## Problem Description and Formulation

The farm needs to transport milk, and it has two options: rail shipments and truck shipments. Each rail shipment can carry 400 litres of milk, but it costs $100. Each truck shipment can carry 200 litres of milk, and it costs $85. The farm has a budget of $3000, and there's a constraint that the number of rail shipments cannot exceed the number of truck shipments. The goal is to maximize the total litres of milk transported.

## Symbolic Representation

Let's denote:
- \(R\) as the number of rail shipments,
- \(T\) as the number of truck shipments.

The objective function to maximize the total litres of milk transported is:
\[ \text{Maximize:} \quad 400R + 200T \]

Subject to:
1. Budget constraint: \( 100R + 85T \leq 3000 \)
2. Rail shipments cannot exceed truck shipments: \( R \leq T \)
3. Non-negativity constraints: \( R \geq 0, T \geq 0 \)
4. Integer constraints: \( R \in \mathbb{Z}, T \in \mathbb{Z} \) (since we can't have fractions of shipments)

## Gurobi Code

```python
import gurobi

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

    # Define variables
    R = model.addVar(name="Rail Shipments", vtype=gurobi.GRB.INTEGER)
    T = model.addVar(name="Truck Shipments", vtype=gurobi.GRB.INTEGER)

    # Objective function: Maximize 400R + 200T
    model.setObjective(400*R + 200*T, gurobi.GRB.MAXIMIZE)

    # Budget constraint: 100R + 85T <= 3000
    model.addConstr(100*R + 85*T <= 3000, name="Budget Constraint")

    # Rail shipments cannot exceed truck shipments: R <= T
    model.addConstr(R <= T, name="Rail vs Truck Constraint")

    # Non-negativity constraints are inherently satisfied by using integer variables

    # Solve the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal Solution:")
        print(f"Rail Shipments: {R.varValue}")
        print(f"Truck Shipments: {T.varValue}")
        print(f"Total litres: {400*R.varValue + 200*T.varValue}")
    else:
        print("The model is infeasible.")

solve_milk_transportation()
```