2

我的 MILP 问题基于简单但大量的约束和变量。

  1. 大多数决策变量是“半”类型的(例如生产数量、运输数量等)
  2. S( wijk ) 是整数类型(一行的移位数)
  3. 数据:delta、theta、alpha、lambda、N( ijw )、Mmin( ijk )、Mmax( ijk )、V0( ik )、D wijk 配方如下

问题公式在小规模上达到了最佳解决方案,但对于较大规模,它只是在某个“间隙”处“停滞”并且不会向前发展

一种。小规模问题规模:少于 15 周(计划范围)
b. 更大规模问题的规模:>= 15 周

问题输入法:.lp 文件(因此在命令提示符下使用 lp.py 脚本解决)
gurobi 版本:6.0(学术许可)

关于我可以做些什么来获得较大案例的最佳解决方案的任何建议?
以下是 15 周和 124 种产品的示例输出日志。

C:\gurobi600\win64\python27\bin>gurobi lp.py model124
Optimize a model with 40146 rows, 61820 columns and 160660 nonzeros
Coefficient statistics:
  Matrix range    [1e+00, 1e+04]
  Objective range [3e-01, 3e+01]
  Bounds range    [0e+00, 0e+00]
  RHS range       [8e+00, 5e+04]
Presolve removed 25296 rows and 23436 columns
Presolve time: 0.15s
Presolved: 14850 rows, 38384 columns, 87748 nonzeros
Variable types: 34754 continuous, 3630 integer (0 binary)

