## Problem Description and Formulation

The problem described is a classic example of a linear programming problem. Kald Vehicle manufactures two types of vehicles: minivans and trucks. Each vehicle requires a certain amount of time on an assembly machine and a painting machine. The goal is to maximize profit given the constraints on machine availability.

Let's define the decision variables:
- \(x\): The number of minivans to produce.
- \(y\): The number of trucks to produce.

The objective function to maximize profit (\(P\)) is:
\[ P = 1200x + 1700y \]

The constraints based on machine availability are:
1. Assembly machine constraint: \(2x + 4y \leq 12\)
2. Painting machine constraint: \(1.5x + 2y \leq 11\)

Also, \(x \geq 0\) and \(y \geq 0\), since the number of vehicles cannot be negative.

## Gurobi Code

To solve this problem using Gurobi in Python, we can use the following code:

```python
import gurobi

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

    # Define the decision variables
    x = model.addVar(name="minivans", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    y = model.addVar(name="trucks", lb=0, vtype=gurobi.GRB.CONTINUOUS)

    # Objective function: Maximize profit
    model.setObjective(1200*x + 1700*y, gurobi.GRB.MAXIMIZE)

    # Assembly machine constraint
    model.addConstr(2*x + 4*y <= 12, name="assembly_constraint")

    # Painting machine constraint
    model.addConstr(1.5*x + 2*y <= 11, name="painting_constraint")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal production levels: Minivans = {x.varValue}, Trucks = {y.varValue}")
        print(f"Maximum profit: ${model.objVal:.2f}")
    else:
        print("The problem is infeasible.")

if __name__ == "__main__":
    solve_vehicle_production_problem()
```

This code defines the problem in Gurobi, solves it, and prints out the optimal production levels for minivans and trucks, along with the maximum achievable profit. If the problem is infeasible (i.e., there are no feasible solutions), it will indicate that as well.