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| 1 | |
Sensitivity analysis answers "what if" questions to help the decision maker. |
| | A) | True |
| | B) | False |
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| 2 | |
In a two variable graphical linear program, if the coefficient of one of the variables in the objective function is changed (while the other remains fixed), then slope of the objective function expression will change. |
| | A) | True |
| | B) | False |
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| 3 | |
In a two variable graphical linear program, if the RHS of one of the constraints is changed (keeping all other things fixed) then the plot of the corresponding constraint will move in parallel to its old plot. |
| | A) | True |
| | B) | False |
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| 4 | |
The value of 0 will always be included in any range produced by sensitivity analysis. |
| | A) | True |
| | B) | False |
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| 5 | |
The value of ∞ will always be included in any range produced by sensitivity analysis. |
| | A) | True |
| | B) | False |
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| 6 | |
Shadow price of a resource corresponding to a binding constraint may be positive. |
| | A) | True |
| | B) | False |
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| 7 | |
In a two variable linear programming problem a nonbinding constraint cannot become a binding constraint even of the RHS of the nonbinding constraint is changed dramatically. |
| | A) | True |
| | B) | False |
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| 8 | |
Dual of a linear programming problem with maximize objective function, all ≤ constraints and non-negative variables has minimize objective function, all ≥ constraints and non-negative decision variables. |
| | A) | True |
| | B) | False |
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| 9 | |
Given the following linear programming problem with two non-negative variables X1 and X2 and 3 constraints all of which are ≤ type, and a maximize objective function and assuming Yi, i=1,2,3 as the dual variables associated with constraints 1,2 and 3 respectively, one of the constraints in the dual problem is:
Max: 250X1 + 500X2
Constraints:
X1 ≤ 320
2X1 + 5X2 ≤ 1100
1X1 + 1.2X2 ≤ 480
Variables are non-negative. |
| | A) | 0Y1 + 2Y2 + 1Y3 ≥ 250 |
| | B) | 1Y1 + 2Y2 + 1Y3 ≥ 250 |
| | C) | 1Y1 + 2Y2 + 1Y3 ≥ 320 |
| | D) | 0Y1 + 2Y2 + 1Y3 ≥ 500 |
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| 10 | |
Given the following linear programming problem with two non-negative variables X1 and X2 and 3 constraints all of which are ≤ type, and a maximize objective function and assuming Yi, i=1,2,3 as the dual variables associated with constraints 1,2 and 3 respectively, the objective function of the dual problem is:
Max: 250X1 + 500X2
Constraints:
X1 ≤ 320
2X1 + 5X2 ≤ 1100
1X1 + 1.2X2 ≤ 480
Variables are non-negative. |
| | A) | Max: 0Y1 + 2Y2 + 1Y3 |
| | B) | Min: 1Y1 + 2Y2 + 1Y3 |
| | C) | Max: 320Y1 + 1100Y2 + 480Y3 |
| | D) | Min: 320Y1 + 1100Y2 + 480Y3 |
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| 11 | |
Given the following linear programming problem with two non-negative variables X1 and X2 and 3 constraints all of which are ≤ type, and a maximize objective function and assuming Yi, i=1,2,3 as the dual variables associated with constraints 1,2 and 3 respectively, the variables of the dual problem are required to be:
Max: 250X1 + 500X2
Constraints:
X1 ≤ 320
2X1 + 5X2 ≤ 1100
1X1 + 1.2X2 ≤ 480
Variables are non-negative. |
| | A) | Strictly negative. |
| | B) | non-negative. |
| | C) | strictly positive. |
| | D) | Non-positive. |
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| 12 | |
Given the following linear programming problem with two non-negative variables X1 and X2 find the range of values for the objective function coefficient of X2 that will leave the current solution optimal (that is range of optimality or range of insignificance as the case may be) (Hint: both constraints are binding.):
Max: 100X1 + 200X2
Constraints:
2X1 + 5X2 ≤ 104
15X1 + 3X2 ≤ 90
Variables are non-negative. |
| | A) | [20,250] |
| | B) | [-∞,250] |
| | C) | [0,250] |
| | D) | [20,∞] |
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