Try our new documentation site.


diet_cs.cs


/* Copyright 2019, Gurobi Optimization, LLC */

/* Solve the classic diet model, showing how to add constraints
   to an existing model. */

using System;
using Gurobi;

class diet_cs
{
  static void Main()
  {
    try {

      // Nutrition guidelines, based on
      // USDA Dietary Guidelines for Americans, 2005
      // http://www.health.gov/DietaryGuidelines/dga2005/
      string[] Categories =
          new string[] { "calories", "protein", "fat", "sodium" };
      int nCategories = Categories.Length;
      double[] minNutrition = new double[] { 1800, 91, 0, 0 };
      double[] maxNutrition = new double[] { 2200, GRB.INFINITY, 65, 1779 };

      // Set of foods
      string[] Foods =
          new string[] { "hamburger", "chicken", "hot dog", "fries",
              "macaroni", "pizza", "salad", "milk", "ice cream" };
      int nFoods = Foods.Length;
      double[] cost =
          new double[] { 2.49, 2.89, 1.50, 1.89, 2.09, 1.99, 2.49, 0.89,
              1.59 };

      // Nutrition values for the foods
      double[,] nutritionValues = new double[,] {
          { 410, 24, 26, 730 },   // hamburger
          { 420, 32, 10, 1190 },  // chicken
          { 560, 20, 32, 1800 },  // hot dog
          { 380, 4, 19, 270 },    // fries
          { 320, 12, 10, 930 },   // macaroni
          { 320, 15, 12, 820 },   // pizza
          { 320, 31, 12, 1230 },  // salad
          { 100, 8, 2.5, 125 },   // milk
          { 330, 8, 10, 180 }     // ice cream
          };

      // Model
      GRBEnv env = new GRBEnv();
      GRBModel model = new GRBModel(env);

      model.ModelName = "diet";

      // Create decision variables for the nutrition information,
      // which we limit via bounds
      GRBVar[] nutrition = new GRBVar[nCategories];
      for (int i = 0; i < nCategories; ++i) {
        nutrition[i] =
            model.AddVar(minNutrition[i], maxNutrition[i], 0, GRB.CONTINUOUS,
                         Categories[i]);
      }

      // Create decision variables for the foods to buy
      GRBVar[] buy = new GRBVar[nFoods];
      for (int j = 0; j < nFoods; ++j) {
        buy[j] =
            model.AddVar(0, GRB.INFINITY, cost[j], GRB.CONTINUOUS, Foods[j]);
      }

      // The objective is to minimize the costs
      model.ModelSense = GRB.MINIMIZE;

      // Nutrition constraints
      for (int i = 0; i < nCategories; ++i) {
        GRBLinExpr ntot = 0.0;
        for (int j = 0; j < nFoods; ++j)
          ntot.AddTerm(nutritionValues[j,i], buy[j]);
        model.AddConstr(ntot == nutrition[i], Categories[i]);
      }

      // Solve
      model.Optimize();
      PrintSolution(model, buy, nutrition);

      Console.WriteLine("\nAdding constraint: at most 6 servings of dairy");
      model.AddConstr(buy[7] + buy[8] <= 6.0, "limit_dairy");

      // Solve
      model.Optimize();
      PrintSolution(model, buy, nutrition);

      // Dispose of model and env
      model.Dispose();
      env.Dispose();

    } catch (GRBException e) {
      Console.WriteLine("Error code: " + e.ErrorCode + ". " +
          e.Message);
    }
  }

  private static void PrintSolution(GRBModel model, GRBVar[] buy,
                                    GRBVar[] nutrition) {
    if (model.Status == GRB.Status.OPTIMAL) {
      Console.WriteLine("\nCost: " + model.ObjVal);
      Console.WriteLine("\nBuy:");
      for (int j = 0; j < buy.Length; ++j) {
        if (buy[j].X > 0.0001) {
          Console.WriteLine(buy[j].VarName + " " + buy[j].X);
        }
      }
      Console.WriteLine("\nNutrition:");
      for (int i = 0; i < nutrition.Length; ++i) {
        Console.WriteLine(nutrition[i].VarName + " " + nutrition[i].X);
      }
    } else {
      Console.WriteLine("No solution");
    }
  }
}

Try Gurobi for Free

Choose the evaluation license that fits you best, and start working with our Expert Team for technical guidance and support.

Evaluation License
Get a free, full-featured license of the Gurobi Optimizer to experience the performance, support, benchmarking and tuning services we provide as part of our product offering.
Academic License
Gurobi supports the teaching and use of optimization within academic institutions. We offer free, full-featured copies of Gurobi for use in class, and for research.
Cloud Trial

Request free trial hours, so you can see how quickly and easily a model can be solved on the cloud.

Search