Constraints may represent
Limitations
Requirements
Balance conditions
All of the above
An iso-profit line represents:
An infinite number of solutions all of which yield the same profit
An infinite number of solutions all of which yield the same costs
None of 1 and 2
Every corner of the feasible region is defined by
The inter-section of 2 constraint lines
Some subset of constraint lines and non-negativity conditions
Neither of the above
The graphical method is useful because
It provides a general way to solve LP problems
It gives geometric insight into the model and the meaning of optimality
Both 1 and 2
An unbound feasible region
Arises from an incorrect formulation
Means the objective function is unbounded
Neither of the above
Both 1 and 2
Consider an optimal solution to an LP. Which of the following must be true?
At least on constraint (not including non-negativity conditions) is active at the point (b) Exactly one constraint (not including non-negativity conditions) is active at the point
Neither of the above
All of the above
The phrase “unbounded LP” means that
At least one decision variable can be made arbitrarily large without leaving the feasible
Objective contours can be moved as far as desired, in the optimizing direction still touch at least one point in the constraint set.
A constraint limit the values that
The objective function can assume
The decision variables can assume
Neither of the above
Both 1 and 2
Linear programming is
A constrained optimization model
A constrained decision-making model
A mathematical programming model
All of the above
Model formulation is important because
It enables us to use algebraic techniques
In a business context, most managers prefer to work with formal models
It forces management to address a clearly defined problem
It allows the manager to better communicate with the management scientist and therefore to be more discriminating in hiring policies
The non-negativity requirement is included in an LP because
It makes the model easier to solve
It makes the model correspond more closely to the real-world problem
Both 1 and 2
Neither of the above
The distinguishing features of an LP (as opposed to more mathematical programming models) is
The problem has an objective function and constraints
All functions in the problem are linear
Optimal values for the decision variables are produced
All of the above
In an LP maximization model
The objective function is maximized
The objective function is maximized and then it is determined whether or not this occurs at an allowable decision
The objective function is maximized over the allowable set of decisions
None of the above
All variables in the solution of a linear programming problem are either positive or zero because of the existence of:
An objective function
Structural constraints
Limited resources
None of the above
Which of the following is not a major requirement of a linear programming problem?
Their must be alternative courses of action among which to decide
An objective for the firm must exist
The problem must be of the maximization type
Resources must be limited
Which of the following assertions is true of an optimal solution to an LP?
Every LP has an optimal solution
The optimal solution uses all resources
If an optimal solution exists, there will always be at least one of a corner
The optimal solution always occurs at an extreme point
All of the above
An Iso-profit contour represents
An infinite number of feasible points, all of which yield the same profit
An infinite number of optimal solution
An infinite number of decisions, all of which yields the same profit
Non of these
Mathematical model of LP problem is important because
It helps in converting the verbal description and numerical data in to mathematical expression
Decision-makers prefer to work with formal models
It captures the relevant relationship among decision factors
It enables the use of algebraic technique
The distinguishing feature of an LP model is
Relationship among all variables is linear
It has single objective function and constraint
Value of decision variables is non-negative
All of the above
Before formulating a formal LP Model, it is better to
Express each constraint in words
Express the objective function in words
Decision variables are identified verbally
All of the above
Each constraint in an LP model is expressed as an
Inequality with = sign
Inequality with = sign
Equation with = sign
None of the above
Maximization of objective function in LP model means
Value occurs at allowable set of decisions
Highest value is chosen among allowable decision
Neither of the above
Both 1 and 2
Which of the following is not a characteristic of LP
Alternative courses of action
An objective function of maximization type
Limited amount of resources
Non-negativity condition on the value of decision variables
The graphical method of LP problem uses
Objective equation
Constraint equations
Linear equations
All of the above
A Feasible solution to an LP problem
Must satisfy all of the problem’s constraints simultaneously
Need not satisfy all of the constraints, only some of them
Must be a corner point of the feasible region
Must optimize the value of the objective function
Which of the following statements is true with respect to the optimal solution of an LP problem
Every LP problem has an optimal solution
Optimal solution of an LP problem always occurs at an extreme point
A optimal solution all resources are used completely
If an optimal solution exists, there will always be at least once at a corner
If an Iso-profit line yielding the optimal solution coincides with a constraint line, then
The solution is unbounded
The solution is infeasible
The constraint, which coincides, is redundant
None of the above
While plotting constraints on a graph paper, terminal points on both the axes are connected by a straight line because
The resources are limited in supply
The objective is a linear function
The constraints are ‘linear equations or inequalities’
All of the above
A constraint in an LP model becomes redundant because
Two Iso-profit lines may be parallel to each other
The solution is unbounded
This constraint is not satisfied by the solution values
None of the above
If two constraints do not intersect in the positive quadrant of the graph; then
The problem is infeasible
The solution is unbounded
One of the constraints is redundant
None of the above
Constraints in LP problem are called active if they
None of the above
Represent optimal solution
At optimality do not consume all the available resources
Both of 1 and 2
The solution space (region) of an LP problem is unbounded due to
An incorrect formulation of the LP model
Objective function is unbounded
Neither 1 nor 2
Both 1 and 2