## Problem Description and Formulation

The problem described is a classic linear programming problem. CE Chemicals produces two types of products: adhesives and plasticizers. The production of these products requires the use of two devices: an automatic device and a human-operated device. Each device has a limited availability of 450 minutes per day.

Let's denote:
- \(x\) as the number of packages of adhesives produced daily.
- \(y\) as the number of packages of plasticizers produced daily.

The constraints based on the device availability are:
1. For the automatic device: \(6x + 8y \leq 450\)
2. For the human-operated device: \(5x + 4y \leq 450\)

The objective is to maximize the revenue \(R\), given by:
\[R = 8.5x + 11.5y\]

## Gurobi Code Formulation

To solve this problem using Gurobi in Python, we will follow these steps:
- Import the necessary libraries.
- Create a Gurobi model.
- Define the variables.
- Set up the objective function.
- Add the constraints.
- Solve the model.
- Print the solution.

```python
import gurobi

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

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

    # Set up the objective function
    model.setObjective(8.5 * x + 11.5 * y, gurobi.GRB.MAXIMIZE)

    # Add the constraints
    model.addConstr(6 * x + 8 * y <= 450, name="automatic_device_constraint")
    model.addConstr(5 * x + 4 * y <= 450, name="human_operated_device_constraint")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: x = {x.varValue}, y = {y.varValue}")
        print(f"Maximum revenue: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_production_problem()
```

This code defines the problem in a way that Gurobi can solve it. It sets up a linear programming problem with the goal to maximize revenue under the given constraints. The solution will provide the optimal number of packages of adhesives and plasticizers to produce daily to maximize revenue.