# Complete PYOMO implementation

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

def swimming_optimization():
    """Optimize swimmer-event assignments to maximize performance score."""
    
    # 1. MODEL CREATION
    model = pyo.ConcreteModel()
    
    # 2. DATA SETUP
    # Example data based on the provided database schema and realistic data
    swimmers = [1, 2, 3]
    events = [101, 102, 103]
    performance_scores = {
        (1, 101): 9.5,
        (2, 102): 8.7,
        (3, 103): 9.0
    }
    stadium_capacities = {
        101: 500,
        102: 1000,
        103: 1500
    }
    max_events_per_swimmer = 2
    
    # CRITICAL: Validate data consistency
    assert all((i, j) in performance_scores for i in swimmers for j in events), "Performance scores missing for some swimmer-event pairs"
    
    # 3. SETS
    model.S = pyo.Set(initialize=swimmers)
    model.E = pyo.Set(initialize=events)
    
    # 4. PARAMETERS
    model.performance_score = pyo.Param(model.S, model.E, initialize=performance_scores, default=0)
    model.capacity = pyo.Param(model.E, initialize=stadium_capacities)
    
    # 5. VARIABLES
    model.x = pyo.Var(model.S, model.E, within=pyo.Binary)
    
    # 6. OBJECTIVE FUNCTION
    def obj_rule(model):
        return sum(model.performance_score[i, j] * model.x[i, j] for i in model.S for j in model.E)
    model.objective = pyo.Objective(rule=obj_rule, sense=pyo.maximize)
    
    # 7. CONSTRAINTS
    
    # Maximum Events per Swimmer Constraint
    def max_events_rule(model, i):
        return sum(model.x[i, j] for j in model.E) <= max_events_per_swimmer
    model.max_events_constraint = pyo.Constraint(model.S, rule=max_events_rule)
    
    # Stadium Capacity Constraint
    def capacity_rule(model, j):
        return sum(model.x[i, j] for i in model.S) <= model.capacity[j]
    model.capacity_constraint = pyo.Constraint(model.E, rule=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)
    
    # 9. RESULT PROCESSING
    if results.solver.termination_condition == pyo.TerminationCondition.optimal:
        print("Optimal solution found!")
        print(f"Optimal value: {pyo.value(model.objective):.3f}")
        
        # Extract variable values
        print("\nAssignment of swimmers to events:")
        for i in model.S:
            for j in model.E:
                if pyo.value(model.x[i, j]) > 0.5:  # Binary variable, check if assigned
                    print(f"Swimmer {i} assigned to Event {j}")
        
    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
swimming_optimization()