def main():
PHweeks = 12
PH = 21 * PHweeks # planning horizon
mo.PH = PH
su.PH = PH
cc.PH = PH
changes = []
tasks = su.create_tasks()
doctors = su.create_doctors(tasks)
su.construct_feasible_solution(doctors, tasks)
print 'Initial Solution cost: ', su.update_objective_function(doctors, tasks)
iterations = 215000
temp = 300
cool = 0.93
costs = [su.return_objective_function(doctors, tasks)]
start_time = time.time()
for i in range(1, iterations):
if i%(iterations/100)==0:
print '{}%'.format(int((i/iterations)*100))
temp *= cool
costs.append(su.return_objective_function(doctors, tasks))
move = su.weighted_choice([
mo.schedule_task_random,
mo.remove_task_random,
mo.swap_task_random,
mo.swap_between_oncall,
mo.swap_morning_oncall,
mo.swap_afternoon_oncall,
mo.swap_weekend_oncall
], [1,0.75,1,1,1,1,1])
original_doctors, original_tasks, good_move = move(doctors, tasks)
for t in original_tasks:
tasks[t].update_all_penalties(doctors)
for d in original_doctors:
doctors[d].update_all_penalties(tasks)
if 'POK' in original_tasks:
good_move = mo.repair_POK(doctors, tasks, original_doctors, original_tasks)
old_cost = sum([d.all_penalties for d in original_doctors.values()]) \
+ sum([t.all_penalties for t in original_tasks.values()])
new_cost = sum([doctors[d].all_penalties for d in original_doctors]) \
+ sum([tasks[t].all_penalties for t in original_tasks])
if (new_cost <= old_cost and good_move):
pass
elif math.exp(-(new_cost - old_cost) / temp) >= random.random() and good_move:
pass
else:
for t in original_tasks:
tasks[t] = original_tasks[t]
for d in original_doctors:
doctors[d] = original_doctors[d]
end_time = time.time()
costs.append(su.return_objective_function(doctors, tasks))
print 'Best objective function: ', su.return_objective_function(doctors,tasks)
time_taken = abs(start_time - end_time)
print 'Time taken: ', time_taken
print 'time per iteration: ', time_taken/iterations
su.write_solution(doctors, tasks, 'SAsol')
+ sum([tasks[t].all_penalties for t in original_tasks])
if new_cost <= old_cost and good_move:
pass
else:
for t in original_tasks:
tasks[t] = original_tasks[t]
for d in original_doctors:
doctors[d] = original_doctors[d]
return doctors
def local_search_lambda(doctors):
tasks = su.create_tasks()
return local_search(doctors, tasks, 100000)
if __name__ == '__main__':
p = mp.Pool()
tasks = su.create_tasks()
best_solutions = [i for row in p.map(RLS, [1, 2, 3,4]) for i in row]
print 'Number of schedules: ', len(best_solutions)
best_solutions = sorted(best_solutions, key = lambda x: su.update_objective_function(x, tasks))[0:2]
best_solutions = p.map(local_search_lambda, best_solutions)
best = min(best_solutions, key=lambda x: su.update_objective_function(x, tasks))
su.update_objective_function(best, tasks)
su.write_solution(best, tasks, 'best_sol')
print 'time: ', start_time - time.time()