Root relaxation: objective 4.221641e+08, 20576 iterations, 0.27 seconds

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

     0     0 4.2216e+08    0 1394          - 4.2216e+08      -     -    0s
H    0     0                    4.344708e+08 4.2216e+08  2.83%     -    1s
H    0     0                    4.269043e+08 4.2216e+08  1.11%     -    1s
     0     0 4.2246e+08    0 1296 4.2690e+08 4.2246e+08  1.04%     -    1s
     0     0 4.2255e+08    0 1199 4.2690e+08 4.2255e+08  1.02%     -    1s
     0     0 4.2260e+08    0 1141 4.2690e+08 4.2260e+08  1.01%     -    1s
     0     0 4.2260e+08    0 1140 4.2690e+08 4.2260e+08  1.01%     -    1s
     0     0 4.2260e+08    0 1139 4.2690e+08 4.2260e+08  1.01%     -    1s
     0     0 4.2260e+08    0 1135 4.2690e+08 4.2260e+08  1.01%     -    2s
     0     0 4.2260e+08    0 1395 4.2690e+08 4.2260e+08  1.01%     -    2s
H    0     0                    4.267905e+08 4.2260e+08  0.98%     -    3s
     0     0 4.2260e+08    0 1147 4.2679e+08 4.2260e+08  0.98%     -    3s
H    0     0                    4.261854e+08 4.2260e+08  0.84%     -    3s
     0     0 4.2263e+08    0 1117 4.2619e+08 4.2263e+08  0.84%     -    3s
     0     0 4.2263e+08    0 1116 4.2619e+08 4.2263e+08  0.84%     -    3s
     0     0 4.2263e+08    0 1116 4.2619e+08 4.2263e+08  0.84%     -    3s
     0     0 4.2263e+08    0 1114 4.2619e+08 4.2263e+08  0.84%     -    3s
     0     2 4.2263e+08    0 1114 4.2619e+08 4.2263e+08  0.84%     -    4s
    18    17 4.2263e+08    9 1105 4.2619e+08 4.2263e+08  0.84%  21.8    5s
H   27    27                    4.261065e+08 4.2263e+08  0.82%  16.3    5s
H   56    57                    4.260888e+08 4.2263e+08  0.81%  11.7    5s
H   57    59                    4.260728e+08 4.2263e+08  0.81%  11.6    5s
H  176   176                    4.257390e+08 4.2263e+08  0.73%  10.1    6s
H  363   366                    4.257251e+08 4.2263e+08  0.73%   9.9    9s
   451   450 4.2271e+08  153 1009 4.2573e+08 4.2263e+08  0.73%   9.6   10s
H  474   476                    4.254454e+08 4.2263e+08  0.66%   9.4   10s
H  815   816                    4.253518e+08 4.2263e+08  0.64%   9.5   13s
   984   985 4.2281e+08  336  880 4.2535e+08 4.2263e+08  0.64%   9.5   15s
H 1000   999                    4.253516e+08 4.2263e+08  0.64%   9.4   15s
H 1086  1085                    4.253373e+08 4.2263e+08  0.64%   9.4   16s
H 1482  1473                    4.253030e+08 4.2263e+08  0.63%   9.2   19s
  1505  1505 4.2297e+08  536  730 4.2530e+08 4.2263e+08  0.63%   9.1   20s
H 1552  1546                    4.252662e+08 4.2263e+08  0.62%   9.0   20s
H 1577  1580                    4.252426e+08 4.2263e+08  0.62%   9.0   21s
H 1622  1610                    4.252100e+08 4.2263e+08  0.61%   8.9   21s
H 1746  1743                    4.251278e+08 4.2263e+08  0.59%   8.7   22s
H 1779  1782                    4.250611e+08 4.2263e+08  0.57%   8.6   23s
H 1828  1820                    4.250061e+08 4.2263e+08  0.56%   8.5   23s
H 1878  1868                    4.250016e+08 4.2263e+08  0.56%   8.4   23s
H 1931  1917                    4.249827e+08 4.2263e+08  0.55%   8.3   24s
H 1978  1954                    4.248879e+08 4.2263e+08  0.53%   8.3   24s
H 2003  2003                    4.247702e+08 4.2263e+08  0.50%   8.2   39s
H 2004  2003                    4.243849e+08 4.2263e+08  0.41%   8.2   39s
  2007  2008 4.2331e+08  713  604 4.2438e+08 4.2263e+08  0.41%   8.2   40s
H 2450  2450                    4.242743e+08 4.2263e+08  0.39%   7.4   45s
H 2505  2478                    4.242170e+08 4.2263e+08  0.38%   7.3   47s
  2614  2614 4.2371e+08  921  450 4.2422e+08 4.2263e+08  0.38%   7.1   50s
H 2898  2900                    4.242040e+08 4.2263e+08  0.37%   6.9   54s
