1.
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When using a graphical solution procedure, the region bounded by the set of constraints is called the
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| solution |
| feasible region |
| infeasible region |
| maximum profit region |
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2.
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Which of the following is not a property of linear programming problems?
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| the presence of restrictions |
| optimization of some objective |
| usage of only linear equations and inequalities |
| all of the above are properties of linear programming |
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3.
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A feasible solution to a linear programming problem
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| 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 give the maximum possible profit |
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4.
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Consider the following linear programming problem: Maximize 12X + 10Y
Subject to 4X + 3Y < 480
2X + 3Y < 360
all variables > 0 The maximum possible profit for the objective function is
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| 1440 |
| 1520 |
| 1600 |
| 1800 |
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5.
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Consider the following linear programming problem: Maximize 12X + 10Y
Subject to 4X + 3Y < 480
2X + 3Y < 360
all variables > 0 Which of the following points (X,Y) is not feasible?
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| (0,100) |
| (100,10) |
| (70,70) |
| (20,90) |
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6.
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Consider the following linear programming problem: Maximize 4X + 10Y
Subject to 3X + 4Y < 480
4X + 2Y < 360
all variables > 0 The feasible corner points are (48,84), (0,120), (0,0), and (90,0). What is the maximum possible value for the objective function?
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| 360 |
| 1032 |
| 1200 |
| 1600 |
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7.
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Which of the following is not a property of all LP problems?
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| it seeks to maximize or minimize some quantity |
| constraints limit the degree to which we can pursue our objectives |
| there are alternative courses of action to follow |
| all of the above are properties of LP problems |
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8.
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Which of the following is not an example of an application of linear programming.
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| scheduling school buses to minimize distance traveled when carrying students |
| scheduling tellers at banks so that the needs are met during each hour of the day while minimizing the total cost of labor |
| picking blends of raw materials in feed mills to produce finished feed combinations at minimum cost. |
| all of the above are examples of LP applications |
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9.
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Which of the following is a mathematical expression in linear programming that maximizes or minimizes some quantity.
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| objective function |
| constraints |
| decision variables |
| all of the above |
