Try our new documentation site (beta).


sensitivity_cs.cs


// Copyright 2020, Gurobi Optimization, LLC

// A simple sensitivity analysis example which reads a MIP model from a
// file and solves it. Then uses the scenario feature to analyze the impact
// w.r.t. the objective function of each binary variable if it is set to
// 1-X, where X is its value in the optimal solution.
//
// Usage:
//     sensitivity_cs <model filename>

using System;
using Gurobi;

class sensitivity_cs
{
  // Maximum number of scenarios to be considered
  public const int MAXSCENARIOS = 100;

  static void Main(string[] args)
  {
    const int maxscenarios = sensitivity_cs.MAXSCENARIOS;

    if (args.Length < 1) {
      Console.Out.WriteLine("Usage: sensitivity_cs filename");
      return;
    }

    try {

      // Create environment
      GRBEnv env = new GRBEnv();

      // Read model
      GRBModel model = new GRBModel(env, args[0]);

      int scenarios;

      if (model.IsMIP == 0) {
        Console.WriteLine("Model is not a MIP");
        return;
      }

      // Solve model
      model.Optimize();

      if (model.Status != GRB.Status.OPTIMAL) {
        Console.WriteLine("Optimization ended with status " + model.Status);
        return;
      }

      // Store the optimal solution
      double   origObjVal = model.ObjVal;
      GRBVar[] vars       = model.GetVars();
      double[] origX      = model.Get(GRB.DoubleAttr.X, vars);

      scenarios = 0;

      // Count number of unfixed, binary variables in model. For each we
      // create a scenario.
      for (int i = 0; i < vars.Length; i++) {
        GRBVar v     = vars[i];
        char   vType = v.VType;

        if (v.LB == 0.0 && v.UB == 1.0                    &&
            (vType == GRB.BINARY || vType == GRB.INTEGER)   ) {
          scenarios++;

          if (scenarios >= maxscenarios)
            break;
        }
      }

      Console.WriteLine("###  construct multi-scenario model with "
                        + scenarios + " scenarios");

      // Set the number of scenarios in the model */
      model.NumScenarios = scenarios;

      scenarios = 0;

      // Create a (single) scenario model by iterating through unfixed
      // binary variables in the model and create for each of these
      // variables a scenario by fixing the variable to 1-X, where X is its
      // value in the computed optimal solution
      for (int i = 0; i < vars.Length; i++) {
        GRBVar v     = vars[i];
        char   vType = v.VType;

        if (v.LB == 0.0 && v.UB == 1.0                    &&
            (vType == GRB.BINARY || vType == GRB.INTEGER) &&
            scenarios < maxscenarios                        ) {

          // Set ScenarioNumber parameter to select the corresponding
          // scenario for adjustments
          model.Parameters.ScenarioNumber = scenarios;

          // Set variable to 1-X, where X is its value in the optimal solution */
          if (origX[i] < 0.5)
            v.ScenNLB = 1.0;
          else
            v.ScenNUB = 0.0;

          scenarios++;
        } else {
          // Add MIP start for all other variables using the optimal solution
          // of the base model
          v.Start = origX[i];
        }
      }

      // Solve multi-scenario model
      model.Optimize();

      // In case we solved the scenario model to optimality capture the
      // sensitivity information
      if (model.Status == GRB.Status.OPTIMAL) {

        // get the model sense (minimization or maximization)
        int modelSense = model.ModelSense;

        scenarios = 0;

        for (int i = 0; i < vars.Length; i++) {
          GRBVar v     = vars[i];
          char   vType = v.VType;

          if (v.LB == 0.0 && v.UB == 1.0                    &&
              (vType == GRB.BINARY || vType == GRB.INTEGER)   ) {

            // Set scenario parameter to collect the objective value of the
            // corresponding scenario
            model.Parameters.ScenarioNumber = scenarios;

            double scenarioObjVal = model.ScenNObjVal;
            double scenarioObjBound = model.ScenNObjBound;

            Console.Write("Objective sensitivity for variable "
                          + v.VarName + " is ");

            // Check if we found a feasible solution for this scenario
            if (scenarioObjVal >= modelSense * GRB.INFINITY) {
              // Check if the scenario is infeasible
              if (scenarioObjBound >= modelSense * GRB.INFINITY)
                Console.WriteLine("infeasible");
              else
                Console.WriteLine("unknown (no solution available)");
            } else {
              // Scenario is feasible and a solution is available
              Console.WriteLine(modelSense * (scenarioObjVal - origObjVal));
            }

            scenarios++;

            if (scenarios >= maxscenarios)
              break;
          }
        }
      }

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

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

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

Gurobi Optimization