10 questions
A controllable input for a linear programming model is known as a
decision variable
dummy variable
parameter
constraint
Nonnegativity constraints ensure that
the solution to the problem will contain only nonnegative values for the decision variables
the problem modeling includes only nonnegative values in the constraints
the objective function of the problem always returns maximum quantities
there are no inequalities in the constraints
Which of the following error messages is displayed in Excel Solver when attempting to solve an unbounded problem?
Objective Cell values do not converge
Solver cannot improve the current solution. All constraints are satisfied
Solver could not find a feasible solution
Solver could not find a bounded solution
Which of the following error messages is displayed in Excel Solver when attempting to solve an infeasible problem?
Objective Cell values do not converge
Solver cannot improve the current solution. All constraints are satisfied
Solver could not find a feasible solution
Solver could not find a bounded solution
Which of the following is true of rounding the optimized solution of a linear program to an integer?
It always produces the most optimal integer solution
It may or may not be feasible
It does not affect the value of the objective function
It always produces a feasible solution
The importance of _________ for integer linear programming problems is often intensified by the fact that a small change in one of the coefficients in the constraints can cause a relatively large change in the value of the optimal solution.
sensitivity analysis
objective function
optimization analysis
decision variables
The imposition of an integer restriction is necessary for models where
variables can take negative values
the decision variables cannot take fractional values
nonnegativity constraints are needed
possible values of variables are restricted to particular intervals
The slack value for binding constraints is
equal to the sum of the optimal points in the solution
zero
always a positive integer
a negative integer
The points where constraints intersect on the boundary of the feasible region are termed as the
feasible points
feasible edges
extreme points
objective function contour
The change in the optimal objective function value per unit increase in the right-hand side of a constraint is given by the
allowable increase
shadow price
objective function coefficient
restrictive cost