def insert(self, data, t_convey, total_time):
neh_data = np.zeros([data.shape[0], 2], dtype=data.dtype)
neh_data[:, :2] = data[:, np.array(total_time[0, :2], dtype=int) - 1]
for k in range(2, data.shape[1]):
temp = data[:, int(total_time[0, k] - 1)]
neh_data = np.insert(neh_data, k, temp, axis=1)
for i in range(k):
if makespan_value(neh_data, t_convey) > makespan_value(
np.insert(neh_data, i, temp, axis=1), t_convey):
neh_data = np.delete(neh_data, k, axis=1)
neh_data = np.insert(neh_data, i, temp, axis=1)
data_neh = neh_data[0]
return data_neh
def select(self, data, transfer_time, data_johnson):
data_johnson = np.array(data_johnson, dtype=int) - 1
data_best = data_johnson[0]
for i in range(1, data_johnson.shape[0]):
if makespan_value(
data[:, data_best],
transfer_time,
) > makespan_value(
data[:, data_johnson[i]],
transfer_time,
):
data_best = data_johnson[i]
data_best += 1
return data_best
def ra(data, transfer_time, draw=0):
"""
:param data: n行m列，第一行工序编号，其他是加工时间
:return:
"""
data = data[:, np.argsort(data[0])]
new = RA()
start_time = time.time()
group_data = new.group_machine(data)
ra_data = new.apply_johnson(group_data)
end_time = time.time()
# print("Time used: %s" % (end_time - start_time))
# print("The minimum makespan: %s" % makespan_value(data[:, ra_data - 1]))
if draw:
import matplotlib.pyplot as plt
from tool import gatt
gatt(data[:, ra_data - 1])
plt.show()
return ra_data, makespan_value(data[:, ra_data - 1], transfer_time)
def neh(data, t_convey, draw=0):
"""
:param data: n行m列，第一行工序编号，其他是加工时间
:return:
"""
data = data[:, np.argsort(data[0])]
new = NEH()
start_time = time.time()
total_time = new.sort(data)
data_neh = new.insert(data, t_convey, total_time)
end_time = time.time()
# print("Time used: %s" % (end_time - start_time))
# print("The minimum makespan: %s" % makespan_value(data[:, data_neh - 1]))
if draw:
import matplotlib.pyplot as plt
from tool import gatt
gatt(data[:, data_neh - 1])
plt.show()
return data_neh, makespan_value(data[:, data_neh - 1], t_convey)
def gupta(data, transfer_time, draw=0):
"""
:param data:3行，工序编号，机器1加工时间，机器2加工时间
:return:
"""
data = data[:, np.argsort(data[0])]
new = Gupta()
start_time = time.time()
s = new.cals(data)
data_gupta = new.sort(data, s)
end_time = time.time()
# print("Gupta// Time used: %s" % (end_time - start_time))
# print("Gupta// The minimum makespan: %s" % makespan_value(data[:, data_gupta - 1]))
if draw:
import matplotlib.pyplot as plt
from tool import gatt
gatt(data[:, data_gupta - 1])
plt.show()
return data_gupta, makespan_value(data[:, data_gupta - 1], transfer_time)
def palmer(data, transfer_time, draw=0):
"""
:param data: n行m列，第一行工序编号，其他是加工时间
:return:
"""
data = data[:, np.argsort(data[0])]
new = Palmer()
start_time = time.time()
slope_index = new.slope_index(data)
sort_slope = new.sort_slope(slope_index)
palmer_data = new.palmer_data(data, sort_slope)
end_time = time.time()
# print("Palmer// Time used: %s" % (end_time - start_time))
# print("Palmer// The minimum makespan: %s" % makespan_value(data[:, palmer_data - 1]))
if draw:
import matplotlib.pyplot as plt
