java输出总是目标函数上的背包问题

您好，我正在尝试使用java和cplex 12.8创建并解决一个只有1个箱子的简单背包问题。我不明白为什么它总是在输出中给出目标函数的值。 这是我的全部代码：

    public static void solveModel(){       

        try {

            n_obj = 5;
            int capacity = 4

            int[] profits = new int[n_obj];
            for(int i = 0; i < n_obj; i++ ){
                weight[i] = ThreadLocalRandom.current().nextInt(1, n_obj/2 + 1);
                profits[i] = ThreadLocalRandom.current().nextInt(1, 12);
            }


            for(int i = 0; i < weight.length; i++){
                System.out.println("Weight " + i + ":\t" + weight[i]);
                System.out.println("Profit " + i + ":\t" + profits[i]);

            }

            IloCplex model = new IloCplex();
            IloNumVar x = model.boolVar();



            IloLinearNumExpr obj = model.linearNumExpr();
            for(int i = 0; i < n_obj; i++){
                obj.addTerm(profits[i], x);
            }

            //obj function
            model.addMaximize(obj);

            //constraints

            for(int i = 0; i < n_obj; i++){
                model.addLe(model.prod(weight[i], x), capacity) ;
                model.addEq(x, 1);

            }

            if (model.solve()) {

                System.out.println("Obj = " + model.getObjValue());
            }
            else {
                System.out.println("Problem not solved");
            }

            model.end();



        } catch (IloException e) {
            e.printStackTrace();
        }

    }

---

CPLEX计算出问题的最佳解决方案，目标值为28

    public static void solveModel(){       

        try {

            n_obj = 5;
            int capacity = 4

            int[] profits = new int[n_obj];
            for(int i = 0; i < n_obj; i++ ){
                weight[i] = ThreadLocalRandom.current().nextInt(1, n_obj/2 + 1);
                profits[i] = ThreadLocalRandom.current().nextInt(1, 12);
            }


            for(int i = 0; i < weight.length; i++){
                System.out.println("Weight " + i + ":\t" + weight[i]);
                System.out.println("Profit " + i + ":\t" + profits[i]);

            }

            IloCplex model = new IloCplex();
            IloNumVar x = model.boolVar();



            IloLinearNumExpr obj = model.linearNumExpr();
            for(int i = 0; i < n_obj; i++){
                obj.addTerm(profits[i], x);
            }

            //obj function
            model.addMaximize(obj);

            //constraints

            for(int i = 0; i < n_obj; i++){
                model.addLe(model.prod(weight[i], x), capacity) ;
                model.addEq(x, 1);

            }

            if (model.solve()) {

                System.out.println("Obj = " + model.getObjValue());
            }
            else {
                System.out.println("Problem not solved");
            }

            model.end();



        } catch (IloException e) {
            e.printStackTrace();
        }

    }

---

默认情况下，CPLEX以完全确定的方式运行。也就是说，当在相同条件下多次运行时（要解决的问题相同，机器相同等），CPLEX将始终返回完全相同的结果。因此，在多次运行您的程序时，没有理由期待不同的解决方案。

好的，对于那些感兴趣或将感兴趣的人来说。。。我自己解决了。

    public static void solveModel(){       

        try {

            n_obj = 5;
            int capacity = 4

            int[] profits = new int[n_obj];
            for(int i = 0; i < n_obj; i++ ){
                weight[i] = ThreadLocalRandom.current().nextInt(1, n_obj/2 + 1);
                profits[i] = ThreadLocalRandom.current().nextInt(1, 12);
            }


            for(int i = 0; i < weight.length; i++){
                System.out.println("Weight " + i + ":\t" + weight[i]);
                System.out.println("Profit " + i + ":\t" + profits[i]);

            }

            IloCplex model = new IloCplex();
            IloNumVar x = model.boolVar();



            IloLinearNumExpr obj = model.linearNumExpr();
            for(int i = 0; i < n_obj; i++){
                obj.addTerm(profits[i], x);
            }

            //obj function
            model.addMaximize(obj);

            //constraints

            for(int i = 0; i < n_obj; i++){
                model.addLe(model.prod(weight[i], x), capacity) ;
                model.addEq(x, 1);

            }

            if (model.solve()) {

                System.out.println("Obj = " + model.getObjValue());
            }
            else {
                System.out.println("Problem not solved");
            }

            model.end();



        } catch (IloException e) {
            e.printStackTrace();
        }

    }

布尔变量的声明是通过以下方式完成的：

    public static void solveModel(){       

        try {

            n_obj = 5;
            int capacity = 4

            int[] profits = new int[n_obj];
            for(int i = 0; i < n_obj; i++ ){
                weight[i] = ThreadLocalRandom.current().nextInt(1, n_obj/2 + 1);
                profits[i] = ThreadLocalRandom.current().nextInt(1, 12);
            }


            for(int i = 0; i < weight.length; i++){
                System.out.println("Weight " + i + ":\t" + weight[i]);
                System.out.println("Profit " + i + ":\t" + profits[i]);

            }

            IloCplex model = new IloCplex();
            IloNumVar x = model.boolVar();



            IloLinearNumExpr obj = model.linearNumExpr();
            for(int i = 0; i < n_obj; i++){
                obj.addTerm(profits[i], x);
            }

