# Complete PYOMO implementation - Retry Attempt 4

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

def optimize_tournament_scheduling():
    # 1. MODEL CREATION
    model = pyo.ConcreteModel()
    
    # 2. DATA SETUP
    players = [1, 2, 3]
    tournaments = [101, 102, 103]
    
    ranking_points = {
        (1, 101): 100,
        (2, 102): 200,
        (3, 103): 300
    }
    
    max_players_per_tournament = {
        101: 32,
        102: 64,
        103: 128
    }
    
    availability = {
        1: True,
        2: False,
        3: True
    }
    
    # 3. SETS
    model.P = pyo.Set(initialize=players)
    model.T = pyo.Set(initialize=tournaments)
    
    # 4. PARAMETERS
    model.ranking_points = pyo.Param(model.P, model.T, initialize=ranking_points, default=0)
    model.max_players_per_tournament = pyo.Param(model.T, initialize=max_players_per_tournament)
    model.availability = pyo.Param(model.P, initialize=availability)
    
    # 5. VARIABLES
    model.x = pyo.Var(model.P, model.T, within=pyo.Binary)
    
    # 6. OBJECTIVE FUNCTION
    def obj_rule(model):
        return sum(model.ranking_points[p, t] * model.x[p, t] for p in model.P for t in model.T)
    model.objective = pyo.Objective(rule=obj_rule, sense=pyo.maximize)
    
    # 7. CONSTRAINTS
    
    # Player Participation Limit
    def player_participation_limit_rule(model, p):
        return sum(model.x[p, t] for t in model.T) <= 5
    model.player_participation_limit = pyo.Constraint(model.P, rule=player_participation_limit_rule)
    
    # Tournament Capacity Limit
    def tournament_capacity_limit_rule(model, t):
        return sum(model.x[p, t] for p in model.P) <= model.max_players_per_tournament[t]
    model.tournament_capacity_limit = pyo.Constraint(model.T, rule=tournament_capacity_limit_rule)
    
    # Player Availability
    def player_availability_rule(model, p, t):
        return model.x[p, t] <= model.availability[p]
    model.player_availability = pyo.Constraint(model.P, model.T, rule=player_availability_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
    if results.solver.termination_condition == pyo.TerminationCondition.optimal:
        print(f"Optimal value: {pyo.value(model.objective)}")
    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}")

# Execute the optimization
optimize_tournament_scheduling()