from tool import gatt
gatt(data[:, palmer_data - 1])
plt.show()
return palmer_data, makespan_value(data[:, palmer_data - 1], transfer_time)
def cds(data, transfer_time, draw=0):
"""
:param data: n行m列，第一行工序编号，其他是加工时间
:return:
"""
data = data[:, np.argsort(data[0])]
new = CDS()
start_time = time.time()
data_group = new.group(data)
data_johnson = new.johnson(data_group)
data_best = new.select(data, transfer_time, data_johnson)
end_time = time.time()
# print("CDS// Time used: %s" % (end_time - start_time))
# print("CDS// The minimum makespan: %s" %makespan_value(data[:, data_best - 1]))
if draw:
import matplotlib.pyplot as plt
from tool import gatt
gatt(data[:, data_best - 1])
plt.show()
return data_best, makespan_value(data[:, data_best - 1], transfer_time)
def ga_fsp_new(data,
c_op,
op_duetime,
c_od,
batch_packingtime,
t_convey,
pop_size=80,
max_gen=300,
Pc=0.65,
Pm=0.35,
draw=222):
"""
流水车间作业调度的改进遗传算法。
新增精英保留机制，即将每次迭代及其之前的最优个体保留下来
轮盘赌选、部分匹配交叉混合顺序交叉
:param data: m行n列，第1行工序编号，值加工时间
:param pop_size: 种群大小
:param max_gen: 最大进化代数
:param Pc: 交叉概率
:param Pm: 变异概率
:param draw:甘特图、适应度图、动态适应度图
:return:
"""
data = data[:, np.argsort(data[0])]
# pop_size=max(data.shape[1]*12,80)
pop_size = 80
# max_gen=max(data.shape[1]*40,200)
max_gen = 800
new = GA_FSP_NEW(data, pop_size, max_gen, Pc, Pm)
pop = new.crtp()
pop_trace = np.zeros([max_gen, 3])
genetic_trace = np.zeros([max_gen, data.shape[1]], dtype=int)
start_time = time.time()
for g in range(max_gen):
fitness = new.fitness(pop, c_op, op_duetime, c_od, batch_packingtime,
t_convey)
pop_trace[g] = [g, np.mean(fitness), np.max(fitness)]
genetic_trace[g] = pop[np.argmax(fitness)]
select = new.select(pop, fitness)
crossover = new.crossover(select)
pop = new.mutation(crossover)
end_time = time.time()
best_genetic = genetic_trace[np.argmax(pop_trace[:, 2])]
total_best = np.where(pop_trace[:, 2] == np.max(pop_trace[:, 2]))[0]
print("GA Time used:", (end_time - start_time))
# print("The first best generation：%s" % np.argmax(pop_trace[:, 2]))
# print("Best generations：%s" % total_best)
# print("Numbers of best generation：%s" % total_best.shape[0])
print("The minimum makespan: %s" %
makespan_value(data[:, best_genetic], t_convey)) #输出最大完成时间
if draw != 222:
import matplotlib.pyplot as plt
from tool import gatt
if int(str(draw)[0]) != 2:
plt.figure(1)
gatt(data[:, best_genetic], t_convey)
plt.show()
if int(str(draw)[1]) != 2:
plt.figure(2)
plt.plot(pop_trace[:, 0],
pop_trace[:, 2],
"r-",
label=r"$Best$ $fitness$")
# plt.plot(pop_trace[:, 0], pop_trace[:, 1], "b-", label=r"$Pop$ $fitness$")
plt.xlabel(r"$Generation_i$")
plt.ylabel(r"$Fitness$")
plt.legend()
plt.show()
if int(str(draw)[2]) != 2:
plt.ioff()
for i in range(1, max_gen):
plt.figure(2)
plt.plot([pop_trace[i - 1, 0], pop_trace[i, 0]],
[pop_trace[i - 1, 2], pop_trace[i, 2]],
"r-",
label=r"$Best$ $fitness$")
plt.plot([pop_trace[i - 1, 0], pop_trace[i, 0]],
[pop_trace[i - 1, 1], pop_trace[i, 1]],
"b-",
label=r"$Pop$ $fitness$")
plt.xlabel(r"$Generation$")
plt.ylabel(r"$Fitness$")
plt.pause(0.01)
plt.show()
best_genetic += 1
return best_genetic