一、涉及软件

Visual Studio 2017、Visual Studio 2019
CPLEX 12.9.0

二、配置效果

1、可以实现C++调用CPLEX求解线性规划，混合整数规划等；
2、可以在debug和release两种模式下进行调试或者运行代码

三、配置步骤

1、首先选择代码运行的环境

(1)、将平台设置为x64或者(活动)x64，此处一定需要修改！不能使用x32平台模式
(2)、配置的设置可以根据需要自行选择release模式或者debug模式（作者建议两种都进行配置一下，方便后续使用）

2、打开项目的属性项

3、修改C/C++附加包含目录

这里找到CPLEX的安装目录(根据自己的安装目录进行修改)，如本人的是：
(注：下述若不做特殊说明，均需要将目录替换为自己的目录，若安装时没进行修改，从IBM/ILOG/…的目录同下述目录应该相同)

//CPLEX安装目录
D:\Program Files\IBM\ILOG\CPLEX_Studio129\concert\include
D:\Program Files\IBM\ILOG\CPLEX_Studio129\cplex\include

将上述两项加入到附加目录之中，点击确定，效果如下：

4、修改C/C++预处理器中的预处理器定义项

添加下述命令到预处理器定义中（此处无需进行修改，直接复制粘贴就好）：

NDEBUG
_CONSOLE
IL_STD

5、修改C/C++代码生成中的运行库

将此处的改为多线程DLL(/MD)或者多线程调试DLL(/MDd)

6、修改链接器常规项中的“附加库目录项”

将下述两个目录添加到“附加库目录项”中，目录地址同上述CPLEX安装目录相同

D:\Program Files\IBM\ILOG\CPLEX_Studio129\concert\lib\x64_windows_vs2017\stat_mda
D:\Program Files\IBM\ILOG\CPLEX_Studio129\cplex\lib\x64_windows_vs2017\stat_mda

7、修改链接器输入中的“附加依赖项”

将下述两个目录添加到“附加库目录项”中，目录地址同上述CPLEX安装目录相同

D:\Program Files\IBM\ILOG\CPLEX_Studio129\concert\lib\x64_windows_vs2017\stat_mda\concert.lib
D:\Program Files\IBM\ILOG\CPLEX_Studio129\cplex\lib\x64_windows_vs2017\stat_mda\cplex1290.lib
D:\Program Files\IBM\ILOG\CPLEX_Studio129\cplex\lib\x64_windows_vs2017\stat_mda\ilocplex.lib

8、 点击确定，点击应用，大功告成！

四、运行测试代码

1、下面给出CPLEX官方测试文档中的cutstock代码进行测试

#include <ilcplex/ilocplex.h>ILOSTLBEGIN#define RC_EPS 1.0e-6static void readData(const char* filename, IloNum& rollWidth,IloNumArray& size, IloNumArray& amount);
static void report1(IloCplex& cutSolver, IloNumVarArray Cut,IloRangeArray Fill);
static void report2(IloAlgorithm& patSolver,IloNumVarArray Use,IloObjective obj);
static void report3(IloCplex& cutSolver, IloNumVarArray Cut);/// MAIN PROGRAM ///int main(int argc, char **argv)
{IloEnv env;try {IloInt  i, j;IloNum      rollWidth;IloNumArray amount(env);IloNumArray size(env);if (argc > 1)readData(argv[1], rollWidth, size, amount);elsereadData("cutstock.dat",rollWidth, size, amount);/// CUTTING-OPTIMIZATION PROBLEM ///IloModel cutOpt(env);IloObjective   RollsUsed = IloAdd(cutOpt, IloMinimize(env));IloRangeArray  Fill = IloAdd(cutOpt,IloRangeArray(env, amount, IloInfinity));IloNumVarArray Cut(env);IloInt nWdth = size.getSize();for (j = 0; j < nWdth; j++) {Cut.add(IloNumVar(RollsUsed(1) + Fill[j](int(rollWidth / size[j]))));}IloCplex cutSolver(cutOpt);/// PATTERN-GENERATION PROBLEM ///IloModel patGen(env);IloObjective ReducedCost = IloAdd(patGen, IloMinimize(env, 1));IloNumVarArray Use(env, nWdth, 0.0, IloInfinity, ILOINT);patGen.add(IloScalProd(size, Use) <= rollWidth);IloCplex patSolver(patGen);/// COLUMN-GENERATION PROCEDURE ///IloNumArray price(env, nWdth);IloNumArray newPatt(env, nWdth);/// COLUMN-GENERATION PROCEDURE ///for (;;) {/// OPTIMIZE OVER CURRENT PATTERNS ///cutSolver.solve();report1(cutSolver, Cut, Fill);/// FIND AND ADD A NEW PATTERN ///for (i = 0; i < nWdth; i++) {price[i] = -cutSolver.getDual(Fill[i]);}ReducedCost.setLinearCoefs(Use, price);patSolver.solve();report2(patSolver, Use, ReducedCost);if (patSolver.getValue(ReducedCost) > -RC_EPS) break;patSolver.getValues(newPatt, Use);Cut.add(IloNumVar(RollsUsed(1) + Fill(newPatt)));}cutOpt.add(IloConversion(env, Cut, ILOINT));cutSolver.solve();cout << "Solution status: " << cutSolver.getStatus() << endl;report3(cutSolver, Cut);}catch (IloException& ex) {cerr << "Error: " << ex << endl;}catch (...) {cerr << "Error" << endl;}env.end();return 0;
}static void readData(const char* filename, IloNum& rollWidth,IloNumArray& size, IloNumArray& amount)
{ifstream in(filename);if (in) {in >> rollWidth;in >> size;in >> amount;}else {cerr << "No such file: " << filename << endl;throw(1);}
}static void report1(IloCplex& cutSolver, IloNumVarArray Cut,IloRangeArray Fill)
{cout << endl;cout << "Using " << cutSolver.getObjValue() << " rolls" << endl;cout << endl;for (IloInt j = 0; j < Cut.getSize(); j++) {cout << "  Cut" << j << " = " << cutSolver.getValue(Cut[j]) << endl;}cout << endl;for (IloInt i = 0; i < Fill.getSize(); i++) {cout << "  Fill" << i << " = " << cutSolver.getDual(Fill[i]) << endl;}cout << endl;
}static void report2(IloAlgorithm& patSolver, IloNumVarArray Use,IloObjective obj)
{cout << endl;cout << "Reduced cost is " << patSolver.getValue(obj) << endl;cout << endl;if (patSolver.getValue(obj) <= -RC_EPS) {for (IloInt i = 0; i < Use.getSize(); i++) {cout << "  Use" << i << " = " << patSolver.getValue(Use[i]) << endl;}cout << endl;}
}static void report3(IloCplex& cutSolver, IloNumVarArray Cut)
{cout << endl;cout << "Best integer solution uses "<< cutSolver.getObjValue() << " rolls" << endl;cout << endl;for (IloInt j = 0; j < Cut.getSize(); j++) {cout << "  Cut" << j << " = " << cutSolver.getValue(Cut[j]) << endl;}
}

2、首先编译代码，出现下面截图说明配置成功辽

3、给出输入文件信息如下：

115
[25, 40, 50, 55, 70]
[50, 36, 24, 8, 30]

4、运行结果如下：

THE END

谢谢观看，欢迎批评指正