            //obj function
            model.addMaximize(obj);

            //constraints

            for(int i = 0; i < n_obj; i++){
                model.addLe(model.prod(weight[i], x), capacity) ;
                model.addEq(x, 1);

            }

            if (model.solve()) {

                System.out.println("Obj = " + model.getObjValue());
            }
            else {
                System.out.println("Problem not solved");
            }

            model.end();



        } catch (IloException e) {
            e.printStackTrace();
        }

    }

IloNumVar[] x = new IloNumVar[n_obj];
for (int i = 0; i < n_obj; i++) {
//x[i] = model.numVar(0, Double.POSITIVE_INFINITY, IloNumVarType.Bool, "x[" + i + 
//"]");
    x[i] = model.boolVar();
}

IloNumVar[]x=新的IloNumVar[n_obj]；
对于（int i=0；i

我修改了约束条件：

    public static void solveModel(){       

        try {

            n_obj = 5;
            int capacity = 4

            int[] profits = new int[n_obj];
            for(int i = 0; i < n_obj; i++ ){
                weight[i] = ThreadLocalRandom.current().nextInt(1, n_obj/2 + 1);
                profits[i] = ThreadLocalRandom.current().nextInt(1, 12);
            }


            for(int i = 0; i < weight.length; i++){
                System.out.println("Weight " + i + ":\t" + weight[i]);
                System.out.println("Profit " + i + ":\t" + profits[i]);

            }

            IloCplex model = new IloCplex();
            IloNumVar x = model.boolVar();



            IloLinearNumExpr obj = model.linearNumExpr();
            for(int i = 0; i < n_obj; i++){
                obj.addTerm(profits[i], x);
            }

            //obj function
            model.addMaximize(obj);

            //constraints

            for(int i = 0; i < n_obj; i++){
                model.addLe(model.prod(weight[i], x), capacity) ;
                model.addEq(x, 1);

            }

            if (model.solve()) {

                System.out.println("Obj = " + model.getObjValue());
            }
            else {
                System.out.println("Problem not solved");
            }

            model.end();



        } catch (IloException e) {
            e.printStackTrace();
        }

    }

 IloLinearNumExpr lin = model.linearNumExpr();
        for (int i = 0; i < n_obj; i++) {
            //model.addLe(model.prod(weight[i], x[i]), capacity);
            lin.addTerm(x[i], weight[i]);
        }

        model.addLe(lin, capacity, "Constraints");

IloLinearNumExpr lin=model.linearNumExpr（）；
对于（int i=0；i

我知道这是一个简单的背包问题，但我是一个cplex初学者，我希望它对其他人有用

    public static void solveModel(){       

        try {

            n_obj = 5;
            int capacity = 4

            int[] profits = new int[n_obj];
            for(int i = 0; i < n_obj; i++ ){
                weight[i] = ThreadLocalRandom.current().nextInt(1, n_obj/2 + 1);
                profits[i] = ThreadLocalRandom.current().nextInt(1, 12);
            }


            for(int i = 0; i < weight.length; i++){
                System.out.println("Weight " + i + ":\t" + weight[i]);
                System.out.println("Profit " + i + ":\t" + profits[i]);

            }

            IloCplex model = new IloCplex();
            IloNumVar x = model.boolVar();



            IloLinearNumExpr obj = model.linearNumExpr();
            for(int i = 0; i < n_obj; i++){
                obj.addTerm(profits[i], x);
            }

            //obj function
            model.addMaximize(obj);

            //constraints

            for(int i = 0; i < n_obj; i++){
                model.addLe(model.prod(weight[i], x), capacity) ;
                model.addEq(x, 1);

            }

            if (model.solve()) {

                System.out.println("Obj = " + model.getObjValue());
            }
            else {
                System.out.println("Problem not solved");
            }

            model.end();



        } catch (IloException e) {
            e.printStackTrace();
        }

    }

---

玩得开心

也许我解释错了。问题是，对于任何价值清单，它总是在产出中给出利润的总和。我想我在初始化布尔变量时出错了

    public static void solveModel(){       

        try {

            n_obj = 5;
            int capacity = 4

            int[] profits = new int[n_obj];
            for(int i = 0; i < n_obj; i++ ){
                weight[i] = ThreadLocalRandom.current().nextInt(1, n_obj/2 + 1);
                profits[i] = ThreadLocalRandom.current().nextInt(1, 12);
            }


            for(int i = 0; i < weight.length; i++){
                System.out.println("Weight " + i + ":\t" + weight[i]);
                System.out.println("Profit " + i + ":\t" + profits[i]);

            }

            IloCplex model = new IloCplex();
            IloNumVar x = model.boolVar();



            IloLinearNumExpr obj = model.linearNumExpr();
            for(int i = 0; i < n_obj; i++){
                obj.addTerm(profits[i], x);
            }

            //obj function
            model.addMaximize(obj);

            //constraints

            for(int i = 0; i < n_obj; i++){
                model.addLe(model.prod(weight[i], x), capacity) ;
                model.addEq(x, 1);

            }

            if (model.solve()) {

                System.out.println("Obj = " + model.getObjValue());
            }
            else {
                System.out.println("Problem not solved");
            }

            model.end();



        } catch (IloException e) {
            e.printStackTrace();
        }

    }