## Problem Description and Formulation

The problem is a classic example of a linear programming problem. The store manufactures two types of tools: hammers and screwdrivers, using two machines: a lathe and a CNG machine. The goal is to maximize the profit by determining the optimal number of packages of hammers and screwdrivers to produce.

Let's define the variables:

* $x$: number of packages of hammers to produce
* $y$: number of packages of screwdrivers to produce

The objective function is to maximize the profit:

* Profit = $72x + 70y$

The constraints are:

* Lathe machine availability: $28x + 23y \leq 720$
* CNG machine availability: $82x + 76y \leq 720$
* Non-negativity constraints: $x \geq 0, y \geq 0$

## Gurobi Code

```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x = model.addVar(name="x", lb=0, ub=None, obj=72)  # number of packages of hammers
y = model.addVar(name="y", lb=0, ub=None, obj=70)  # number of packages of screwdrivers

# Define the constraints
lathe_constraint = model.addConstr(28*x + 23*y <= 720, name="lathe_constraint")
cng_constraint = model.addConstr(82*x + 76*y <= 720, name="cng_constraint")

# Set the objective function
model.setObjective(72*x + 70*y, gurobi.GRB.MAXIMIZE)

# Optimize the model
model.optimize()

# Print the results
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Number of packages of hammers: {x.varValue}")
    print(f"Number of packages of screwdrivers: {y.varValue}")
    print(f"Maximum profit: {model.objVal}")
else:
    print("No optimal solution found.")
```