def cerate_test_enviroment():
TEST_MAPS = dict()
TEST_POSITIONS = dict()
for i in range(10):
TEST_MAPS[i] = mapaAleatorio(N_STATIONS, N_STATES)
for i in range(10):
positions_list = random.sample(range(1, N_STATIONS), N_CARS+1)
positions = list()
for station in positions_list:
positions.append("estacion_" + str(station))
TEST_POSITIONS[i] = positions
save_obj(TEST_POSITIONS, "TEST_POSITIONS")
save_obj(TEST_MAPS, "TEST_MAPS")
position = POSITIONS[id_car]
capacity = random.randint(5,20) #TO DO: each car have their on capcity
car = Car( id_car, position, capacity )
FLEET.insertCar(car)
FLEET.update_carsPosition() #Save car positions in FLEET.positions
MAP.update_available_stations(FLEET) #Remove car positio and car subsytem from available_stations
FLEET.assignArea(MAP) #Assign subsystem to each car
FLEET.solve_subsystems(MAP, SOLUCIONES, N_STATIONS, N_STATES)
ts = time.time()
MIN_COST, FLEET_MIN = subsystem_distribution_gradientOptimization(FLEET, MAP, SOLUCIONES, N_STATIONS, N_STATES, C, E)
time_f = time.time() - ts
RESULT_GRADIENT[name_config] = dict()
RESULT_GRADIENT[name_config]["FLEET"] = FLEET_MIN
RESULT_GRADIENT[name_config]["POSITIONS"] = POSITIONS
RESULT_GRADIENT[name_config]["TIME"] = time_f
RESULT_GRADIENT[name_config]["SCORE"] = MIN_COST
save_obj(RESULT_GRADIENT, name)
save_obj(RESULT_GRADIENT, name)
print("MODELO GUARDADO")
name = "MAP" + i + "_POSITION" +j
try:
print(RESULTS_GENETIC[name])
except Exception as e:
POSITIONS = TEST_POSITIONS[j]
print("\n\tExecuting POSITIONS: ", POSITIONS)
ts = time.time()
POPULATION_X, solution = utils_genetic.solve_genetic_algorithm( N, PC, PM, MAX_ITERATIONS, MAP, N_STATIONS, AREA_SIZE, MAX_COBERTURE, N_CARS, N_STATES, SOLUCIONES, POSITIONS )
time = time.time() - ts
RESULTS_GENETIC[name] = dict()
RESULTS_GENETIC[name]["FLEET"] = POPULATION_X["FLEET"]
RESULTS_GENETIC[name]["POSITIONS"] = POSITIONS
RESULTS_GENETIC[name]["TIME"] = time
RESULTS_GENETIC[name]["SCORE"] = POPULATION_X["FLEET"].accumulated_cost
save_obj(RESULTS_GENETIC, "RESULTS_GENETIC")
"""
RESULT_ITERATIVE = dict()
for i in TEST_MAPS.keys():
MAP = TEST_MAPS[i]
print("Executing MAP: ",i)
for j in TEST_POSITIONS.keys():
POSITIONS = TEST_POSITIONS[j]
print("\n\tExecuting POSITIONS: ", POSITIONS)
ts = time.time()
subsytem_distribution_iterativeOptimization(N_ITERATION,FLEET, MAP, SOLUCIONES)
time = time.time() - ts
name = "MAP" + i + "_POSITION" +j
FLEET = Fleet(AREA_SIZE, MAX_COBERTURE, N_STATIONS)
# We assign a random station for each car
for j in range(N_CARS):
id_car = j
position = POSITIONS[id_car]
capacity = random.randint(
5, 20) #TO DO: each car have their on capcity
car = Car(id_car, position, capacity)
FLEET.insertCar(car)
FLEET.update_carsPosition(
) #Save car positions in FLEET.positions
MAP.update_available_stations(
FLEET
) #Remove car positio and car subsytem from available_stations
FLEET.assignArea(MAP) #Assign subsystem to each car
FLEET.solve_subsystems(MAP, SOLUCIONES, N_STATIONS, N_STATES)
ts = time.time()
MIN_COST, FLEET_MIN = subsytem_distribution_iterativeOptimization(
N_ITERATION, FLEET, MAP, SOLUCIONES, N_STATIONS, N_STATES)
time_f = time.time() - ts
RESULT_ITERATIVE[name] = dict()
RESULT_ITERATIVE[name]["FLEET"] = FLEET_MIN
RESULT_ITERATIVE[name]["POSITIONS"] = POSITIONS
RESULT_ITERATIVE[name]["TIME"] = time_f
RESULT_ITERATIVE[name]["SCORE"] = MIN_COST
save_obj(RESULT_ITERATIVE, name)
if SCORES[estado][area]["gradient_model"][
"min_time"] > execution_time:
SCORES[estado][area]["gradient_model"][
"min_time"] = execution_time
if SCORES[estado][area]["gradient_model"][
"max_time"] < execution_time:
SCORES[estado][area]["gradient_model"][
"max_time"] = execution_time
finalSOLUCIONES = countSOLUCIONES(SOLUCIONES)
print(
"---------------- Se ahn agregado {} soluciones NUEVAS---------------"
.format(startSOLUCIONES - finalSOLUCIONES))
utils_solver.save_obj(SCORES, 'SCORES_optimization')
"""
for estado in tqdm(n_estados, ascii= True, desc= "ITERATION TEST"):
for area in n_subsystems:
SOLUCIONES = dict()
#We are going to delete the stations that are over the 7 decil.
MAX_COBERTURE = round(estado*.7)
for iter in range(SECUENTIAL_ITERATIONS):
#The FLEET.position (position of each car) and the System must be constant for the optimization of S
#but they have to change for each iter in SECUENTIAL_ITERATIONS.
MAP = Map(N_STATIONS, N_STATES) #Always the same size.
FLEET = Fleet(area, MAX_COBERTURE)
MAP.update_available_stations(FLEET) #Remove car positio and car subsytem from available_stations