Build by Rows

ILOG CPLEX 11.0 User's Manual > Languages and APIs > ILOG Concert Technology for .NET Users > Model > Build by Rows

Build by Rows

The finished application is capable of building a model by rows or by columns, according to an option entered through the command line by the user. The next steps in this tutorial show you how to add a static method to your application. This method builds a model by rows.


Step 6 -	Set up rows

Go to the comment Step 6 in Dietlesson.cs, and add the following lines to set up your application to build the model by rows.

   internal static void BuildModelByRow(IModeler    model,
                                        Data        data,
                                        INumVar[]   Buy,
                                        NumVarType  type) {
      int nFoods = data.nFoods;
      int nNutrs = data.nNutrs;

Those lines begin the static method to build a model by rows. The next steps in this tutorial show you the heart of that method.


Step 7 -	Create the variables: build and populate by rows

Go to the comment Step 7 in Dietlesson.cs, and add the following lines to create a loop that creates the variables of the problem with the bounds specified by the input data.

      for (int j = 0; j < nFoods; j++) {
         Buy[j] = model.NumVar(data.foodMin[j], data.foodMax[j], type);
      }


Step 8 -	Add objective

Go to the comment Step 8 in Dietlesson.cs, and add this statement to add the objective to the model.

      model.AddMinimize(model.ScalProd(data.foodCost, Buy));

The objective function indicates that you want to minimize the cost of the diet computed as the sum of the amount of each food to buy Buy[i] times the unit price of that food data.foodCost[i].


Step 9 -	Add nutritional constraints

Go to the comment Step 9 in Dietlesson.cs, and add the following lines to add the ranged nutritional constraints to the model.

      for (int i = 0; i < nNutrs; i++) {
         model.AddRange(data.nutrMin[i],
                        model.ScalProd(data.nutrPerFood[i], Buy),
                        data.nutrMax[i]);
      }
   }

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