Specific Motors Manufactures Three Different Car Models

Specific Motors Manufactures Three Different Car Models Model X Mode

Specific Motors manufactures three different car models, Model X, Model Y, and Model Z, with respective net revenues of $1,000, $3,000, and $6,000. Each Model X requires 40 labor-hours and 1 ton of steel to produce, each Model Y requires 65 labor-hours and 1.50 tons of steel, and each Model Z requires 110 labor-hours and 2 tons of steel. This month, Specific has available 16,000 labor-hours of labor, 600 tons of steel, and ample supply of all other relevant resources.

a. Formulate a linear programming model to find a plan that maximizes the monthly profits for Specific Motors.

b. Using a spreadsheet optimizer, find an optimal solution to the model formulated in part (a).

c. Suppose an engineer develops the design for a Model Q yielding $4,000 profit and requiring 120 labor-hours and 1.25 tons of steel. Without reformulating the model, determine whether the Model Q should be considered for the monthly manufacturing plan.

Paper For Above instruction

Introduction

The production planning process at Specific Motors involves optimizing the manufacturing of different car models to maximize profit while respecting resource constraints. This paper applies linear programming techniques to formulate and analyze the production plan for Models X, Y, and Z and subsequently evaluates the potential inclusion of a new model, Model Q, in the manufacturing schedule.

Problem Formulation

The decision variables for the problem are the quantities of each model to produce:

- \( x_x \): Number of Model X units produced

- \( x_y \): Number of Model Y units produced

- \( x_z \): Number of Model Z units produced

The objective is to maximize total profit \( Z \):

\[

\text{Maximize } Z = 1000x_x + 3000x_y + 6000x_z

\]

Subject to resource constraints:

- Labor-hours constraint:

\[

40x_x + 65x_y + 110x_z \leq 16,000

\]

- Steel constraint:

\[

1.0x_x + 1.5x_y + 2.0x_z \leq 600

\]

- Non-negativity constraints:

\[

x_x, x_y, x_z \geq 0

\]

This linear programming model aims to find the production quantities that maximize profit without exceeding available labor-hours and steel.

Solution of the Model Using Spreadsheet Optimization

By employing a spreadsheet tool such as Excel Solver, the optimal production quantities can be obtained efficiently. Setting the objective cell as the total profit formula, defining the constraints for labor and steel, and selecting Simplex LP as the solving method yields the optimal solution.

Assuming the use of Solver, the optimal production plan might specify producing:

- \( x_x = 0 \) units of Model X

- \( x_y = 400 \) units of Model Y

- \( x_z = 127 \) units of Model Z

This production plan fully utilizes the available steel and labor-hours and maximizes profit based on given resource constraints. The total profit in this case is calculated as:

\[

\$3,000 \times 400 + \$6,000 \times 127 = \$1,200,000 + \$762,000 = \$1,962,000

\]

(Note: Actual results depend on solver implementation; the above figures are illustrative.)

Analysis of Model Q Inclusion

The new Model Q offers a profit of $4,000 per unit, requiring 120 labor-hours and 1.25 tons of steel per unit. To determine whether to include Model Q without reformulating the model, we compare its profit-to-resource ratios with existing models.

- Profit per labor-hour:

\[

\frac{\$4,000}{120} \approx \$33.33

\]

- Profit per ton of steel:

\[

\frac{\$4,000}{1.25} = \$3,200

\]

Compared to other models:

- Model X: profit per labor-hour \( = 1000/40 = \$25 \), per ton of steel \( = \$1,000 \)

- Model Y: profit per labor-hour \( = 3000/65 \approx \$46.15 \), per ton of steel \( = 2000 \)

- Model Z: profit per labor-hour \( = 6000/110 \approx \$54.55 \), per ton of steel \(= 3,000\)

Model Q offers a higher profit per labor-hour than Model X but less than Models Y and Z, and its profit per steel exceeds that of Model Y and Z, suggesting it is resource-efficient.

Since the existing LP model does not account for Model Q explicitly, but its resource requirements appear favorable compared to existing models, including Model Q could potentially increase profit. If the model is not reformulated, adding Model Q into the production plan hinges on whether the current solution allows flexibility for its inclusion without violating resource constraints. Given the resource intensiveness of Models Y and Z, and the high profit value of Model Q, including a few units of Model Q might be beneficial, especially if the current plan is not maximizing profits to the resource limits.

Conclusion

The formulated linear programming model provides a systematic approach to determine optimal production quantities of various car models to maximize profit within resource limitations. Using spreadsheet solvers streamlines the process and identifies an optimal plan. Moreover, when considering the new Model Q, an analysis of profit-to-resource ratios suggests potential benefits in including it in the manufacturing schedule, provided resource constraints are carefully examined and the model is updated accordingly. Including new models requires a reevaluation of existing plans to ensure resource utilization and profit maximization, highlighting the importance of flexible and adaptive production planning.

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