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True 19 only binding constraints form the shape
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19. Only binding constraints form the shape (boundaries) of the feasible region.
FALSE
20. The constraint 5x1 - 2x2 £ 0 passes through the point (20, 50).
TRUE
21. A redundant constraint is a binding constraint.
FALSE
22. Because surplus variables represent the amount by which the solution exceeds a minimum targ
given positive coefficients in the objective function.
FALSE
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FALSE
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23. Alternative optimal solutions occur when there is no feasible solution to the problem.
FALSE
24. A range of optimality is applicable only if the other coefficient remains at its original value.
TRUE
25. Because the dual price represents the improvement in the value of the optimal solution per uni
right-hand-side, a dual price cannot be negative.
FALSE
26. Decision variables limit the degree to which the objective in a linear programming problem is
FALSE
27. No matter what value it has, each objective function line is parallel to every other objective fu
a problem.
TRUE
28. The point (3, 2) is feasible for the constraint 2x1 + 6x2 £ 30.
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TRUE
TRUE
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29. The constraint 2x1 - x 2 = 0 passes through the point (200,100).
FALSE
30. The standard form of a linear programming problem will have the same solution as the origina
TRUE
31. An optimal solution to a linear programming problem can be found at an extreme point of the
region for the problem.
TRUE
32. An unbounded feasible region might not result in an unbounded solution for a minimization o
maximization problem.
TRUE
33. An infeasible problem is one in which the objective function can be increased to infinity.
FALSE
34. A linear programming problem can be both unbounded and infeasible.
FALSE
35. It is possible to have exactly two optimal solutions to a linear programming problem.
FALSE
36. Explain the difference between profit and contribution in an objective function. Why is it imp
decision maker to know which of these the objective function coefficients represent?
Answer not provided.
37. Explain how to graph the line x1 - 2x2 ³ 0.
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38. Create a linear programming problem with two decision variables and three constraints that w
both a slack and a surplus variable in standard form. Write your problem in standard form.
Answer not provided.
39. Explain what to look for in problems that are infeasible or unbounded.
Answer not provided.
40. Use a graph to illustrate why a change in an objective function coefficient does not necessarily
change in the optimal values of the decision variables, but a change in the right-hand sides of a bi
constraint does lead to new values.
Answer not provided.
41. Explain the concepts of proportionality, additivity, and divisibility.
Answer not provided.
42. Explain the steps necessary to put a linear program in standard form.
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TERM
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Fall '12
Dr.VinhQuan
TAGS
Operations Research, Linear Programming, Optimization, Linear Programming Problem, Muir
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