def main():
args = parse_args()
echo_args(args)
vrp = VRP(*read_file(os.path.abspath(args.input)))
game = GameFactory.create_game(args.game)
cost, routes, dist = ga_social_interaction_vrp(
vrp,
game,
population_size=args.pop_size,
num_generations=args.num_gens,
mutation_rate=args.mut_rate,
crossover_rate=args.cro_rate,
wgt_solution=1 - args.wgt_social,
wgt_social=args.wgt_social,
debug=args.debug)
solution = {
'input':
os.path.basename(args.input),
'total distance':
cost,
'routes': [{
f'route {i+1}': [l + 1 for l in r]
} for i, r in enumerate(routes)]
}
if args.debug:
print(json.dumps(solution))
if args.output:
with open(args.output, 'w') as outfile:
json.dump(solution, outfile)
def update_fitness(population: List[Individual], vrp: VRP, game: Game,
wgt_solution: float, wgt_social: float):
max_solution_fitness = -999999
max_social_fitness = -999999
for idv_1, idv_2 in random_pairs(population):
main_chromosome_1, strategy_chromosome_1 = idv_1.chromosomes()
main_chromosome_2, strategy_chromosome_2 = idv_2.chromosomes()
# solution fitness (route costs)
solution_fitness_1 = vrp.total_distance(main_chromosome_1)
solution_fitness_2 = vrp.total_distance(main_chromosome_2)
# social interaction fitness (payoffs from games)
social_fitness_1, social_fitness_2 = game.play(strategy_chromosome_1,
strategy_chromosome_2)
# set solution and social fitness
idv_1.update_fitness_parts(solution_fitness_1, social_fitness_1)
idv_2.update_fitness_parts(solution_fitness_2, social_fitness_2)
# update max solution and social fitness (used for normalization)
max_solution_fitness = max(max_solution_fitness,
max(solution_fitness_1, solution_fitness_2))
max_social_fitness = max(max_social_fitness,
max(social_fitness_1, social_fitness_2))
# update total fitness using weights and max fitness terms
for idv in population:
idv.update_total_fitness(max_solution_fitness, max_social_fitness,
wgt_solution, wgt_social)
def vrp():
mat, capacity, cities_nb, vehicules_nb, demand_matrix, coords = get_mat()
#VRP
vrp = VRP(vehicules_nb,cities_nb)
if mat is None:
vrp.create_data_model()
else:
vrp.pass_matrix(mat)
print(vrp.data)
vrp_solve(vrp)
def call_stats_dev(arg1):
print('VRP ou CVRP : ', end='')
vrp_type = input()
if vrp_type.upper() != 'VRP' and vrp_type.upper() != 'CVRP':
raise Exception("Type de VRP non reconnu")
dataset_name = None
print('Chemin vers le fichier contenant les path : ', end='')
path = input()
if(os.path.isfile(path) == False):
raise Exception("Chemin invalide")
allPath = get_all_path(path)
for line in allPath:
dataset_name = os.path.basename(line[0])
mat, capacity, cities_nb, vehicules_nb, demand_matrix, coords = from_file_to_adj_matr(line[0])
if vrp_type.upper() == 'VRP':
# VRP
vrp = VRP(vehicules_nb,cities_nb)
vrp.pass_matrix(mat)
elif vrp_type.upper() == 'CVRP':
# CVRP
vrp = CVRP(vehicules_nb,cities_nb)
vrp.pass_matrix(mat, demand_matrix,capacity)
path_cost = line[1]
if(os.path.isfile(path_cost) == False):
raise Exception("Chemin invalide")
cost = get_particular_info(path_cost, 'cost')
print(line[0], line[1])
execution_quality_cities(algos_metaheuristic, vrp, cities_nb, dataset_name, cost)
def ga_social_interaction_vrp(vrp: VRP,
game: Game,
population_size: int,
num_generations: int,
mutation_rate=0.7,
crossover_rate=0.7,
wgt_solution=0.5,
wgt_social=0,
debug=False):
population = random_population(vrp, game, population_size)
for gen in range(num_generations):
update_fitness(population, vrp, game, wgt_solution, wgt_social)
offspring = []
for i in range(population_size // 2):
p1 = tournament_select(population)
p2 = tournament_select(population)
if random.random(
) < crossover_rate and p1._main_chromosome != p2._main_chromosome:
c1, c2 = reproduce(p1, p2)
c1 = mutate(c1, mutation_rate)
c2 = mutate(c2, mutation_rate)
offspring.append(c1)
offspring.append(c2)
update_fitness(offspring, vrp, game, wgt_solution, wgt_social)
population = replace(population, offspring)
if debug and gen % 100 == 0:
print(fittest_solution(population))
fittest_stn = fittest_solution(population)
return (fittest_stn._solution_fitness, [
route for route in vrp.decode_routes(fittest_stn._main_chromosome)
], list(map(lambda x: x._strategy_chromosome, population)))
#from ..vrp import VRP
import sys
sys.path.append("..")
