def main():
agents, tasks = generate_painter(X, Y, 3)
# We give back m tasks in a giveback phase, and each iteration we assign one task,
# so for m agents we should have a giveback probability < 1/m
auctioneer = Auctioneer(agents, tasks, 0.25) #1 / ( * len(agents)))
schedule = auctioneer.optimize_schedule()
Agent.print_schedule(X, Y, auctioneer, schedule)
def main():
agents, tasks = generate_painter(X, Y, 3)
# nFeas = The number of feasible 1-moves
# Given a schedule, we can move tasks equal to the number of agents.
# Using the result by Osman, we set alpha = n * Nfeas, gamma = n
alpha = math.factorial(X * Y)
# The number of iterations is n
annealer = Annealer(agents, tasks, 2500, 100, 0.1, 1)
schedule = annealer.optimize_schedule()
Agent.print_schedule(X, Y, annealer, schedule)