# Complete PYOMO implementation - Retry Attempt 4

import pyomo.environ as pyo
from pyomo.opt import SolverFactory

def optimize_inn_revenue():
    # 1. MODEL CREATION
    model = pyo.ConcreteModel()

    # 2. DATA SETUP
    # Assuming these are the given parameters
    reservation_ids = [1, 2, 3]
    rates = {1: 120.0, 2: 150.0, 3: 200.0}
    lengths_of_stay = {1: 2, 2: 3, 3: 5}
    minimum_rate = 100.0
    room_capacity = 4  # Assuming a fixed capacity for simplicity

    # 3. SETS
    model.I = pyo.Set(initialize=reservation_ids)

    # 4. PARAMETERS
    model.rate = pyo.Param(model.I, initialize=rates)
    model.length_of_stay = pyo.Param(model.I, initialize=lengths_of_stay)

    # 5. VARIABLES
    model.r = pyo.Var(model.I, within=pyo.NonNegativeReals, bounds=(minimum_rate, None))

    # 6. OBJECTIVE FUNCTION
    def revenue_rule(model):
        return sum(model.r[i] * model.length_of_stay[i] for i in model.I)
    model.objective = pyo.Objective(rule=revenue_rule, sense=pyo.maximize)

    # 7. CONSTRAINTS
    # Minimum Rate Constraint
    def min_rate_rule(model, i):
        return model.r[i] >= minimum_rate
    model.min_rate_constraint = pyo.Constraint(model.I, rule=min_rate_rule)

    # Room Capacity Constraint
    # Assuming each reservation corresponds to a single room and the capacity is a known parameter
    def capacity_rule(model, i):
        # This constraint is a placeholder as the problem statement does not provide occupancy data
        # Assuming occupancy is always within capacity for this example
        return pyo.Constraint.Feasible
    model.capacity_constraint = pyo.Constraint(model.I, rule=capacity_rule)

    # 8. SOLVING WITH GUROBI
    solver = SolverFactory('gurobi')

    # Solve the model
    results = solver.solve(model, tee=True)

    # 9. RESULT PROCESSING
    if results.solver.termination_condition == pyo.TerminationCondition.optimal:
        print("Optimal solution found!")
        print(f"Optimal value: {pyo.value(model.objective)}")
        
        # Extract variable values
        print("\nVariable values:")
        for i in model.I:
            r_val = pyo.value(model.r[i])
            print(f"Rate for reservation {i} = {r_val:.2f}")
        
    elif results.solver.termination_condition == pyo.TerminationCondition.infeasible:
        print("Problem is infeasible")
    elif results.solver.termination_condition == pyo.TerminationCondition.unbounded:
        print("Problem is unbounded")
    else:
        print(f"Solver terminated with condition: {results.solver.termination_condition}")

    return model

# Execute the optimization
optimize_inn_revenue()