McqMate
These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Bachelor of Business Administration in Computer Applications (BBA [CA]) , Bachelor of Business Administration (BBA) .
Chapters
| 101. |
Operation research analysis does not |
| A. | predict future operation |
| B. | build more than one model |
| C. | collect the relevant data |
| D. | recommended decision and accept |
| Answer» A. predict future operation | |
| 102. |
A constraint in an LP model restricts |
| A. | value of the objective function |
| B. | value of the decision variable |
| C. | use of the available resourses |
| D. | all of the above |
| Answer» D. all of the above | |
| 103. |
A feasible solution of LPP |
| A. | must satisfy all the constraints simultaneously |
| B. | need not satisfy all the constraints, only some of them |
| C. | must be a corner point of the feasible region |
| D. | all of the above |
| Answer» A. must satisfy all the constraints simultaneously | |
| 104. |
Maximization of objective function in LPP means |
| A. | value occurs at allowable set decision |
| B. | highest value is chosen among allowable decision |
| C. | none of the above |
| D. | all of the above |
| Answer» B. highest value is chosen among allowable decision | |
| 105. |
Alternative solution exist in a linear programming problem when |
| A. | one of the constraint is redundant |
| B. | objective function is parallel to one of the constraints |
| C. | two constraints are parallel |
| D. | all of the above |
| Answer» D. all of the above | |
| 106. |
The linear function of the variables which is to be maximize or minimize is called |
| A. | constraints |
| B. | objective function |
| C. | decision variable |
| D. | none of the above |
| Answer» B. objective function | |
| 107. |
The true statement for the graph of inequations 3x+2y≤6 and 6x+4y≥20 , is |
| A. | both graphs are disjoint |
| B. | both do not contain origin |
| C. | both contain point (1, 1) |
| D. | none of these |
| Answer» A. both graphs are disjoint | |
| 108. |
The value of objective function is maximum under linear constraints |
| A. | at the center of feasible region |
| B. | at (0,0) |
| C. | at any vertex of feasible region |
| D. | the vertex which is at maximum distance from (0, 0) |
| Answer» C. at any vertex of feasible region | |
| 109. |
A model is |
| A. | an essence of reality |
| B. | an approximation |
| C. | an idealization |
| D. | all of the above |
| Answer» D. all of the above | |
| 110. |
The first step in formulating a linear programming problem is |
| A. | identify any upper or lower bound on the decision variables |
| B. | state the constraints as linear combinations of the decision variables |
| C. | understand the problem |
| D. | identify the decision variables |
| Answer» D. identify the decision variables | |
| 111. |
Constraints in an LP model represents |
| A. | limititations |
| B. | requirements |
| C. | balancing, limitations and requirements |
| D. | all of above |
| Answer» D. all of above | |
| 112. |
The best use of linear programming is to find optimal use of |
| A. | money |
| B. | manpower |
| C. | machine |
| D. | all the above |
| Answer» D. all the above | |
| 113. |
Which of the following is assumption of an LP model |
| A. | divisibility |
| B. | proportionality |
| C. | additivity |
| D. | all of the above |
| Answer» D. all of the above | |
| 114. |
Before formulating a formal LP model, it is better to |
| A. | express each constraints in words |
| B. | express the objective function in words |
| C. | verbally identify decision variables |
| D. | all of the above |
| Answer» D. all of the above | |
| 115. |
Non-negative condition in an LP model implies |
| A. | a positive coefficient of variables in objective function |
| B. | a positive coefficient of variables in any constraint |
| C. | non-negative value of resourse |
| D. | none of the above |
| Answer» C. non-negative value of resourse | |
| 116. |
The set of decision variable which satisfies all the constraints of the LPP is called as----- |
| A. | solution |
| B. | basic solution |
| C. | feasible solution |
| D. | none of the above |
| Answer» C. feasible solution | |
| 117. |
The intermediate solutions of constraints must be checked by substituting them back into |
| A. | objective function |
| B. | constraint equations |
| C. | not required |
| D. | none of the above |
| Answer» B. constraint equations | |
| 118. |
A basic solution is called non-degenerate, if |
| A. | all the basic variables are zero |
| B. | none of the basic variables is zero |
| C. | at least one of the basic variables is zero |
| D. | none of these |
| Answer» B. none of the basic variables is zero | |
