## Problem Description and Formulation

The food truck optimization problem can be formulated as a linear programming problem. The goal is to maximize the revenue given the constraints on the number of burritos and tacitos that can be sold.

### Decision Variables

- \(B\): Number of burritos to sell
- \(T\): Number of tacitos to sell

### Objective Function

The objective is to maximize the revenue \(R\), which is given by:
\[ R = 17B + 12T \]

### Constraints

1. **Burrito Sales Constraints**
   - \( B \geq 30 \) (at least 30 orders of burritos)
   - \( B \leq 100 \) (at most 100 orders of burritos)

2. **Tacito Sales Constraints**
   - \( T \geq 20 \) (at least 20 orders of tacitos)
   - \( T \leq 150 \) (at most 150 orders of tacitos)

3. **Total Sales Constraint**
   - \( B + T \leq 250 \) (total orders cannot exceed 250)

### Non-Negativity Constraints

- \( B \geq 0 \) and \( T \geq 0 \) (number of orders cannot be negative, but these are implicitly satisfied by the given constraints)

## Gurobi Code

```python
import gurobi

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

    # Define the decision variables
    B = model.addVar(lb=30, ub=100, name="Burritos")
    T = model.addVar(lb=20, ub=150, name="Tacitos")

    # Objective function: Maximize revenue
    model.setObjective(17 * B + 12 * T, gurobi.GRB.MAXIMIZE)

    # Additional constraint: Total sales cannot exceed 250
    model.addConstraint(B + T <= 250)

    # Optimize the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution found.")
        print(f"Burritos to sell: {B.varValue}")
        print(f"Tacitos to sell: {T.varValue}")
        print(f"Max Revenue: {model.objVal}")
    else:
        print("No optimal solution found.")

# Run the function
solve_food_truck_problem()
```

This code defines the optimization problem using Gurobi's Python interface, solves it, and prints out the optimal number of burritos and tacitos to sell in order to maximize revenue, given the constraints.