## Problem Description and Formulation

The problem is a classic example of a linear programming problem. The goal is to maximize revenue by determining the optimal number of burgers and hot-dogs to produce given the constraints on meat and binding agent availability.

Let's define the variables:
- \(B\): Number of burgers to produce
- \(H\): Number of hot-dogs to produce

The objective function to maximize revenue (\(R\)) is:
\[ R = 0.30B + 0.20H \]

The constraints based on the availability of meat (\(M\)) and binding agent (\(A\)) are:
- Meat constraint: \(3B + 2H \leq 2000\)
- Binding agent constraint: \(2B + H \leq 1800\)
- Non-negativity constraints: \(B \geq 0, H \geq 0\)

## Gurobi Code

To solve this problem using Gurobi in Python, we will use the Gurobi Python API. First, ensure you have Gurobi installed in your environment. You can install it via pip:
```bash
pip install gurobi
```

Here's the Gurobi code for the problem:

```python
import gurobi as gp

# Create a new model
model = gp.Model("Burgers_and_Hotdogs")

# Define variables
B = model.addVar(lb=0, name="Burgers")
H = model.addVar(lb=0, name="Hotdogs")

# Objective function: Maximize revenue
model.setObjective(0.30*B + 0.20*H, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(3*B + 2*H <= 2000, name="Meat_Constraint")
model.addConstr(2*B + H <= 1800, name="Binding_Agent_Constraint")

# Optimize
model.optimize()

# Print solution
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal number of burgers: {B.varValue}")
    print(f"Optimal number of hot-dogs: {H.varValue}")
    print(f"Max Revenue: ${model.objVal:.2f}")
else:
    print("The model is infeasible.")
```