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: low (lightly 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=0.75 (low)
InstanceSizePeriodsProductsstdcutfl
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