from vrp import VRP
k = 5 # number of vehicles
############# DFJ ###############
vrp_dfj = VRP()
vrp_dfj.setup_preset_data(file_name="../validation_data_A/A-n32-k5.vrp",
number_of_vehicles=k)
vrp_dfj.gap_goal = 0.1
vrp_dfj.subtour_type = 'DFJ'
vrp_dfj.setup()
# vrp_dfj.model.Params.TimeLimit = 5*60
vrp_dfj.model.Params.Threads = 2
vrp_dfj.optimize()
vrp_dfj.visualize()
if input("continue? ").lower() not in ['y', 'yes']:
sys.exit()
############## MTZ ###############
vrp_mtz = VRP()
vrp_mtz.setup_preset_data(file_name="../validation_data_A/A-n32-k5.vrp",
number_of_vehicles=k)
vrp_mtz.gap_goal = 0.1
#from ..vrp import VRP
import sys
sys.path.append("..")
import numpy as np
from vrp import VRP
n = 5 # number of customers
k = 1 # number of vehicles
Q = 32
############## DFJ ###############
vrp_dfj = VRP()
vrp_dfj.setup_random_data(number_of_customers=n,
number_of_vehicles=k,
vehicle_capacity=Q,
demand_lower=1,
demand_higher=10,
seed=420)
vrp_dfj.gap_goal = 0.1
vrp_dfj.subtour_type = 'DFJ'
vrp_dfj.setup()
vrp_dfj.optimize()
vrp_dfj.visualize()
if input("continue?").lower() not in ['y', 'yes']:
sys.exit()
def call_stats(arg1):
print('VRP ou CVRP : ', end='')
vrp_type = input()
if vrp_type.upper() != 'VRP' and vrp_type.upper() != 'CVRP':
raise Exception("Type de VRP non reconnu")
dataset_name = None
if arg1.upper()== "TIMECITIESQ":
random = False
else:
random = query_yes_no("Générer une matrice aléatoirement ?")
if not random:
print('Chemin vers le fichier : ', end='')
path = input()
if(os.path.isfile(path) == False):
raise Exception("Chemin invalide")
dataset_name = os.path.basename(path)
mat, capacity, cities_nb, vehicules_nb, demand_matrix, coords = from_file_to_adj_matr(path)
else:
print('Entrez le nombre de villes : ', end='')
cities_nb = int(input())
print('Entrez le nombre de véhicules : ', end='')
vehicules_nb = int(input())
dataset_name = "random-k" + str(vehicules_nb) + "-n" + str(cities_nb)
if vrp_type.upper() == 'VRP':
# VRP
vrp = VRP(vehicules_nb,cities_nb)
if random:
vrp.create_data_model()
else:
vrp.pass_matrix(mat)
elif vrp_type.upper() == 'CVRP':
# CVRP
vrp = CVRP(vehicules_nb,cities_nb)
if random:
vrp.create_data_model()
else:
vrp.pass_matrix(mat, demand_matrix,capacity)
if arg1.upper()== "TIMESOLUTIONS":
solutionsLimitArray = []
while True:
print('Appuyez sur enter pour arreter l ajout de solution... ')
print('Entrez un nombre dans le tableau de solutions locales max : ', end='')
tempLimit = input()
if tempLimit.isnumeric():
solutionsLimitArray.append(int(tempLimit))
else:
print("Fin de l'entrée, tableau : " , solutionsLimitArray)
break
execution_time_solutions(algos_metaheuristic, vrp, solutionsLimitArray, dataset_name)
elif arg1.upper()== "TIMEVEHICULES":
for i in tqdm(range(vehicules_nb)):
execution_time_vehicules(algos_metaheuristic, vrp, i, dataset_name)
elif arg1.upper()== "TIMECITIES":
for i in tqdm(range(cities_nb)):
execution_time_cities(algos_metaheuristic, vrp, i, dataset_name)
elif arg1.upper()== "TIMECITIESQ":
print('Chemin vers le fichier de la solution: ', end='')
path_cost = input()
if(os.path.isfile(path_cost) == False):
raise Exception("Chemin invalide")
cost = get_particular_info(path_cost, 'cost')
execution_quality_cities(algos_metaheuristic, vrp, cities_nb, dataset_name, cost)
print('Appuyez sur entrée pour continuer...')
input()
from vrp import VRP
from cvrp import CVRP
filename = 'PL'
if __name__ == '__main__':
print("\nVRP: ")
vrp = VRP(filename)
vrp.calculate_best_base_location()
print("\nCVRP: ")
cvrp = CVRP(filename)
cvrp.calculate_best_base_location()
from vrp import VRP
vrp = VRP()
random = True
formulation = ["capacitated", "timed"][1]
subtour_type = ['DFJ', 'MTZ'][0]
n = 15 # number of customers
k = 5 # number of vehicles
Q = 50
goal_gap = 0.0
plot_all_nodes = False
vrp.gap_goal = goal_gap
if formulation is "capacitated":
if random:
vrp.subtour_type = subtour_type
vrp.setup_random_data(number_of_customers=n,
number_of_vehicles=k,
vehicle_capacity=Q,
x_range=20,
y_range=20,
demand_lower=1,
demand_higher=10,
seed=420)
else:
vrp.setup_preset_data(file_name="validation_data_A/A-n32-k5.vrp",