Chapter 3: Problem 21
Determine graphically the solution set for each system of inequalities and indicate whether the solution set is bounded or unbounded. $$ \begin{array}{r} x-y \leq 0 \\ 2 x+3 y \geq 10 \end{array} $$
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Chapter 3: Problem 21
Determine graphically the solution set for each system of inequalities and indicate whether the solution set is bounded or unbounded. $$ \begin{array}{r} x-y \leq 0 \\ 2 x+3 y \geq 10 \end{array} $$
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Determine whether the statement is true or false. If it is true, explain why it is true. If it is false, give an example to show why it is false. The problem $$ \begin{aligned} \text { Minimize } & C=2 x+3 y \\ \text { subject to } & 2 x+3 y \leq 6 \\ & x-y=0 \\ & x \geq 0, y \geq 0 \end{aligned} $$ is a linear programming problem.
Kane Manufacturing has a division that produces two models of fireplace grates, model A and model B. To produce each model-A grate requires \(3 \mathrm{lb}\) of cast iron and 6 min of labor. To produce each model-B grate requires \(4 \mathrm{lb}\) of cast iron and \(3 \mathrm{~min}\) of labor. The profit for each model-A grate is \(\$ 2\), and the profit for each model-B grate is \(\$ 1.50 .1000 \mathrm{lb}\) of cast iron and 20 labor-hours are available for the production of grates each day. Because of an excess inventory of model-B grates, management has decided to limit the production of model-B grates to no more than 200 grates per day. How many grates of each model should the division produce daily to maximize Kane's profit? a. Use the method of corners to solve the problem. b. Find the range of values that the contribution to the profit of a model-A grate can assume without changing the optimal solution. c. Find the range of values that the resource for cast iron can assume without changing the optimal solution. d. Find the shadow price for the resource for cast iron. e. Identify the binding and nonbinding constraints.
Determine whether the statement is true or false. If it is true, explain why it is true. If it is false, give an example to show why it is false. An optimal solution of a linear programming problem is a feasible solution, but a feasible solution of a linear programming problem need not be an optimal solution.
Madison Finance has a total of \(\$ 20\) million earmarked for homeowner and auto loans. On the average, homeowner loans have a \(10 \%\) annual rate of return, whereas auto loans yield a \(12 \%\) annual rate of return. Management has also stipulated that the total amount of homeowner loans should be greater than or equal to 4 times the total amount of automobile loans. Determine the total amount of loans of each type that Madison should extend to each category in order to maximize its returns. What are the optimal returns?
A company manufactures two products, \(\mathrm{A}\) and \(\mathrm{B}\), on machines \(\mathrm{I}\) and \(\mathrm{II}\). The company will realize a profit of \(\$ 3 /\) unit of product \(A\) and a profit of \(\$ 4 /\) unit of product \(B\). Manufacturing 1 unit of product A requires 6 min on machine I and 5 min on machine II. Manufacturing 1 unit of product \(\mathrm{B}\) requires 9 min on machine I and 4 min on machine II. There are 5 hr of time available on machine I and \(3 \mathrm{hr}\) of time available on machine II in each work shift. a. How many units of each product should be produced in each shift to maximize the company's profit? b. Find the range of values that the contribution to the profit of 1 unit of product A can assume without changing the optimal solution. c. Find the range of values that the resource associated with the time constraint on machine I can assume. d. Find the shadow price for the resource associated with the time constraint on machine \(\mathrm{I}\),
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