## Problem Description and Formulation

The problem is a classic example of a linear programming problem. The company wants to maximize its profit by producing the optimal number of individual and family size cereal boxes given the constraints on cereal units and filling time.

Let's define the variables:
- \(x\): the number of individual cereal boxes
- \(y\): the number of family size cereal boxes

The objective function to maximize profit is:
\[ \text{Maximize:} \quad 4x + 8y \]

Subject to the constraints:
1. Cereal constraint: \( 20x + 60y \leq 2000 \)
2. Filling time constraint: \( 10x + 15y \leq 750 \)
3. Non-negativity constraints: \( x \geq 0, y \geq 0 \)

## Gurobi Code

To solve this problem using Gurobi in Python, we will use the Gurobi Python API. First, ensure you have Gurobi installed in your Python environment. You can install it via pip if you have the Gurobi license and credentials set up:

```bash
pip install gurobi
```

Here's the Gurobi code for the problem:

```python
import gurobi as gp

# Create a new model
m = gp.Model("Cereal_Production")

# Define variables
x = m.addVar(lb=0, name="individual_cereal_boxes")  # Number of individual cereal boxes
y = m.addVar(lb=0, name="family_size_cereal_boxes")  # Number of family size cereal boxes

# Objective function: Maximize profit
m.setObjective(4*x + 8*y, gp.GRB.MAXIMIZE)

# Constraints
m.addConstr(20*x + 60*y <= 2000, name="cereal_constraint")  # Cereal units constraint
m.addConstr(10*x + 15*y <= 750, name="filling_time_constraint")  # Filling time constraint

# Solve the model
m.optimize()

# Print solution
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution found. Production levels: individual boxes = {x.varValue}, family size boxes = {y.varValue}")
    print(f"Maximum profit: ${4*x.varValue + 8*y.varValue:.2f}")
else:
    print("No optimal solution found.")
```