Linear Programming

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Question 1

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Linear programming techniques will always produce an optimal solution to an LP problem.

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Question 2

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LP problems must have a single goal or objective specified.

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Question 3

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Constraints limit the alternatives available to a decision maker

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Question 4

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Profit maximization could be an objective of an LP problem; but cost minimization cannot be the objective of an LP problem.

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Question 5

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The feasible solution space only contains points that satisfy all constraints.

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Question 6

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The equation 5x + 7y = 10 is linear.

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Question 7

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The equation 3xy = 9 is linear.

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Question 8

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Graphical linear programming can handle problems that involve any number of decision variables.

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Question 9

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An objective function represents a family of parallel lines.

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Question 10

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The term isoprofit line means that all points on the line will yield the same profit.

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Question 11

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The feasible solution space is the set of all feasible combinations of decision variables as defined by only binding constraints.

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Question 12

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The value of an objective function always decreases as it is moved away from the origin.

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Question 13

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A linear programming problem can have multiple optimal solutions.

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Question 14

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A maximization problem is limited by all greater than or equal to constraints.

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Question 15

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If a single optimal solution exists to a graphical LP problem, it will exist at a corner point.

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Question 16

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The simplex method is a general-purpose LP algorithm that can be used for solving only problems with more than six variables.

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Question 17

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A change in the value of an objective function coefficient does not change the optimal solution.

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Question 18

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The term range of feasibility refers to a constraint's right-hand-side quantity.

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Question 19

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A shadow price indicates how much a one-unit decrease/increase in the right-hand-side value of a constraint will decrease/increase the optimal value of the objective function.

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Question 20

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The term range of feasibility refers to coefficients of the objective function.

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