## Problem Description and Formulation

The stationary company needs to determine the optimal production levels of one-inch and two-inch binders to maximize profit while meeting demand constraints. The constraints are as follows:

- The company must make at least 60 one-inch binders.
- The company must make at least 70 two-inch binders.
- The company can make at most 150 one-inch binders.
- The company can make at most 160 two-inch binders.
- The company must send at least 200 binders of either type to a school.

The profit per one-inch binder is $1, and the profit per two-inch binder is $2.

## Symbolic Representation

Let's denote:
- \(x_1\) as the number of one-inch binders produced.
- \(x_2\) as the number of two-inch binders produced.

The objective function to maximize profit is:
\[ \text{Maximize:} \quad 1x_1 + 2x_2 \]

Subject to:
\[ x_1 \geq 60 \]
\[ x_2 \geq 70 \]
\[ x_1 \leq 150 \]
\[ x_2 \leq 160 \]
\[ x_1 + x_2 \geq 200 \]
\[ x_1, x_2 \geq 0 \] and are integers (though Gurobi can handle this automatically, we note it for completeness)

## Gurobi Code

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(lb=0, ub=150, name="one_inch_binders", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(lb=0, ub=160, name="two_inch_binders", vtype=gurobi.GRB.INTEGER)

    # Objective function: Maximize 1*x1 + 2*x2
    model.setObjective(1*x1 + 2*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 >= 60, name="min_one_inch")
    model.addConstr(x2 >= 70, name="min_two_inch")
    model.addConstr(x1 <= 150, name="max_one_inch")
    model.addConstr(x2 <= 160, name="max_two_inch")
    model.addConstr(x1 + x2 >= 200, name="total_binders")

    # Solve the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal Solution: x1 = {x1.varValue}, x2 = {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("The model did not converge to an optimal solution.")

solve_binder_problem()
```