# Complete PYOMO implementation

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

def flight_scheduling_optimization():
    """Optimize flight scheduling to minimize total fuel consumption."""
    
    # 1. MODEL CREATION
    model = pyo.ConcreteModel()
    
    # 2. DATA SETUP
    # Example data (replace with actual data loading from database)
    flights = [1, 2, 3]
    airports = [1, 2, 3]
    companies = [1, 2, 3]
    
    fuel_coefficients = {1: 0.5, 2: 0.6, 3: 0.7}
    airport_capacities = {1: 10, 2: 15, 3: 20}
    company_capacities = {1: 5, 2: 10, 3: 15}
    
    # Validate array lengths
    assert len(flights) == len(fuel_coefficients), "Fuel coefficients length mismatch"
    assert len(airports) == len(airport_capacities), "Airport capacities length mismatch"
    assert len(companies) == len(company_capacities), "Company capacities length mismatch"
    
    # 3. SETS
    model.F = pyo.Set(initialize=flights)  # Flights
    model.A = pyo.Set(initialize=airports)  # Airports
    model.C = pyo.Set(initialize=companies)  # Companies
    
    # 4. PARAMETERS
    model.fuel_coefficient = pyo.Param(model.F, initialize=fuel_coefficients)
    model.airport_capacity = pyo.Param(model.A, initialize=airport_capacities)
    model.company_capacity = pyo.Param(model.C, initialize=company_capacities)
    
    # 5. VARIABLES
    model.x = pyo.Var(model.F, model.A, model.C, within=pyo.Binary)
    
    # 6. OBJECTIVE FUNCTION
    def obj_rule(model):
        return sum(model.fuel_coefficient[f] * model.x[f, a, c] for f in model.F for a in model.A for c in model.C)
    model.objective = pyo.Objective(rule=obj_rule, sense=pyo.minimize)
    
    # 7. CONSTRAINTS
    
    # Flight Assignment Constraint
    def flight_assignment_rule(model, f):
        return sum(model.x[f, a, c] for a in model.A for c in model.C) == 1
    model.flight_assignment = pyo.Constraint(model.F, rule=flight_assignment_rule)
    
    # Airport Capacity Constraint
    def airport_capacity_rule(model, a):
        return sum(model.x[f, a, c] for f in model.F for c in model.C) <= model.airport_capacity[a]
    model.airport_capacity_constraint = pyo.Constraint(model.A, rule=airport_capacity_rule)
    
    # Company Capacity Constraint
    def company_capacity_rule(model, c):
        return sum(model.x[f, a, c] for f in model.F for a in model.A) <= model.company_capacity[c]
    model.company_capacity_constraint = pyo.Constraint(model.C, rule=company_capacity_rule)
    
    # 8. SOLVING WITH GUROBI
    solver = SolverFactory('gurobi')
    
    # Optional: Set solver options
    solver.options['TimeLimit'] = 300  # 5 minutes
    solver.options['MIPGap'] = 0.01    # 1% gap
    
    # Solve the model
    results = solver.solve(model, tee=True)  # tee=True shows solver output
    
    # 9. RESULT PROCESSING
    # Check solver status
    if results.solver.termination_condition == pyo.TerminationCondition.optimal:
        print("Optimal solution found!")
        print(f"Optimal value: {pyo.value(model.objective)}")
        
        # Extract variable values
        print("\nFlight assignments:")
        for f in model.F:
            for a in model.A:
                for c in model.C:
                    if pyo.value(model.x[f, a, c]) > 0.5:  # Only print assigned flights
                        print(f"Flight {f} assigned to Airport {a} and Company {c}")
        
    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

# Run the optimization
if __name__ == "__main__":
    flight_scheduling_optimization()