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This is a linear programming problem with the objective function and constraints specified as follows:

### Objective:
Minimize \( Z = 2x_1 + 3x_2 \)

### Constraints:
1. \( 2x_1 + 2x_2 \leq 16 \)
2. \( 2x_1 + 3x_2 \geq 20 \)
3. \( x_1 + 2x_2 = 10 \)

### Solution Approach:
To solve this problem, we need to:
1. Graph each constraint in the \(x_1x_2\)-plane.
2. Identify the feasible region that satisfies all constraints.
3. Find the points of intersection within the feasible region.
4. Evaluate the objective function \( Z = 2x_1 + 3x_2 \) at each vertex of the feasible region.
5. Determine the minimum value of \( Z \) based on these calculations.

Would you like a detailed step-by-step solution for each part of the process?

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Here are 5 related questions you might explore:
1. How would the solution change if we maximized \( Z \) instead of minimizing it?
2. What happens if one of the constraints is changed or removed?
3. How can we solve this problem using the Simplex method instead of graphing?
4. What are the conditions for a feasible region to be bounded?
5. How can we interpret each constraint geometrically?

**Tip:** When graphing linear programming constraints, use intercepts or slope-intercept form to make plotting easier.

Minimize Z = 2x1 + 3x2 with constraints: 2x1 + 2x2 <= 16, 2x1 + 3x2 >= 20, and x1 + 2x2 = 10.

Learn how to solve a linear programming problem by minimizing Z = 2x1 + 3x2 with given constraints. This example covers key concepts in optimization, including identifying feasible regions and evaluating objective functions. Suitable for Grades 10-12.

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