Nonzero slack-surplus is associated with binding constraint

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Reference no: EM132258463

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

True/False

2. A linear programming problem can have multiple optimal solutions.

True/False

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

True/ False

4. 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

5. An objective function represents a family of parallel lines.

True/False

6. Constraints limit the alternatives available to a decision maker.

True/False

7. Every change in the value of an objective function coefficient will lead to changes in the optimal solution.

True/False

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

True/False

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

True/False

10. In the range of feasibility, the value of the shadow price remains constant.

True/False

11. LP problems must have a single goal or objective specified.

True/False

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

True/False

13. Nonbinding constraints are not associated with the feasible solution space; i.e., they are redundant and can be eliminated from the matrix.

True/False

14. Nonzero slack or surplus is associated with a binding constraint.

True/False

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

True/False

16. The equation 3xy = 9 is linear.

True/False

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

True/False

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

True/False

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

True/False

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

True/False

Reference no: EM132258463

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