 12 | 0.19 | 5.59 | 0.33 |
lsp_30_12_low_5 | 360 | 30 | 12 | 0.10 | 1.52 | 0.21 |
lsp_30_12_low_6 | 360 | 30 | 12 | 0.14 | 2.14 | 0.26 |
lsp_30_12_low_7 | 360 | 30 | 12 | 0.27 | 6.62 | 0.32 |
lsp_30_12_low_8 | 360 | 30 | 12 | 0.11 | 0.92 | 0.27 |
lsp_30_12_low_9 | 360 | 30 | 12 | 0.11 | 2.22 | 0.26 |
lsp_30_24_low_0 | 720 | 30 | 24 | 0.31 | 3.43 | 0.50 |
lsp_30_24_low_1 | 720 | 30 | 24 | 0.19 | 0.85 | 0.13 |
lsp_30_24_low_2 | 720 | 30 | 24 | 0.17 | 0.59 | 0.13 |
lsp_30_24_low_3 | 720 | 30 | 24 | 0.22 | 1.08 | 0.18 |
lsp_30_24_low_4 | 720 | 30 | 24 | 0.21 | 0.83 | 0.16 |
lsp_30_24_low_5 | 720 | 30 | 24 | 0.30 | 4.14 | 0.17 |
lsp_30_24_low_6 | 720 | 30 | 24 | 0.29 | 6.32 | 0.41 |
lsp_30_24_low_7 | 720 | 30 | 24 | 0.16 | 0.67 | 0.12 |
lsp_30_24_low_8 | 720 | 30 | 24 | 0.28 | 1.72 | 0.20 |
lsp_30_24_low_9 | 720 | 30 | 24 | 0.35 | 4.62 | 0.45 |