Java Cplex结果与IBM Optimization studio不同

Question

我使用OPL创建模型，在IBM Optimization Studio中实现了一个LP问题。在验证了模型之后，我想将其放入Java中，以便编写用于模拟的参数脚本。我通过以下代码找到了在Java中使用OLP模型的最快方法：

IloOplFactory.setDebugMode(false);
IloOplFactory oplF = new IloOplFactory();
IloOplErrorHandler errHandler = oplF.createOplErrorHandler(System.out);
IloOplModelSource modelSource = oplF.createOplModelSource("myModel.mod");
IloCplex cplex = null;
cplex = oplF.createCplex();
IloOplSettings settings = oplF.createOplSettings(errHandler);
IloOplModelDefinition def = oplF.createOplModelDefinition(modelSource, settings);
IloOplModel opl = oplF.createOplModel(def, cplex);

String inDataFile = "myData.dat";
IloOplDataSource dataSource = oplF.createOplDataSource(inDataFile);
opl.addDataSource(dataSource);

opl.generate();
opl.convertAllIntVars(); // converts integer bounds into LP compatible format
if (cplex.solve()) {
        double obj = opl.getCplex().getObjValue();
        System.out.println("OBJECTIVE: " + obj);
}

现在的问题是，如果我在IBM Optimization studio和Java中运行"myModel.mod“和"myData.dat”，我会得到非常不同的客观结果。

在IBM Optimization Studio中：

solution (optimal) with objective 125

在Java中：

Parallel mode: deterministic, using up to 4 threads for concurrent optimization.
Tried aggregator 1 time.
LP Presolve eliminated 0 rows and 1 columns.
Reduced LP has 5280 rows, 5325 columns, and 25525 nonzeros.
Presolve time = 0.01 sec. (3.77 ticks)

Iteration log . . .
Iteration:     1   Dual objective     =             0.000000
Iteration:   345   Dual objective     =            90.297455
Iteration:   568   Dual objective     =           117.206047
Perturbation started.
Iteration:   707   Dual objective     =           117.206047
Removing perturbation.
Reinitializing dual norms . . .

Dual simplex solved model.

OBJECTIVE: 117.20608137232513

我查看了数据集和我的模型，低于125的目标应该是不可能的(我使用了极值来确保我的一个目标变量是125，所以任何低于125的目标都应该是不可能的)。

有人知道为什么这些结果是不同的吗？与IBM相比，它是否存在于Java的设置中？我是否可以将我的IBM优化工作室设置也导入到Java中来进行测试？

谢谢!

编辑:这是IBM Optimization studio的日志，我忘了把它们包括进去。它确实显示了更多关于整数被截断的信息，但这是我第一次使用这两个程序，所以有人能帮我定位问题所在或这一切意味着什么吗？

Found incumbent of value 125.000000 after 0.00 sec. (0.53 ticks)
Tried aggregator 1 time.
MIP Presolve eliminated 5027 rows and 4764 columns.
MIP Presolve modified 1191 coefficients.
Reduced MIP has 253 rows, 562 columns, and 1958 nonzeros.
Reduced MIP has 562 binaries, 0 generals, 0 SOSs, and 0 indicators.
Presolve time = 0.02 sec. (8.46 ticks)
Probing fixed 8 vars, tightened 0 bounds.
Probing time = 0.00 sec. (2.86 ticks)
Tried aggregator 1 time.
MIP Presolve eliminated 158 rows and 306 columns.
MIP Presolve modified 293 coefficients.
Reduced MIP has 95 rows, 256 columns, and 632 nonzeros.
Reduced MIP has 256 binaries, 0 generals, 0 SOSs, and 0 indicators.
Presolve time = 0.02 sec. (1.73 ticks)
Probing fixed 6 vars, tightened 0 bounds.
Probing time = 0.00 sec. (0.12 ticks)
Tried aggregator 1 time.
MIP Presolve eliminated 29 rows and 30 columns.
MIP Presolve modified 2 coefficients.
Reduced MIP has 66 rows, 226 columns, and 522 nonzeros.
Reduced MIP has 226 binaries, 0 generals, 0 SOSs, and 0 indicators.
Presolve time = 0.00 sec. (0.42 ticks)
Probing time = 0.00 sec. (0.07 ticks)
Clique table members: 34.
MIP emphasis: balance optimality and feasibility.
MIP search method: dynamic search.
Parallel mode: deterministic, using up to 4 threads.
Root relaxation solution time = 0.00 sec. (0.36 ticks)

    Nodes                                         Cuts/
   Node  Left     Objective  IInf  Best Integer    Best Bound    ItCnt     Gap

*     0+    0                          125.0000      123.0000             1.60%
      0     0      123.8469     4      125.0000      123.8469       62    0.92%
      0     0        cutoff            125.0000                     81    0.00%
Elapsed time = 0.09 sec. (17.09 ticks, tree = 0.00 MB, solutions = 1)

Zero-half cuts applied:  1
Gomory fractional cuts applied:  1

Root node processing (before b&c):
  Real time             =    0.09 sec. (17.22 ticks)
Parallel b&c, 4 threads:
  Real time             =    0.00 sec. (0.00 ticks)
  Sync time (average)   =    0.00 sec.
  Wait time (average)   =    0.00 sec.
                      ------------
Total (root+branch&cut) =    0.09 sec. (17.22 ticks)

编辑2:我发现我的矩阵0..1中的整数值不是四舍五入为0或1，而是被计为0.932……如何强制Java cplex对整数进行舍入？

已解决:它是"opl.convertAllIntVars();“，它将所有内容转换为doubles。我从示例代码中提取并删除了它，现在一切正常。

Answer 1

你的模型在某些方面会有所不同。我怀疑整数变量可能被处理的方式-值125是一个相当舍入的数字，而117.2显然不是整数，日志显示它是作为一个直接的LP来解决的。

查看两个模型的解决方案中变量的值，找出值的不同之处。希望两个解决方案中的许多变量值都是相似的，这将有助于缩小搜索范围。

尝试将模型导出为LP文件或两者中的类似文件，并比较生成的模型。也许可以在CPLEX命令行求解器中求解两个导出的模型，并比较日志和结果。

尝试在另一个模型中修复一个解决方案中的一些值，然后重新求解。

另外，给我们提供更多来自OPL模型的日志输出。