## Problem Description and Formulation

The problem is an optimization problem with two variables: `potatoes` and `protein bars`. The objective is to minimize the function `5 * potatoes + 2 * protein bars` subject to several constraints.

### Constraints

1. **Healthiness Rating**: The total combined healthiness rating from `potatoes` and `protein bars` must be at least 31 and at most 71.
2. **Tastiness Rating**: The total combined tastiness rating from `potatoes` and `protein bars` must be at least 20 and at most 52.
3. **Grams of Fiber**: The total combined grams of fiber from `potatoes` and `protein bars` must be at least 24 and at most 54.
4. **Additional Constraint**: `9 * potatoes - protein bars >= 0`

### Resource Attributes

| Resource | Description | Upper Bound | Potatoes | Protein Bars |
| --- | --- | --- | --- | --- |
| r0 | Healthiness Rating | 71 | 5 | 7 |
| r1 | Tastiness Rating | 52 | 2 | 1 |
| r2 | Grams of Fiber | 54 | 8 | 10 |

## Gurobi Code

```python
import gurobi

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

    # Define variables
    potatoes = model.addVar(lb=0, name="potatoes", vtype=gurobi.GRB.CONTINUOUS)
    protein_bars = model.addVar(lb=0, name="protein_bars", vtype=gurobi.GRB.CONTINUOUS)

    # Objective function: Minimize 5 * potatoes + 2 * protein_bars
    model.setObjective(5 * potatoes + 2 * protein_bars, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstraint(5 * potatoes + 7 * protein_bars >= 31, name="healthiness_rating_min")
    model.addConstraint(5 * potatoes + 7 * protein_bars <= 71, name="healthiness_rating_max")
    model.addConstraint(2 * potatoes + protein_bars >= 20, name="tastiness_rating_min")
    model.addConstraint(2 * potatoes + protein_bars <= 52, name="tastiness_rating_max")
    model.addConstraint(8 * potatoes + 10 * protein_bars >= 24, name="fiber_min")
    model.addConstraint(8 * potatoes + 10 * protein_bars <= 54, name="fiber_max")
    model.addConstraint(9 * potatoes - protein_bars >= 0, name="additional_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Potatoes: {potatoes.varValue}")
        print(f"Protein Bars: {protein_bars.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```