Week $3-$Chapter $3-$Questions

The reduced cost for a positive decision variable is $0.$

a$.$ True

b$.$ False

Decreasing the objective function coefficient of a variable to its lower limit will create a revised problem that is

unbounded.

a$.$ True

b$.$ False

In order to tell the impact of a change in a constraint coefficient, the change must be made and then the model resolved

a$.$ True

b$.$ False

The amount of a sunk cost will vary depending on the values of the decision variables.

a$.$ True

b$.$ False

Any change to the objective function coefficient of a variable that is positive in the optimal solution will change the

optimal solution.

a$.$ True

b$.$ False

If a decision variable is not positive in the optimal solution, its reduced cost is

a$.$ what its objective function value would need to be before it could become positive.

b$.$ the amount its objective function value would need to improve before it could become positive.

c$.$ zero.

d$.$ its dual price.

The amount by which an objective function coefficient can change before a different set of values for the decision

variables becomes optimal is the

a$.$ optimal solution.

b$.$ dual solution.

c$.$ range of optimality.

d$.$ range of feasibility.

The $100\%$ Rule compares

a$.$ proposed changes to allowed changes.

b$.$ new values to original values.

c$.$ objective function changes to right$-$hand side changes.

d$.$ dual prices to reduced costs.

The cost that varies depending on the values of the decision variables is a

a$.$ reduced cost.

b$.$ relevant cost.

c$.$ sunk cost.

d$.$ dual cost.