QBA Midterm 1 T/F & MCQ
The cost that varies depending on the values of the decision variables is a
Relevant cost
A resource cost is a __________ cost if the amount paid for it is dependent upon the amount of the resource used by the decision variables
Relevant cost; reflected in the objective function coefficients
A variable added to the left-hand side of a less-than-or-equal-to constraint to convert the constraint into an equality is
Slack Variable
A range of optimality is applicable only if the other coefficient remains at its original value
True
In a feasible problem, an equal-to constraint cannot be nonbinding
True
In a linear programming problem, the objective function and the constraints must be linear functions of the decision variables
True
In the graphical method, if the objective function line is parallel to a boundary constaint in the direction of optimization, there are alternate optimal solutions, with all points on this line segment being optimal.
True
No matter what value it has, each objective function line is parallel to every other objective function line in a problem
True
As long as the slope of the OF stays between the slopes of the binding constraints,
the values of the dual variables won't change.
A redundant constraint is a binding constraint
False
Alternative optimal solutions occur when there is no feasible solution to the problem
False
An infeasible problem is one in which the objective function can be increased to infinity
False
Because surplus variables represent the amount by which the solution exceeds a minimum target, they are given positive coefficients in the objective function
False
Decreasing the OF coefficient of a variable to its lower limit will create a revised problem that is unbounded
False
It is possible to have exactly two optimal solutions to a linear programming problem
False
Only binding constraints form the shape (boundaires) of the feasible region
False
Slack and surplus variables have objective function coefficients equal to 1
False
Sunk Cost = min amount company should pay per unit of resource
False
When the RHS of two constraints are each increased by one unit, the OF value will be adjsuted by the sum of the constraints' dual (shadow) prices
False
In what part(s) of a linear programming formulation would the decision variables be stated?
LHS
A redundant constraint results in
No change in the optimal solution(s)
24. Which of the following special cases does not require reformulation of the problem in order to obtain a solution? a. alternate optimality b. infeasibility c. unboundedness d. each case requires a reformulation.
A
Which of the following is a valid objective function for a linear programming problem? a. Max 5xy b. Min 4x + 3y + (2/3)z c. Max 5x2 + 6y2 d. Min (x1 + x2)/x3
B
All linear programming problems have all of the following properties EXCEPT a. a linear objective function that is to be maximized or minimized. b. a set of linear constraints. c. alternative optimal solutions. d. variables that are all restricted to nonnegative values.
C
The decision variables of a model are also knowns as the
Controllable inputs
The three assumptions necessary for a linear programming model to be appropriate include all of the following except a. proportionality b. additivity c. divisibility d. normality
D
The improvement in the value of the objective function per unit increase in a right-hand side is the
Dual Price
A resource cost is a __________ cost if it must be paid regardless of teh amount of the resource actually used by the decision variables
Sunk cost; not reflected in the objective function coefficients
The first step in problem solving is
The identification of a difference between the actual and the desired state of affairs--what's the problem?
A feasible region may be unbounded and yet there may be optimal solutions
True
Relevant cost = Max premium company should pay over normal cost
True
Sunk Cost = MAX amount company should pay per unit of resource
True
The reduced cost for a decision variable whose value is > 0 in the optimal solution is 0
True
Infeasibility means that the number of solutions to the linear programming models that satisfies all constraints is
Zero.