| 119. |
The graph of x≤2 and y≥2 will be situated in the |
| A. | first and second quadrant |
| B. | second and third quadrant |
| C. | first and third quadrant |
| D. | third and fourth quadrant |
| Answer» B. second and third quadrant | |
| 120. |
A solution which satisfies non-negative conditions also is called as----- |
| A. | solution |
| B. | basic solution |
| C. | feasible solution |
| D. | none of the above |
| Answer» C. feasible solution | |
| 121. |
A solution which optimizes the objective function is called as ------ |
| A. | solution |
| B. | basic solution |
| C. | feasible solution |
| D. | optimal solution |
| Answer» D. optimal solution | |
| 122. |
In. L.P.P---- |
| A. | objective function is linear |
| B. | constraints are linear |
| C. | both objective function and constraints are linear |
| D. | none of the above |
| Answer» C. both objective function and constraints are linear | |
| 123. |
If the constraints in a linear programming problem are changed |
| A. | the problem is to be re-evaluated |
| B. | solution is not defined |
| C. | the objective function has to be modified |
| D. | the change in constraints is ignored. |
| Answer» A. the problem is to be re-evaluated | |
| 124. |
Linear programming is a |
| A. | constrained optimization technique |
| B. | technique for economic allocation of limited resources |
| C. | mathematical technique |
| D. | all of the above |
| Answer» D. all of the above | |
| 125. |
A constraint in an LP model restricts |
| A. | value of objective function |
| B. | value of a decision variable |
| C. | use of the available resources |
| D. | all of the above |
| Answer» D. all of the above | |
| 126. |
The distinguishing feature of an LP model is |
| A. | relationship among all variables is linear |
| B. | it has single objective function & constraints |
| C. | value of decision variables is non-negative |
| D. | all of the above |
| Answer» A. relationship among all variables is linear | |
| 127. |
The best use of linear programming technique is to find an optimal use of |
| A. | money |
| B. | manpower |
| C. | machine |
| D. | all of the above |
| Answer» D. all of the above | |
| 128. |
Which of the following is not a characteristic of the LP |
| A. | resources must be limited |
| B. | only one objective function |
| C. | parameters value remains constant during the planning period |
| D. | the problem must be of minimization type |
| Answer» D. the problem must be of minimization type | |
| 129. |
Which of the following is an assumption of an LP model |
| A. | divisibility |
| B. | proportionality |
| C. | additivity |
| D. | all of the above |
| Answer» D. all of the above | |
| 130. |
Which of the following is a limitation associated with an LP model |
| A. | the relationship among decision variables in linear |
| B. | no guarantee to get integer valued solutions |
| C. | no consideration of effect of time & uncertainty on lp model |
| D. | all of the above |
| Answer» D. all of the above | |
| 131. |
The graphical method of LP problem uses |
| A. | objective function equation |
| B. | constraint equations |
| C. | linear equations |
| D. | all of the above |
| Answer» D. all of the above | |
| 132. |
A feasible solution to an LP problem |
| A. | must satisfy all of the problem’s constraints simultaneously |
| B. | need not satisfy all of the constraints, only some of them |
| C. | must be a corner point of the feasible region |
| D. | must optimize the value of the objective function |
| Answer» D. must optimize the value of the objective function | |
| 133. |
An iso-profit line represents |
| A. | an infinite number of solutions all of which yield the same profit |
| B. | an infinite number of solution all of which yield the same cost |
| C. | an infinite number of optimal solutions |
| D. | a boundary of the feasible region |
| Answer» D. a boundary of the feasible region | |
| 134. |
If an iso-profit line yielding the optimal solution coincides with a constaint line, then |
| A. | the solution is unbounded |
| B. | the solution is infeasible |
| C. | the constraint which coincides is redundant |
| D. | none of the above |
| Answer» A. the solution is unbounded | |
| 135. |
A constraint in an LP model becomes redundant because |
| A. | two iso-profit line may be parallel to each other |
| B. | the solution is unbounded |
| C. | this constraint is not satisfied by the solution values |
| D. | none of the above |
| Answer» A. two iso-profit line may be parallel to each other | |
| 136. |
Constraints in LP problem are called active if they |
| A. | represent optimal solution |
| B. | at optimality do not consume all the available resources |
| C. | both a & b |
