Simplex Method Concepts and Applications

This video tutorial covers special cases in the Simplex Method, including infeasibility, alternative optimal solutions, unboundedness, and degeneracy. It explains how each case is identified in a simplex tableau and provides examples to illustrate these concepts. The video also includes a section on classifying tableaus into these special cases.

What are the four special cases discussed in the video?

• Feasibility, Alternative Optimal Solutions, Unboundedness, Non-degeneracy

• Infeasibility, Single Optimal Solution, Boundedness, Degeneracy

• Feasibility, Single Optimal Solution, Boundedness, Non-degeneracy

• Infeasibility, Alternative Optimal Solutions, Unboundedness, Degeneracy

What does infeasibility in the Simplex Method indicate?

• The solution is bounded.

• There is no feasible solution.

• The solution is optimal.

• There is a feasible solution.

In the context of infeasibility, what does a positive artificial variable in the final solution mean?

• The solution is bounded.

• The solution is infeasible.

• The solution is optimal.

• The solution is feasible.

What is the pivot element in the context of the Simplex Method?

• The largest positive value in the net evaluation row.

• The smallest negative value in the tableau.

• The largest negative value in the net evaluation row.

• The smallest ratio in the tableau.

What is the role of artificial variables in the Simplex Method?

• To identify degeneracy.

• To determine the pivot element.

• To convert inequalities into equalities.

• To indicate infeasibility.

What does a positive artificial variable in the final solution indicate?

• The solution is bounded.

• The solution is infeasible.

• The solution is optimal.

• The solution is feasible.

What indicates the presence of alternative optimal solutions in a Simplex tableau?

• A basic variable with a positive net evaluation row value.

• A non-basic variable with a zero net evaluation row value.

• A basic variable with a zero net evaluation row value.

• A non-basic variable with a positive net evaluation row value.

What happens when the objective function line is parallel to a constraint line?

• The solution is unbounded.

• There is no feasible solution.

• There are multiple optimal solutions.

• There is a single optimal solution.

What does a zero value in the net evaluation row for a non-basic variable suggest?

• The solution is optimal.

• The solution is unbounded.

• There are alternative optimal solutions.

• The solution is infeasible.

What is the significance of the net evaluation row in the Simplex Method?

• It determines the boundedness of the solution.

• It identifies the degeneracy of the solution.

• It shows the potential change in the objective function value.

• It indicates the feasibility of the solution.