  2899  2897 4.2385e+08 1011  397 4.2420e+08 4.2263e+08  0.37%   6.9   55s
  3652  3643 4.2416e+08 1247  242 4.2420e+08 4.2263e+08  0.37%   6.5   60s
  3980  3911 4.2265e+08   12 1080 4.2420e+08 4.2265e+08  0.37%  11.6   65s
  4402  4192 4.2298e+08  119 1002 4.2420e+08 4.2265e+08  0.37%  11.3   71s
  4739  4415 4.2313e+08  206  936 4.2420e+08 4.2265e+08  0.37%  11.2   75s
  5194  4723 4.2327e+08  327  863 4.2420e+08 4.2265e+08  0.37%  11.0   81s
  5600  4990 4.2335e+08  430  800 4.2420e+08 4.2265e+08  0.37%  10.9   85s
  6055  5294 4.2347e+08  544  722 4.2420e+08 4.2265e+08  0.37%  10.7   91s
  6526  5610 4.2357e+08  662  647 4.2420e+08 4.2265e+08  0.37%  10.6   96s
  6840  5818 4.2364e+08  743  579 4.2420e+08 4.2265e+08  0.37%  10.5  100s
  7381  6177 4.2376e+08  880  481 4.2420e+08 4.2265e+08  0.37%  10.4  106s
  7745  6422 4.2383e+08  970  414 4.2420e+08 4.2265e+08  0.37%  10.3  110s
  7955  6560 4.2386e+08 1024  370 4.2420e+08 4.2265e+08  0.37%  10.3  119s
  8079  6642 4.2388e+08 1055  342 4.2420e+08 4.2265e+08  0.37%  10.3  121s
  8574  6974 4.2399e+08 1180  242 4.2420e+08 4.2265e+08  0.37%  10.1  126s
  9087  7317 4.2413e+08 1313  174 4.2420e+08 4.2265e+08  0.37%   9.9  131s
H 9675  6763                    4.240515e+08 4.2265e+08  0.33%   9.7  139s
  9677  6760     cutoff 1472      4.2405e+08 4.2265e+08  0.33%   9.7  142s
H 9913  6697                    4.240485e+08 4.2266e+08  0.33%   9.7  148s
H 9914  6503                    4.240433e+08 4.2266e+08  0.33%   9.7  148s
  9915  6510 4.2282e+08   70 1044 4.2404e+08 4.2266e+08  0.33%   9.7  151s
 10194  6691 4.2295e+08  137 1002 4.2404e+08 4.2266e+08  0.33%   9.6  157s
 10196  6695 4.2295e+08  137 1003 4.2404e+08 4.2266e+08  0.33%   9.6  160s
 10843  7125 4.2327e+08  297  881 4.2404e+08 4.2266e+08  0.33%   9.6  167s
 11170  7343 4.2335e+08  376  835 4.2404e+08 4.2266e+08  0.33%   9.5  171s
H11485  7331                    4.240303e+08 4.2266e+08  0.32%   9.5  176s
 11651  7447 4.2345e+08  455  798 4.2403e+08 4.2266e+08  0.32%   9.5  180s
 12009  7680 4.2356e+08  548  735 4.2403e+08 4.2266e+08  0.32%   9.4  186s
H12010  7497                    4.240210e+08 4.2266e+08  0.32%   9.4  186s
 12011  7500 4.2356e+08  548  734 4.2402e+08 4.2266e+08  0.32%   9.4  190s
 12748  7991 4.2376e+08  737  605 4.2402e+08 4.2266e+08  0.32%   9.3  198s
 13135  8275 4.2382e+08  831  528 4.2402e+08 4.2266e+08  0.32%   9.3  203s
 13534  8670 4.2389e+08  928  450 4.2402e+08 4.2266e+08  0.32%   9.2  207s
 13912  9012 4.2395e+08 1024  404 4.2402e+08 4.2266e+08  0.32%   9.2  212s
H14310  9370                    4.240165e+08 4.2266e+08  0.32%   9.2  218s
 14323  9388 4.2401e+08 1125  319 4.2402e+08 4.2266e+08  0.32%   9.2  220s
 14858  9900 4.2290e+08  124 1006 4.2402e+08 4.2266e+08  0.32%   9.2  251s
 14900  9944 4.2291e+08  129  999 4.2402e+08 4.2266e+08  0.32%   9.2  256s
 15362 10409 4.2306e+08  201  942 4.2402e+08 4.2266e+08  0.32%   9.2  261s
 15822 10886 4.2317e+08  283  883 4.2402e+08 4.2266e+08  0.32%   9.1  265s
 16264 11314 4.2327e+08  339  857 4.2402e+08 4.2266e+08  0.32%   9.1  270s
 16698 11771 4.2333e+08  400  822 4.2402e+08 4.2266e+08  0.32%   9.1  275s
 17470 12516 4.2352e+08  493 1135 4.2402e+08 4.2266e+08  0.32%   9.1  297s
 17475 12519 4.2345e+08  288  774 4.2402e+08 4.2345e+08  0.13%   9.1  300s
 17482 12524 4.2374e+08  710  771 4.2402e+08 4.2346e+08  0.13%   9.1  305s
 17490 12531 4.2352e+08  493  764 4.2402e+08 4.2346e+08  0.13%  10.5  310s
 17492 12532 4.2346e+08  232  764 4.2402e+08 4.2346e+08  0.13%  10.5  315s
 17494 12536 4.2346e+08   32  763 4.2402e+08 4.2346e+08  0.13%  11.8  322s
 17495 12536 4.2347e+08   32  763 4.2402e+08 4.2346e+08  0.13%  11.8  328s
