```json
{
  "sym_variables": [
    ("x0", "hours worked by Hank"),
    ("x1", "hours worked by Ringo"),
    ("x2", "hours worked by Dale"),
    ("x3", "hours worked by Bobby")
  ],
  "objective_function": "3.19*x0 + 1.64*x1 + 2.01*x2 + 2.72*x3",
  "constraints": [
    "2*x0 + 15*x1 >= 26",
    "15*x1 + 1*x2 >= 30",
    "2*x0 + 1*x2 >= 10",
    "2*x0 + 16*x3 >= 21",
    "2*x0 + 15*x1 + 1*x2 >= 18",
    "2*x0 + 15*x1 + 16*x3 >= 18",
    "2*x0 + 15*x1 + 1*x2 >= 22",
    "2*x0 + 15*x1 + 16*x3 >= 22",
    "2*x0 + 15*x1 + 1*x2 + 16*x3 >= 22",
    "4*x1 + 15*x2 >= 43",
    "14*x0 + 15*x3 >= 36",
    "15*x2 + 15*x3 >= 23",
    "14*x0 + 4*x1 + 15*x2 + 15*x3 >= 23",
    "7*x2 + 11*x3 >= 47",
    "10*x0 + 7*x2 >= 27",
    "3*x1 + 11*x3 >= 29",
    "10*x0 + 3*x1 >= 29",
    "10*x0 + 11*x3 >= 76",
    "10*x0 + 7*x2 + 11*x3 >= 65",
    "3*x1 + 7*x2 + 11*x3 >= 65",
    "10*x0 + 3*x1 + 7*x2 >= 65",
    "10*x0 + 7*x2 + 11*x3 >= 41",
    "3*x1 + 7*x2 + 11*x3 >= 41",
    "10*x0 + 3*x1 + 7*x2 >= 41",
    "10*x0 + 7*x2 + 11*x3 >= 73",
    "3*x1 + 7*x2 + 11*x3 >= 73",
    "10*x0 + 3*x1 + 7*x2 >= 73",
    "10*x0 + 3*x1 + 7*x2 + 11*x3 >= 73",
    "4*x2 - 3*x3 >= 0",
    "7*x0 - 10*x2 >= 0",
    "15*x1 + 1*x2 <= 83",
    "2*x0 + 1*x2 <= 89",
    "2*x0 + 15*x1 <= 89",
    "2*x0 + 15*x1 + 1*x2 <= 49",
    "4*x1 + 15*x2 <= 143",
    "14*x0 + 15*x3 <= 126",
    "14*x0 + 15*x2 <= 104",
    "4*x1 + 15*x3 <= 101",
    "10*x0 + 7*x2 <= 316",
    "10*x0 + 3*x1 + 11*x3 <= 218",
    "3*x1 + 7*x2 + 11*x3 <= 219"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
hank = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="hank")
ringo = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="ringo")
dale = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="dale")
bobby = m.addVar(lb=0, vtype=gp.GRB.INTEGER, name="bobby")

# Set objective function
m.setObjective(3.19 * hank + 1.64 * ringo + 2.01 * dale + 2.72 * bobby, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(2 * hank + 15 * ringo >= 26)
m.addConstr(15 * ringo + 1 * dale >= 30)
m.addConstr(2 * hank + 1 * dale >= 10)
m.addConstr(2 * hank + 16 * bobby >= 21)
m.addConstr(2 * hank + 15 * ringo + 1 * dale >= 18)
m.addConstr(2 * hank + 15 * ringo + 16 * bobby >= 18)
m.addConstr(2 * hank + 15 * ringo + 1 * dale >= 22)
m.addConstr(2 * hank + 15 * ringo + 16 * bobby >= 22)
m.addConstr(2 * hank + 15 * ringo + 1 * dale + 16 * bobby >= 22)
m.addConstr(4 * ringo + 15 * dale >= 43)
m.addConstr(14 * hank + 15 * bobby >= 36)
m.addConstr(15 * dale + 15 * bobby >= 23)
m.addConstr(14 * hank + 4 * ringo + 15 * dale + 15 * bobby >= 23)
m.addConstr(7 * dale + 11 * bobby >= 47)
m.addConstr(10 * hank + 7 * dale >= 27)
m.addConstr(3 * ringo + 11 * bobby >= 29)
m.addConstr(10 * hank + 3 * ringo >= 29)
m.addConstr(10 * hank + 11 * bobby >= 76)
m.addConstr(10 * hank + 7 * dale + 11 * bobby >= 65)
m.addConstr(3 * ringo + 7 * dale + 11 * bobby >= 65)
m.addConstr(10 * hank + 3 * ringo + 7 * dale >= 65)
m.addConstr(10 * hank + 7 * dale + 11 * bobby >= 41)
m.addConstr(3 * ringo + 7 * dale + 11 * bobby >= 41)
m.addConstr(10 * hank + 3 * ringo + 7 * dale >= 41)
m.addConstr(10 * hank + 7 * dale + 11 * bobby >= 73)
m.addConstr(3 * ringo + 7 * dale + 11 * bobby >= 73)
m.addConstr(10 * hank + 3 * ringo + 7 * dale >= 73)
m.addConstr(10 * hank + 3 * ringo + 7 * dale + 11 * bobby >= 73)
m.addConstr(4 * dale - 3 * bobby >= 0)
m.addConstr(7 * hank - 10 * dale >= 0)
m.addConstr(15 * ringo + 1 * dale <= 83)
m.addConstr(2 * hank + 1 * dale <= 89)
m.addConstr(2 * hank + 15 * ringo <= 89)
m.addConstr(2 * hank + 15 * ringo + 1 * dale <= 49)
m.addConstr(4 * ringo + 15 * dale <= 143)
m.addConstr(14 * hank + 15 * bobby <= 126)
m.addConstr(14 * hank + 15 * dale <= 104)
m.addConstr(4 * ringo + 15 * bobby <= 101)
m.addConstr(10 * hank + 7 * dale <= 316)
m.addConstr(10 * hank + 3 * ringo + 11 * bobby <= 218)
m.addConstr(3 * ringo + 7 * dale + 11 * bobby <= 219)


# Optimize model
m.optimize()

# Print solution
if m.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    for v in m.getVars():
        print('%s %g' % (v.varName, v.x))
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print("Optimization ended with status:", m.status)

```
