# 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 data Factor: low Factor: med Factor: high CPU time required [select] [select] [select] Number of solution failures [select] [select] [select] Solutions [select] [select] [select]

## Solutions obtained

### 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.)
 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_med_0 90 15 6 infeas infeas infeas lsp_15_6_med_1 90 15 6 29975* 29975* 29975* lsp_15_6_med_2 90 15 6 33870* 33870* 33870* lsp_15_6_med_3 90 15 6 infeas infeas infeas lsp_15_6_med_4 90 15 6 31914* 31914* 31914* lsp_15_6_med_5 90 15 6 30100* 30100* 30100* lsp_15_6_med_6 90 15 6 36434* 36434* 36434* lsp_15_6_med_7 90 15 6 29465* 29465* 29465* lsp_15_6_med_8 90 15 6 32067* 32067* 32067* lsp_15_6_med_9 90 15 6 29815* 29815* 29815* lsp_15_12_med_0 180 15 12 62608* 62608* 62608* lsp_15_12_med_1 180 15 12 61684* 61684* 61684* lsp_15_12_med_2 180 15 12 62674* 62674* 62674* lsp_15_12_med_3 180 15 12 infeas infeas infeas lsp_15_12_med_4 180 15 12 64304* 64304* 64304* lsp_15_12_med_5 180 15 12 56869* 56869* 56869* lsp_15_12_med_6 180 15 12 62045* 62045* 62045* lsp_15_12_med_7 180 15 12 60734* 60734* 60734* lsp_15_12_med_8 180 15 12 63135* 63135* 63135* lsp_15_12_med_9 180 15 12 56269* 56269* 56269* lsp_15_24_med_0 360 15 24 125769* 125769* 125769* lsp_15_24_med_1 360 15 24 127745* 127745* 127745* lsp_15_24_med_2 360 15 24 122034* 122034* 122034* lsp_15_24_med_3 360 15 24 118360* 118360* 118360* lsp_15_24_med_4 360 15 24 127240* 127240* 127244* lsp_15_24_med_5 360 15 24 111583* 111583* 111583* lsp_15_24_med_6 360 15 24 122226* 122226* 122226* lsp_15_24_med_7 360 15 24 116208* 116208* 116208* lsp_15_24_med_8 360 15 24 117616* 117616* 117616* lsp_15_24_med_9 360 15 24 111668* 111668* 111668* lsp_30_6_med_0 180 30 6 infeas infeas infeas lsp_30_6_med_1 180 30 6 60906* 60906, 60536.1 60906* lsp_30_6_med_2 180 30 6 64883* 64883* 64883* lsp_30_6_med_3 180 30 6 infeas infeas infeas lsp_30_6_med_4 180 30 6 65661* 65661* 65661* lsp_30_6_med_5 180 30 6 55405* 55405* 55405* lsp_30_6_med_6 180 30 6 64555* 64555* 64555* lsp_30_6_med_7 180 30 6 59523* 59523* 59523* lsp_30_6_med_8 180 30 6 66912* 66912, 66811.4 66912* lsp_30_6_med_9 180 30 6 61717* 61717* 61717* lsp_30_12_med_0 360 30 12 120854* 120854* 120854* lsp_30_12_med_1 360 30 12 129944* 129944, 129631 129944* lsp_30_12_med_2 360 30 12 123264* 123264, 123185 123264* lsp_30_12_med_3 360 30 12 119411* 119406* 119406* lsp_30_12_med_4 360 30 12 128548* 128548, 128222 128548* lsp_30_12_med_5 360 30 12 116010* 116010* 116010* lsp_30_12_med_6 360 30 12 121365* 121365* 121365* lsp_30_12_med_7 360 30 12 119575* 119575* 119575* lsp_30_12_med_8 360 30 12 120059* 120059* 120059* lsp_30_12_med_9 360 30 12 117522* 117522* 117522* lsp_30_24_med_0 720 30 24 248216* 248216, 248109 248216* lsp_30_24_med_1 720 30 24 242151* 242151* 242151* lsp_30_24_med_2 720 30 24 243189* 243189* 243189* lsp_30_24_med_3 720 30 24 243494* 243494, 243247 243494* lsp_30_24_med_4 720 30 24 242022* 242015* 242015* lsp_30_24_med_5 720 30 24 231428* 231433* 231421* lsp_30_24_med_6 720 30 24 247543* 247543, 247464 247548* lsp_30_24_med_7 720 30 24 232357* 232357* 232357* lsp_30_24_med_8 720 30 24 234098* 234092* 234092* lsp_30_24_med_9 720 30 24 223357* 223357* 223357*