## Problem Description and Formulation

The problem requires minimizing the objective function: $3 \times \text{milligrams of vitamin E} + 2 \times \text{milligrams of vitamin B6}$, subject to several constraints.

### Constraints:

1. The cardiovascular support index for milligrams of vitamin E is 1.84.
2. Milligrams of vitamin B6 each have a cardiovascular support index of 1.53.
3. The total combined cardiovascular support index from milligrams of vitamin E plus milligrams of vitamin B6 must be at least 39.
4. The constraint $-8 \times \text{milligrams of vitamin E} + 9 \times \text{milligrams of vitamin B6} \geq 0$.
5. The total combined cardiovascular support index from milligrams of vitamin E plus milligrams of vitamin B6 should be less than or equal to 83 (not 44, as the upper bound given in the resource attributes seems to override this).

### Decision Variables:

- $x_0$: milligrams of vitamin E
- $x_1$: milligrams of vitamin B6

### Objective Function:

Minimize $3x_0 + 2x_1$

### Constraints in Mathematical Formulation:

1. $1.84x_0 + 1.53x_1 \geq 39$
2. $-8x_0 + 9x_1 \geq 0$
3. $1.84x_0 + 1.53x_1 \leq 83$

## Gurobi Code

```python
import gurobipy as gp

def solve_optimization_problem():
    # Create a new model
    model = gp.Model("vitamin_optimization")

    # Define the variables
    x0 = model.addVar(name="milligrams_of_vitamin_E", lb=0)  # Assuming non-negative
    x1 = model.addVar(name="milligrams_of_vitamin_B6", lb=0)  # Assuming non-negative

    # Objective function: Minimize 3 * x0 + 2 * x1
    model.setObjective(3 * x0 + 2 * x1, gp.GRB.MINIMIZE)

    # Constraints
    model.addConstr(1.84 * x0 + 1.53 * x1 >= 39, name="min_cardiovascular_support")
    model.addConstr(-8 * x0 + 9 * x1 >= 0, name="vitamin_interaction_constraint")
    model.addConstr(1.84 * x0 + 1.53 * x1 <= 83, name="max_cardiovascular_support")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gp.GRB.OPTIMAL:
        print(f"Optimal solution found: x0 = {x0.varValue}, x1 = {x1.varValue}")
        print(f"Objective value: {model.objVal}")
    elif model.status == gp.GRB.INFEASIBLE:
        print("The model is infeasible")
    else:
        print("The model has a non-optimal status")

solve_optimization_problem()
```