## Problem Description and Formulation

The problem is a linear programming optimization problem. The goal is to maximize the return on investment for a woman who has $300,000 to invest in two health food industries: avocado and kale. The return on investment for the avocado industry is 5%, and for the kale industry, it is 8%. There are constraints on the investment amounts:

- At least 30% of the money must be invested in the avocado industry.
- At most 60% of the money can be invested in the kale industry.

## Symbolic Representation

Let's denote:
- \(x_1\) as the amount invested in the avocado industry.
- \(x_2\) as the amount invested in the kale industry.

The objective function to maximize the total return is:
\[ \text{Maximize:} \quad 0.05x_1 + 0.08x_2 \]

Subject to:
1. \( x_1 + x_2 \leq 300,000 \) (total investment constraint)
2. \( x_1 \geq 0.3 \times 300,000 \) (minimum investment in avocado industry)
3. \( x_2 \leq 0.6 \times 300,000 \) (maximum investment in kale industry)
4. \( x_1, x_2 \geq 0 \) (non-negativity constraint)

## Gurobi Code

```python
import gurobi

def solve_investment_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="avocado_investment", lb=0)
    x2 = model.addVar(name="kale_investment", lb=0)

    # Objective function: Maximize 0.05x1 + 0.08x2
    model.setObjective(0.05 * x1 + 0.08 * x2, sense=gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 300000, name="total_investment")
    model.addConstr(x1 >= 0.3 * 300000, name="min_avocado_investment")
    model.addConstr(x2 <= 0.6 * 300000, name="max_kale_investment")

    # Optimize model
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal investment in avocado industry: $", x1.varValue)
        print("Optimal investment in kale industry: $", x2.varValue)
        print("Maximum return: ", 0.05 * x1.varValue + 0.08 * x2.varValue)
    else:
        print("No optimal solution found.")

solve_investment_problem()
```