## Step 1: Define the symbolic representation of the problem
The problem involves variables 'bananas', 'chicken thighs', and 'strips of bacon', which we can denote as $x_0$, $x_1$, and $x_2$ respectively. The objective function to minimize is $6.76x_0 + 8.33x_1 + 5.66x_2$. The constraints are based on the attributes of these variables: grams of carbohydrates ($r_0$), tastiness rating ($r_1$), and sourness index ($r_2$).

## Step 2: List the constraints in symbolic notation
- $11.46x_0 + 6.36x_1 + 4.63x_2$ represents the total grams of carbohydrates.
- The tastiness ratings are $6.51x_0$, $10.01x_1$, and $7.3x_2$.
- The sourness indices are $11.08x_0$, $5.82x_1$, and $10.99x_2$.

## 3: Translate given conditions into mathematical expressions
1. $11.46x_0 \leq 135$
2. $6.51x_0 \leq 179$
3. $11.08x_0 \leq 31$
4. $6.36x_1 \leq 135$
5. $10.01x_1 \leq 179$
6. $5.82x_1 \leq 31$
7. $4.63x_2 \leq 135$
8. $7.3x_2 \leq 179$
9. $10.99x_2 \leq 31$
10. $11.46x_0 + 6.36x_1 \geq 17$
11. $6.36x_1 + 4.63x_2 \geq 30$
12. $11.46x_0 + 6.36x_1 + 4.63x_2 \geq 23$
13. $11.46x_0 + 6.36x_1 + 4.63x_2 \geq 23$ (same as 12)
14. $6.51x_0 + 10.01x_1 \geq 52$
15. $10.01x_1 + 7.3x_2 \geq 27$
16. $6.51x_0 + 7.3x_2 \geq 51$
17. $6.51x_0 + 10.01x_1 + 7.3x_2 \geq 51$
18. $11.08x_0 + 10.99x_2 \geq 10$
19. $11.08x_0 + 5.82x_1 + 10.99x_2 \geq 10$
20. $8x_0 - 6x_2 \geq 0$
21. $-x_0 + 3x_1 \geq 0$
22. $11.46x_0 + 4.63x_2 \leq 119$
23. $6.36x_1 + 4.63x_2 \leq 123$
24. $6.51x_0 + 7.3x_2 \leq 168$
25. $10.01x_1 + 7.3x_2 \leq 107$
26. $6.51x_0 + 10.01x_1 \leq 171$
27. $11.08x_0 + 10.99x_2 \leq 16$

## 4: Define the symbolic representation
```json
{
'sym_variables': [
    ('x0', 'bananas'),
    ('x1', 'chicken thighs'),
    ('x2', 'strips of bacon')
],
'objective_function': '6.76*x0 + 8.33*x1 + 5.66*x2',
'constraints': [
    '11.46*x0 + 6.36*x1 + 4.63*x2 <= 135',
    '6.51*x0 <= 179',
    '11.08*x0 <= 31',
    '6.36*x1 <= 135',
    '10.01*x1 <= 179',
    '5.82*x1 <= 31',
    '4.63*x2 <= 135',
    '7.3*x2 <= 179',
    '10.99*x2 <= 31',
    '11.46*x0 + 6.36*x1 >= 17',
    '6.36*x1 + 4.63*x2 >= 30',
    '11.46*x0 + 6.36*x1 + 4.63*x2 >= 23',
    '6.51*x0 + 10.01*x1 >= 52',
    '10.01*x1 + 7.3*x2 >= 27',
    '6.51*x0 + 7.3*x2 >= 51',
    '6.51*x0 + 10.01*x1 + 7.3*x2 >= 51',
    '11.08*x0 + 10.99*x2 >= 10',
    '11.08*x0 + 5.82*x1 + 10.99*x2 >= 10',
    '8*x0 - 6*x2 >= 0',
    '-x0 + 3*x1 >= 0',
    '11.46*x0 + 4.63*x2 <= 119',
    '6.36*x1 + 4.63*x2 <= 123',
    '6.51*x0 + 7.3*x2 <= 168',
    '10.01*x1 + 7.3*x2 <= 107',
    '6.51*x0 + 10.01*x1 <= 171',
    '11.08*x0 + 10.99*x2 <= 16'
]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()
    
    # Define variables
    x0 = model.addVar(name="bananas", lb=-gurobi.GRB.INFINITY)
    x1 = model.addVar(name="chicken_thighs", lb=-gurobi.GRB.INFINITY)
    x2 = model.addVar(name="strips_of_bacon", lb=-gurobi.GRB.INFINITY)

    # Objective function
    model.setObjective(6.76 * x0 + 8.33 * x1 + 5.66 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(11.46 * x0 + 6.36 * x1 + 4.63 * x2 <= 135)
    model.addConstr(6.51 * x0 <= 179)
    model.addConstr(11.08 * x0 <= 31)
    model.addConstr(6.36 * x1 <= 135)
    model.addConstr(10.01 * x1 <= 179)
    model.addConstr(5.82 * x1 <= 31)
    model.addConstr(4.63 * x2 <= 135)
    model.addConstr(7.3 * x2 <= 179)
    model.addConstr(10.99 * x2 <= 31)
    model.addConstr(11.46 * x0 + 6.36 * x1 >= 17)
    model.addConstr(6.36 * x1 + 4.63 * x2 >= 30)
    model.addConstr(11.46 * x0 + 6.36 * x1 + 4.63 * x2 >= 23)
    model.addConstr(6.51 * x0 + 10.01 * x1 >= 52)
    model.addConstr(10.01 * x1 + 7.3 * x2 >= 27)
    model.addConstr(6.51 * x0 + 7.3 * x2 >= 51)
    model.addConstr(6.51 * x0 + 10.01 * x1 + 7.3 * x2 >= 51)
    model.addConstr(11.08 * x0 + 10.99 * x2 >= 10)
    model.addConstr(11.08 * x0 + 5.82 * x1 + 10.99 * x2 >= 10)
    model.addConstr(8 * x0 - 6 * x2 >= 0)
    model.addConstr(-x0 + 3 * x1 >= 0)
    model.addConstr(11.46 * x0 + 4.63 * x2 <= 119)
    model.addConstr(6.36 * x1 + 4.63 * x2 <= 123)
    model.addConstr(6.51 * x0 + 7.3 * x2 <= 168)
    model.addConstr(10.01 * x1 + 7.3 * x2 <= 107)
    model.addConstr(6.51 * x0 + 10.01 * x1 <= 171)
    model.addConstr(11.08 * x0 + 10.99 * x2 <= 16)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Bananas: {x0.varValue}")
        print(f"Chicken Thighs: {x1.varValue}")
        print(f"Strips of Bacon: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```