def run_algorithm(self):
val_0 = int(self.w.val_0.value())
val_1 = int(self.w.val_1.value())
val_2 = int(self.w.val_2.value())
val_3 = int(self.w.val_3.value())
val_4 = int(self.w.val_4.value())
val_5 = int(self.w.doubleSpinBox.value())
print(val_0, val_1, val_2)
print(val_5)
#self.w.text.append("CLicked with values %d, %d , %d " % (val_0, val_1, val_2))
if self.type == 0: #branch and bound
pass
elif self.type == 1: #NEH
pass
elif self.type == 2: #NEH Amelioree
pass
elif self.type == 3: #CDS
pass
elif self.type == 4: #Algorithme genetique
sequence, makespan = solve_problem.solve_benchmark_problem(
val_0, val_2, val_1, val_3, val_4, val_5)
elif self.type == 5: #VNS
pass
elif self.type == 6: #Hyper Heuristqiue
pass
elif self.type == 7: #Palmer
pass
print(sequence, makespan)
self.w.label_resultat.setText(str(sequence))
self.w.timeField.display(makespan)
def test_AG(instance, job, machine):
print("probleme ", instance, job, machine, " : \n")
makespan_list = []
# toutes les combinaisons possibles de probabilites et de taille population et taille generation
for p in list_probabilite:
for o in list_population:
for g in list_generation:
sequence, makespan = solve_problem.solve_benchmark_problem(
instance, job, machine, o, g, p)
makespan_list.append(makespan)
print("mutations probabilite: ", p, "generation", g, "population", o)
print(makespan_list)
print(np.min(makespan_list))
def AG_study():
#on parcours tous les problemes
for i in range(1, 10):
#on parcours tous les tailles d'instances
for j in list_machine:
for k in list_job:
print("probleme ", i, j, k, " : \n")
makespan_list = []
#toutes les combinaisons possibles de probabilites et de taille population et taille generation
for p in list_probabilite:
for o in list_population:
for g in list_generation:
sequence, makespan = solve_problem.solve_benchmark_problem(
i, k, j, o, g, p)
makespan_list.append(makespan)
print(makespan_list)
print(np.min(makespan_list))
def run_main(problem):
jobs, machines, timeseed, prev_best_makespan = read_problem(
taillard, problem)
best_sequence, best_makespan = \
solve_benchmark_problem(problem, jobs, machines, timeseed, show_matrix=False)
best_sequence = list(map(int, best_sequence))
best_sequence = list(map(str, best_sequence))
best_sequence = ', '.join(best_sequence)
print("Jobs: ", jobs, "\nMachines: ", machines, "\nTimeseed: ", timeseed,
"\nOptimal sequence: ", best_sequence, "\nOptimal makespan: ",
best_makespan)
if best_makespan < prev_best_makespan:
write_new_best(problem,
best_sequence,
best_makespan,
taillard,
taillard_file,
benchmark_file=benchmark_file)
def test_AG(instance, job, machine):
print("probleme ", instance, job, machine, " : \n")
makespan_list = []
min = 10000000
population = 0
generation = 0
proba = 0
makespan_parameter = {}
# toutes les combinaisons possibles de probabilites et de taille population et taille generation
for p in list_probabilite:
for o in list_population:
for g in list_generation:
sequence, makespan = solve_problem.solve_benchmark_problem(
instance, job, machine, o, g, p)
if makespan < min:
min = makespan
population = o
generation = g
proba = p
# makespan_parameter.append({makespan:[p,o,g]})
print("mutations probabilite: ", p, "generation", g,
"population", o, "resultat", makespan)
"""print(makespan_list)
minimum = np.min(makespan_list)
print(makespan_parameter.fromkeys(minimum))
"""
row = []
row.append(str(instance) + str(machine) + str(job) + ".txt")
row.append(sequence)
row.append(min)
row.append(proba)
row.append(population)
row.append(generation)
print(row)
with open('resultat5.csv', 'a') as csvFile:
writer = csv.writer(csvFile)
writer.writerow(row)
csvFile.close()
def run_algorithm(self):
val_0 = int(self.w.val_0.value())
val_1 = int(self.w.val_1.value())
val_2 = int(self.w.val_2.value())
val_3 = int(self.w.val_3.value())
val_4 = int(self.w.val_4.value())
val_5 = int(self.w.doubleSpinBox.value())
print(val_0, val_1, val_2)
print(val_5)
# get the file name for NEH amelioree and CDS
file_name = str(self.w.val_0.value()) + str(
self.w.val_1.value()) + str(self.w.val_2.value()) + ".txt"
open("instance/" + file_name, 'r')
# self.w.text.append("CLicked with values %d, %d , %d " % (val_0, val_1, val_2))
if self.type == 0: # branch and bound
pass
elif self.type == 1: # NEH
sequence2 = sb.check_output(
['neh.exe', 'instance/' + str(file_name)])
sequence = sequence2.decode("utf-8")
x = sequence.split(",")
makespan = x[self.w.val_2.value()]
pass
elif self.type == 2: # NEH Amelioree
a = 'NehAmelioree.exe instance/' + str(file_name)
sequence = sb.getoutput(str(a))
# get the makespan from the sequence string
x = sequence.split(",")
makespan = x[self.w.val_2.value()]
elif self.type == 3: # CDS
a = 'cds.exe instance/' + str(file_name)
sequence = sb.getoutput(str(a))
# get the makespan from the sequence string
x = sequence.split(",")
makespan = x[self.w.val_2.value()]
elif self.type == 4: # Algorithme genetique
sequence, makespan = solve_problem.solve_benchmark_problem(
val_0, val_2, val_1, val_3, val_4, val_5)
elif self.type == 5: # VNS
sequence2 = sb.check_output(
['neh.exe', 'instance/' + str(file_name)])
sequence3 = sb.check_output([
'vns.exe', 'instance/' + str(file_name),
str(val_0),
sequence2.decode("utf-8"), "0",
str(val_1),
str(int(val_2))
])
sequence = sequence3.decode("utf-8")
x = sequence.split(",")
makespan = x[self.w.val_2.value()]
elif self.type == 6: # Hyper Heuristqiue
res = sb.getoutput('python hyper.py' + ' instance/' + file_name)
print(res)
res = res.split(',')
sequence = res[:-1]
# Convert them to a list of int
# sequence = list(map(int, sequence))
makespan = res[-1]
elif self.type == 7: # Palmer
res = sb.getoutput('python Palmer_s_Heurtistic.py' + ' instance/' +
file_name).split(',')
sequence = res[:-1]
# Convert them to a list of int
sequence = list(map(int, sequence))
makespan = res[-1]
print(sequence, makespan)
self.w.label_resultat.setText(str(sequence))
self.w.timeField.display(makespan)