 17496 12537 4.2346e+08   33  764 4.2402e+08 4.2346e+08  0.13%  11.8  333s
 17505 12548 4.2348e+08   36  767 4.2402e+08 4.2347e+08  0.13%  11.8  339s
 17641 12631 4.2354e+08   70  751 4.2402e+08 4.2347e+08  0.13%  11.9  343s
 17729 12695 4.2357e+08   91  748 4.2402e+08 4.2347e+08  0.13%  11.9  349s
 17917 12822 4.2360e+08  138  714 4.2402e+08 4.2347e+08  0.13%  11.9  355s
 18182 12993 4.2364e+08  209  658 4.2402e+08 4.2347e+08  0.13%  12.0  361s
 18476 13194 4.2369e+08  282  598 4.2402e+08 4.2347e+08  0.13%  12.0  368s
 18807 13409 4.2372e+08  363  543 4.2402e+08 4.2347e+08  0.13%  12.0  374s
 19048 13572 4.2374e+08  423  492 4.2402e+08 4.2347e+08  0.13%  12.0  381s
 19363 13780 4.2377e+08  501  429 4.2402e+08 4.2347e+08  0.13%  12.1  388s
 19736 14033 4.2380e+08  591  381 4.2402e+08 4.2347e+08  0.13%  12.2  395s
H20136 13669                    4.239906e+08 4.2347e+08  0.12%  12.2  411s
H20137 13073                    4.239710e+08 4.2347e+08  0.12%  12.2  411s
 20138 13074 4.2384e+08  693  315 4.2397e+08 4.2347e+08  0.12%  12.2  419s
 20562 13357 4.2388e+08  797  261 4.2397e+08 4.2347e+08  0.12%  12.3  427s
 21013 13646 4.2391e+08  910  202 4.2397e+08 4.2347e+08  0.12%  12.3  435s
 21354 13826 4.2393e+08  979  161 4.2397e+08 4.2347e+08  0.12%  12.4  443s
 21809 14087 4.2356e+08   84  735 4.2397e+08 4.2348e+08  0.12%  12.4  451s
 22272 14394 4.2365e+08  198  657 4.2397e+08 4.2348e+08  0.12%  12.5  460s
 22753 14713 4.2373e+08  320  601 4.2397e+08 4.2348e+08  0.12%  12.5  469s
 23262 15052 4.2379e+08  448  514 4.2397e+08 4.2348e+08  0.12%  12.6  478s
 23795 15410 4.2384e+08  581  420 4.2397e+08 4.2348e+08  0.12%  12.6  486s
 24253 15713 4.2387e+08  692  348 4.2397e+08 4.2348e+08  0.12%  12.6  496s
 24858 16100 4.2393e+08  843  226 4.2397e+08 4.2348e+08  0.12%  12.6  506s
H25485 15349                    4.239534e+08 4.2348e+08  0.11%  12.7  533s
 25583 15320     cutoff 1051      4.2395e+08 4.2348e+08  0.11%  12.7  542s
 26023 15611 4.2362e+08  167  697 4.2395e+08 4.2348e+08  0.11%  12.7  552s
 26717 16066 4.2372e+08  327  600 4.2395e+08 4.2348e+08  0.11%  12.6  563s
 27500 16601 4.2381e+08  523  455 4.2395e+08 4.2348e+08  0.11%  12.6  575s
 28256 17085 4.2387e+08  667  383 4.2395e+08 4.2348e+08  0.11%  12.5  580s
 28421 17212 4.2388e+08  689  368 4.2395e+08 4.2348e+08  0.11%  12.5  591s
 29251 17740 4.2394e+08  879  240 4.2395e+08 4.2348e+08  0.11%  12.4  600s
 29930 18145 4.2357e+08   73  739 4.2395e+08 4.2348e+08  0.11%  12.3  610s
 30646 18617 4.2369e+08  227  667 4.2395e+08 4.2348e+08  0.11%  12.3  619s
 31336 19071 4.2377e+08  325  607 4.2395e+08 4.2348e+08  0.11%  12.2  628s
 31924 19439 4.2382e+08  426  535 4.2395e+08 4.2348e+08  0.11%  12.2  640s
 32206 19622 4.2385e+08  469  503 4.2395e+08 4.2348e+08  0.11%  12.2  648s
 32792 20001 4.2389e+08  566  443 4.2395e+08 4.2348e+08  0.11%  12.2  656s
 33348 20366 4.2394e+08  700  371 4.2395e+08 4.2348e+08  0.11%  12.2  680s
 33350 20371 4.2394e+08  698  372 4.2395e+08 4.2348e+08  0.11%  12.2  688s
 33876 20696 4.2359e+08  119  724 4.2395e+08 4.2348e+08  0.11%  12.2  697s
 34374 21029 4.2367e+08  247  652 4.2395e+08 4.2348e+08  0.11%  12.2  705s
 34375 21029 4.2367e+08  248  652 4.2395e+08 4.2348e+08  0.11%  12.2  716s
 34896 21387 4.2374e+08  355  588 4.2395e+08 4.2348e+08  0.11%  12.2  723s
 35412 21734 4.2378e+08  429  537 4.2395e+08 4.2348e+08  0.11%  12.2  730s
 35963 22081 4.2383e+08  536  462 4.2395e+08 4.2348e+08  0.11%  12.2  737s
 35964 22081 4.2384e+08  537  462 4.2395e+08 4.2348e+08  0.11%  12.2  742s

4

1 回答 1

1

古罗比没有拖延。它已在最优值的 0.11% 范围内找到解决方案,并继续尝试将界限提高到 0.01%。如果您想尽快停止它,您应该更改参数MIPGap

要真正让 Gurobi 更快,您可以尝试使用Tuning Tool更改其他参数。你最好的办法是收紧你的模型公式,但这取决于问题,你还没有发布你的实际公式。

于 2014-12-16T06:13:12.757 回答