| D. | none of the above |
| Answer» A. represent optimal solution | |
| 137. |
Mathematical model of Linear Programming is important because |
| A. | It helps in converting the verbal description and numerical data into mathematical expression |
| B. | decision makers prefer to work with formal models. |
| C. | it captures the relevant relationship among decision factors. |
| D. | it enables the use of algebraic techniques. |
| Answer» A. It helps in converting the verbal description and numerical data into mathematical expression | |
| 138. |
In graphical method of linear programming problem if the iOS-cost line coincide with a side of region of basic feasible solutions we get |
| A. | Unique optimum solution |
| B. | unbounded optimum solution |
| C. | no feasible solution |
| D. | Infinite number of optimum solutions |
| Answer» D. Infinite number of optimum solutions | |
| 139. |
If the value of the objective function 𝒛 can be increased or decreased indefinitely, such solution is called |
| A. | Bounded solution |
| B. | Unbounded solution |
| C. | Solution |
| D. | None of the above |
| Answer» B. Unbounded solution | |
| 140. |
For the constraint of a linear optimizing function z=x1+x2 given by x1+x2≤1, 3x1+x2≥3 and x1, x2≥0 |
| A. | There are two feasible regions |
| B. | There are infinite feasible regions |
| C. | There is no feasible region |
| D. | None of these |
| Answer» C. There is no feasible region | |
| 141. |
If the number of available constraints is 3 and the number of parameters to be optimized is 4, then |
| A. | The objective function can be optimized |
| B. | The constraints are short in number |
| C. | The solution is problem oriented |
| D. | None of these |
| Answer» B. The constraints are short in number | |
| 142. |
Non-negativity condition is an important component of LP model because |
| A. | Variables value should remain under the control of the decision-maker |
| B. | Value of variables make sense & correspond to real-world problems |
| C. | Variables are interrelated in terms of limited resources |
| D. | None of the above |
| Answer» B. Value of variables make sense & correspond to real-world problems | |
| 143. |
Maximization of objective function in an LP model means |
| A. | Value occurs at allowable set of decisions |
| B. | Highest value is chosen among allowable decisions |
| C. | Neither of above |
| D. | Both a & b |
| Answer» D. Both a & b | |
| 144. |
Which of the following is not a characteristic of the LP model |
| A. | Alternative courses of action |
| B. | An objective function of maximization type |
| C. | Limited amount of resources |
| D. | Non-negativity condition on the value of decision variables. |
| Answer» A. Alternative courses of action | |
| 145. |
Which of the following statements is true with respect to the optimal solution of an LP problem |
| A. | Every LP problem has an optimal solution |
| B. | Optimal solution of an LP problem always occurs at an extreme point |
| C. | At optimal solution all resources are completely used |
| D. | If an optimal solution exists, there will always be at least one at a corner |
| Answer» A. Every LP problem has an optimal solution | |
| 146. |
While plotting constraints on a graph paper, terminal points on both the axes are connected by a straight line because |
| A. | The resources are limited in supply |
| B. | The objective function as a linear function |
| C. | The constraints are linear equations or inequalities |
| D. | All of the above |
| Answer» D. All of the above | |
| 147. |
In graphical method of linear programming problem if the iOS-cost line coincide with a side of region of basic feasible solutions we get |
| A. | unique optimum solution |
| B. | unbounded optimum solution |
| C. | no feasible solution |
| D. | infinite number of optimum solutions |
| Answer» D. infinite number of optimum solutions | |
| 148. |
The objective function for a L.P model is 3𝑥1 + 2𝑥2, if 𝑥1 = 20 and 𝑥2 = 30, what is the value of the objective function? |
| A. | 0 |
| B. | 50 |
| C. | 60 |
| D. | 120 |
| Answer» D. 120 | |
| 149. |
If the value of the objective function 𝒛 can be increased or decreased indefinitely, such solution is called |
| A. | bounded solution |
| B. | unbounded solution |
| C. | solution |
| D. | none of the above |
| Answer» B. unbounded solution | |
| 150. |
For the constraint of a linear optimizing function z=x1+x2 given by x1+x2≤1, 3x1+x2≥3 and x1, x2≥0 |
| A. | there are two feasible regions |
| B. | there are infinite feasible regions |
| C. | there is no feasible region |
| D. | none of these |
| Answer» C. there is no feasible region | |
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