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Operations Management Study Set 5
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Operations Management Study Set 5
Statistics
Quiz 19 :
Linear Programming
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Quiz 19 :
Linear Programming
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Linear programming techniques will always produce an optimal solution to an LP problem.
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True False
Q01
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Answer:
False
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A maximization problem is limited by all greater than or equal to constraints.
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True False
Q02
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Answer:
False
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The equation 3 xy = 9 is linear.
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True False
Q03
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Answer:
False
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The term range of feasibility refers to a constraint's right-hand-side quantity.
True False
Q04
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Constraints limit the alternatives available to a decision maker
True False
Q05
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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.
True False
Q06
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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.
True False
Q07
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The feasible solution space only contains points that satisfy all constraints.
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Q08
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An objective function represents a family of parallel lines.
True False
Q09
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The term range of feasibility refers to coefficients of the objective function.
True False
Q10
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If a single optimal solution exists to a graphical LP problem, it will exist at a corner point.
True False
Q11
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The value of an objective function always decreases as it is moved away from the origin.
True False
Q12
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The equation 5 x + 7 y = 10 is linear.
True False
Q13
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A linear programming problem can have multiple optimal solutions.
True False
Q14
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The term isoprofit line means that all points on the line will yield the same profit.
True False
Q15
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LP problems must have a single goal or objective specified.
True False
Q16
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The feasible solution space is the set of all feasible combinations of decision variables as defined by only binding constraints.
True False
Q17
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Graphical linear programming can handle problems that involve any number of decision variables.
True False
Q18
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A change in the value of an objective function coefficient does not change the optimal solution.
True False
Q19
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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.
True False
Q20
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