linear programming
Question
Question 1.1.
Consider the following linear programming problem:
Maximize 12X + 10Y
Subject to: 4X + 3Y ≤ 480
2X + 3Y ≤ 360
All variables ≥ 0
What is the optimum solution of (X,Y)?
(Points : 5)
X=480, Y = 360
X=360, Y-480
X=60, Y=80
X=80, Y=60
None of the above
Question 2.2.
When appropriate, the optimal solution to a maximization linear programming problem can be found by graphing the feasible region and
(Points : 5)
finding the profit at every corner point of the feasible region to see which one gives the highest value.
moving the isoprofit lines towards the origin in a parallel fashion until the last point in the feasible region is encountered.
locating the point that is highest on the graph.
None of the above
All of the above
Question 3.3.
An LP formulation typically requires finding the maximum value of an objective while simultaneously maximizing usage of the resource constraints.
(Points : 5)
True
False
Question 4.4.
Infeasibility in a linear programming problem occurs when
(Points : 5)
there is an infinite solution.
a constraint is redundant.
more than one solution is optimal.
the feasible region is unbounded.
there is no solution that satisfies all the constraints given.
Question 5.5.
If the addition of a constraint to a linear programming problem does not change the solution, the constraint is said to be
(Points : 5)
unbounded.
non-negative.
infeasible.
redundant.
bounded.
Question 6.6.
Solve the following linear programming problem:
Maximize 10X + 1Y
Subject to: 4X + 3Y ≤ 36
2X + 4Y ≤ 40
Y ? 3
What is the maximum profit?
(Points : 5)
36
40
3
6.75
70.5
Question 7.7.
In the term linear programming, the word programming comes from the phrase “computer programming.”
(Points : 5)
True
False
Question 8.8.
The difference between the left-hand side and right-hand side of a greater-than-or-equal-to constraint is referred to as
(Points : 5)
surplus.
constraint.
slack.
shadow price.
None of the above
Question 9.9.
Consider the following linear programming:
Maximize 10X + 1Y
Subject to: 4X + 3Y ≤ 36
2X + 4Y ≤ 40
Y ≤ 3
What is the value of X and Y maximizing profit?
(Points : 5)
X = 36, Y = 40
X = 40, Y = 36
X = 70.5, Y = 6.75
X = 6.75, Y = 3
X = 3, Y = 6.75
Question 10.10.
The difference between the left-hand side and right-hand side of a less-than-or-equal-to constraint is referred to as
(Points : 5)
surplus.
constraint.
slack.
slack.
shadow price.
None of the above
Question 11.11.
Which of the following is not an assumption of LP?
(Points : 5)
simultaneity
certainty
proportionality
divisibility
additivity
Question 12.12.
A feasible solution to a linear programming problem
(Points : 5)
must be a corner point of the feasible region.
must satisfy all of the problem’s constraints simultaneously.
need not satisfy all of the constraints, only the non-negativity constraints.
must give the maximum possible profit.
must give the minimum possible cost.
Question 13.13.
Sensitivity analysis may also be called
(Points : 5)
postoptimality analysis.
parametric programming.
optimality analysis.
All of the above
None of the above
Question 14.14.
An objective function is necessary in a maximization problem but is not required in a minimization problem.
(Points : 5)
True
False
Question 15.15.
Management resources that need control include machinery usage, labor volume, money spent, time used, warehouse space used, and material usage.
(Points : 5)
True
False
Question 16.16.
A constraint with zero slack or surplus is called a
(Points : 5)
nonbinding constraint.
resource constraint.
binding constraint.
nonlinear constraint.
linear constraint.
Question 17.17.
The set of solution points that satisfies all of a linear programming problem’s constraints simultaneously is defined as the feasible region in graphical linear programming.
(Points : 5)
True
False
Question 18.18.Sensitivity analysis enables us to look at the effects of changing the coefficients in the objective function, one at a time.(Points : 5)
True
False
Question 19.19.
Consider the following linear programming problem:
Maximize 12X + 10X
Subject to: 4X + 3Y ? 480
2X + 3Y ? 360
All variables ? 0
The maximum possible value for the objective function is
(Points : 5)
360.
480.
1520.
1560.
None of the above
Question 20.20.
In a maximization problem, when one or more of the solution variables and the profit can be made infinitely large without violating any constraints, the linear program has
(Points : 5)
an infeasible solution.
an unbounded solution.
a redundant constraint.
alternate optimal solutions.
None of the above
