Lot sizing: results for Trigeiro's random instances


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 dataFactor: lowFactor: medFactor: high
CPU time required [select] [select] [select]
Number of solution failures [select] [select] [select]
Solutions [select] [select] [select]

CPU used

Constraining factor: med (moderately constrained instances)

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.)
CPU used
LabelDescription
std standard model
cut standard model with cutting planes (single item lot sizing cuts; callback on MIPSOL and MIPNODE)
fl facility location formulation

chart
CPU used as a function of instance size, factor=1.0 (med)
InstanceSizePeriodsProductsstdcutfl
lsp_15_6_med_0 90 15 6 0.00 0.00 0.01
lsp_15_6_med_1 90 15 6 0.43 6.67 0.20
lsp_15_6_med_2 90 15 6 0.40 4.41 0.14
lsp_15_6_med_3 90 15 6 0.01 0.01 0.01
lsp_15_6_med_4 90 15 6 0.53 11.23 0.23
lsp_15_6_med_5 90 15 6 0.48 10.42 0.37
lsp_15_6_med_6 90 15 6 0.21 4.70 0.16
lsp_15_6_med_7 90 15 6 0.12 1.41 0.09
lsp_15_6_med_8 90 15 6 0.39 8.60 0.21
lsp_15_6_med_9 90 15 6 0.29 5.39 0.16
lsp_15_12_med_0 180 15 12 0.14 1.43 0.17
lsp_15_12_med_1 180 15 12 0.74 9.57 0.27
lsp_15_12_med_2 180 15 12 0.69 10.69 0.37
lsp_15_12_med_3 180 15 12 0.01 0.01 0.01
lsp_15_12_med_4 180 15 12 1.98 79.69 1.73
lsp_15_12_med_5 180 15 12 0.34 7.60 0.26
lsp_15_12_med_6 180 15 12 0.39 5.68 0.11
lsp_15_12_med_7 180 15 12 0.44 9.93 0.13
lsp_15_12_med_8 180 15 12 0.43 11.36 0.30
lsp_15_12_med_9 180 15 12 0.52 8.31 0.30
lsp_15_24_med_0 360 15 24 3.36 42.42 0.35
lsp_15_24_med_1 360 15 24 0.51 15.55 0.33
lsp_15_24_med_2 360 15 24 1.21 17.93 0.59
lsp_15_24_med_3 360 15 24 1.08 34.85 0.81
lsp_15_24_med_4 360 15 24 1.29 28.31 0.71
lsp_15_24_med_5 360 15 24 0.90 27.56 0.60
lsp_15_24_med_6 360 15 24 1.23 20.66 0.85
lsp_15_24_med_7 360 15 24 0.70 18.18 0.40
lsp_15_24_med_8 360 15 24 0.78 21.17 0.55
lsp_15_24_med_9 360 15 24 1.08 33.86 2.49
lsp_30_6_med_0 180 30 6 0.01 0.00 0.02
lsp_30_6_med_1 180 30 6 16.47 >3600 15.90
lsp_30_6_med_2 180 30 6 4.31 3123.68 7.68
lsp_30_6_med_3 180 30 6 0.01 0.01 0.01
lsp_30_6_med_4 180 30 6 8.51 3111.06 15.87
lsp_30_6_med_5 180 30 6 1.88 596.52 5.10
lsp_30_6_med_6 180 30 6 0.88 39.92 1.97
lsp_30_6_med_7 180 30 6 1.16 144.83 2.27
lsp_30_6_med_8 180 30 6 8.74 >3600 11.79
lsp_30_6_med_9 180 30 6 8.07 1977.33 16.24
lsp_30_12_med_0 360 30 12 3.86 457.28 5.66
lsp_30_12_med_1 360 30 12 428.53 >3600 55.05
lsp_30_12_med_2 360 30 12 18.17 >3600 8.23
lsp_30_12_med_3 360 30 12 3.81 184.95 6.42
lsp_30_12_med_4 360 30 12 315.21 >3600 49.60
lsp_30_12_med_5 360 30 12 16.99 2981.24 9.67
lsp_30_12_med_6 360 30 12 1.83 55.77 1.93
lsp_30_12_med_7 360 30 12 13.03 1154.70 7.82
lsp_30_12_med_8 360 30 12 4.43 188.63 3.48
lsp_30_12_med_9 360 30 12 10.93 958.53 5.92
lsp_30_24_med_0 720 30 24 59.83 >3600 18.13
lsp_30_24_med_1 720 30 24 3.91 445.90 6.76
lsp_30_24_med_2 720 30 24 16.22 1551.93 7.31
lsp_30_24_med_3 720 30 24 509.62 >3600 93.18
lsp_30_24_med_4 720 30 24 17.18 752.27 7.14
lsp_30_24_med_5 720 30 24 1.21 97.84 0.89
lsp_30_24_med_6 720 30 24 49.74 >3600 30.03
lsp_30_24_med_7 720 30 24 6.85 575.14 4.01
lsp_30_24_med_8 720 30 24 9.57 499.89 5.95
lsp_30_24_med_9 720 30 24 7.44 1130.37 8.05