## Problem Description and Formulation

The cereal company produces three types of cereals: nutritional, kids', and sugary. Each type of cereal requires a specific amount of oat and sugar. The company has limited resources of oat (500 units) and sugar (700 units). The goal is to determine the optimal production levels of each cereal type to maximize revenue.

Let's define the decision variables:

- $N$ = number of boxes of nutritional cereal
- $K$ = number of boxes of kids' cereal
- $S$ = number of boxes of sugary cereal

The objective function to maximize revenue is:
\[ \text{Maximize:} \quad 1N + 1.5K + 2S \]

Subject to the constraints:
\[ 3N + 1.5K + 2S \leq 500 \] (oat constraint)
\[ 1N + 1.5K + 4S \leq 700 \] (sugar constraint)
\[ N \geq 0, K \geq 0, S \geq 0 \] (non-negativity constraints)

## Gurobi Code

```python
import gurobi

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

    # Define the decision variables
    N = model.addVar(name="Nutritional", lb=0, ub=gurobi.GRB.INFINITY)
    K = model.addVar(name="Kids", lb=0, ub=gurobi.GRB.INFINITY)
    S = model.addVar(name="Sugary", lb=0, ub=gurobi.GRB.INFINITY)

    # Objective function: Maximize revenue
    model.setObjective(1*N + 1.5*K + 2*S, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(3*N + 1.5*K + 2*S <= 500, name="Oat_Constraint")
    model.addConstr(1*N + 1.5*K + 4*S <= 700, name="Sugar_Constraint")

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Nutritional Cereal: {N.varValue}")
        print(f"Kids' Cereal: {K.varValue}")
        print(f"Sugary Cereal: {S.varValue}")
        print(f"Max Revenue: {model.objVal}")
    else:
        print("The model is infeasible")

solve_cereal_production()
```