```json
{
  "sym_variables": [
    ("x1", "number of cars"),
    ("x2", "number of bikes")
  ],
  "objective_function": "100*x1 + 30*x2",
  "constraints": [
    "10*x1 + 6*x2 <= 200",
    "x1 < x2",
    "x1 >= 0",
    "x2 >= 0"
  ]
}
```

```python
import gurobipy as gp
from gurobipy import GRB

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

# Create variables
cars = m.addVar(vtype=GRB.INTEGER, name="cars")
bikes = m.addVar(vtype=GRB.INTEGER, name="bikes")

# Set objective function
m.setObjective(100 * cars + 30 * bikes, GRB.MAXIMIZE)

# Add constraints
m.addConstr(10 * cars + 6 * bikes <= 200, "budget")
m.addConstr(cars < bikes, "traffic_law")
m.addConstr(cars >= 0, "non_negativity_cars")  # Explicit non-negativity constraints
m.addConstr(bikes >= 0, "non_negativity_bikes")


# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution found:")
    print(f"  Number of cars: {cars.x}")
    print(f"  Number of bikes: {bikes.x}")
    print(f"  Number of bananas transported: {m.objVal}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}")

```
