def check_current_joins_three_two_edges_case1_next_2_l1_cN(self, l0_col_second, l0_col, l1_col):
next_row = 2
cds = CDS(self.session, 3, 2)
self.session.run(tf.global_variables_initializer())
cds.initialize(["l0_c0", "l0_c1", "l0_c2"])
cds.joins([(0, l0_col)], (1, l1_col), "l1_c0")
cds.joins([(0, l0_col_second), (1, l1_col)], (next_row, 0), "next")
return cds
def run(self):
machine_count, job_count = self.data.shape
if self.method=="B&B":
#do somethign
print('BB')
print(self.data)
elif self.method=="CDS":
start = time()
result = CDS(self.data)
end = time()
self.label.setText(bend(50,'{}\nOrdre : {}\nMakespan : {}\nTemps d\'execution : {:.6}s'.format(self.label.text(),result[0],result[1],end - start)))
plotGantt(self.data,result[0],"CDS",job_count)
elif self.method=="NEH":
start = time()
result = neh(self.data)
end = time()
self.label.setText(bend(50,'{}\nOrdre : {}\nMakespan : {}\nTemps d\'execution : {:.6}s'.format(self.label.text(),result[0],result[1],end - start)))
plotGantt(self.data,result[0],"NEH",job_count)
elif self.method=="GENETIC":
population_size = 50
# Generer une les individus de la population aleatoirement
initPop = [sample(list(range(0, job_count)), job_count) for _ in range(0, population_size)] # same repeated individual
start = time()
result = genetic(self.data, initPop, population_size, 0.1, 200)
end = time()
self.label.setText(bend(50,'{}\nOrdre : {}\nMakespan : {}\nTemps d\'execution : {:.6}s'.format(self.label.text(),result[0],result[1],end - start)))
plotGantt(self.data,result[0],"Genetic",job_count)
elif self.method=="RECUIT":
start = time()
result = simulated_annealing(self.data, Ti = 790,Tf = 3 ,alpha = 0.93)
end = time()
self.label.setText(bend(50,'{}\nOrdre : {}\nMakespan : {}\nTemps d\'execution : {:.6}s'.format(self.label.text(),result[0],result[1],end - start)))
plotGantt(self.data,result[0],"Genetic",job_count)
elif self.method=="HYBRID":
population_size = 50
# Generer une les individus de la population aleatoirement
initPop = [sample(list(range(0, job_count)), job_count) for _ in range(0, population_size)] # same repeated individual
start = time()
result = genetic_rt(self.data, initPop, population_size, 0.1, 200)
end = time()
self.label.setText(bend(50,'{}\nOrdre : {}\n Makespan : {}\nTemps d\'execution : {:.6}s'.format(self.label.text(),result[0],result[1],end - start)))
plotGantt(self.data,result[0],"Hybrid",job_count)
def check_current_joins_three_one_edge_case5(self, next_row): cds = CDS(self.session, 3, 2) self.session.run(tf.global_variables_initializer()) cds.initialize(["l0_c0", "l0_c1", "l0_c2"]) cds.joins([(0, 1)], (next_row, 1), "next") self.assertEqual([((0, 0), "l0_c0"), ((next_row, 1), "next"), ((0, 2), "l0_c2")], cds.current())
def test_current_joins_initialize_three(self): cds = CDS(self.session, 3, 1) self.session.run(tf.global_variables_initializer()) cds.initialize(["l0_c0", "l0_c1", "l0_c2"]) self.assertEqual([((0, 0), "l0_c0"), ((0, 1), "l0_c1"), ((0, 2), "l0_c2")], cds.current())
def check_current_joins_three_three_edges_case1(self, to_column): cds = CDS(self.session, 3, 2) self.session.run(tf.global_variables_initializer()) cds.initialize(["l0_c0", "l0_c1", "l0_c2"]) cds.joins([(0, 0), (0, 1), (0, 2)], (1, to_column), "next") self.assertEqual([((1, to_column), "next")], cds.current())
def test_current_joins_initialize_one(self): cds = CDS(self.session, 1, 1) self.session.run(tf.global_variables_initializer()) cds.initialize(["l0_c0"]) self.assertEqual([((0, 0), "l0_c0")], cds.current())
def test_current_empty(self): cds = CDS(self.session, 1, 1) self.session.run(tf.global_variables_initializer()) self.assertEqual([], cds.current())
def test_depth_is_set(self): self.assertEqual(1, CDS(self.session, 1, 1).depth)
def test_length_is_set(self): self.assertEqual(1, CDS(self.session, 1, 1).length)
def test_session_is_set(self): self.assertEqual(self.session, CDS(self.session, 1, 1).session)
def test_create(self): assert( CDS(self.session, 1,1) )
nbIterAG = []
nbIterHY = []
for nbOfJobs in [20, 50, 100]:
for instance in [1, 2]:
path = './data/ta{}_10_{}.txt'.format(nbOfJobs, instance)
matrice = dataReader.read(path, 10)
matrice = np.array(matrice)
machine_count, job_count = matrice.shape
print('{} machines et {} jobs'.format(machine_count, job_count))
print('Algorithme CDS :')
start = time()
result = CDS(matrice)
end = time()
print(' Ordre : {}'.format(result[0]))
print(' Makespan : {}'.format(result[1]))
print(' Temps d\'execution : {:.6}s\n'.format(end - start))
tempsCDS.append(end - start)
makespanCDS.append(result[1])
print('Algorithme NEH :')
start = time()
result = neh(matrice)
end = time()
print(' Ordre : {}'.format(result[0]))
print(' Makespan : {}'.format(result[1]))
print(' Temps d\'execution : {:.6}s\n'.format(end - start))
tempsNEH.append(end - start)
