def main(file):
instance = Suppliers_MRCPSP.Instance()
file_temp = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
f_p = file
f_CN = file_temp+"\data\CNfks.xlsx"
f_O =file_temp+ "\data\Ofs.xlsx"
qfs =file_temp+ "\data\qfs.xlsx"
n=0
# M=[1, 3, 3, 3, 1, 3, 2, 3, 2, 2, 3, 2, 3, 2, 3, 1]
# starttime=[0, 12, 2, 4, 35, 27, 42, 44, 55, 54, 67, 43, 64, 78, 53, 91]
instance.loadData(file_project=f_p, file_CNfks=f_CN, file_Ofs=f_O, file_qfs=qfs)
supplier=instance.initial_Supplier()
start_time=time.time()
for i in range(50):
ind=GA_Suppliers_MRCPSP.GA_RCPSP_ruba(instance,50,supplier)
sup,ind=GA_Suppliers_MRCPSP.GA_RCPSP_cost(instance,20,ind,i)
# sup, ind = GA_Suppliers_MRCPSP.Greedy_cost(instance, ind)
supplier=sup
end_time=time.time()
T=end_time-start_time
cost=[instance.project_cost(instance.best_ind[g],instance.best_sup[g],instance.best_ind[g].R_order_time) for g in range(len(instance.best_sup))]
best_cost=min(cost)
best_index=cost.index(best_cost)
print(cost)
best_S=instance.best_sup[best_index]
best_I=instance.best_ind[best_index]
best_T=instance.time[best_index]
print( [instance.time[i]-start_time for i in range(len(instance.time))])
ruba=instance.project_ruba(best_I,best_S)
finishtime=[]
duration=[]
for i in range(len(best_I.M)):
finishtime.append(best_I.starttime[i] + instance.job_model_duration[i + 1][best_I.M[i]])
duration.append(instance.job_model_duration[i + 1][best_I.M[i]])
doc=open("best_Bi-GA_J16.txt","w")
print("-------------------最终结果----------------------",file=doc)
print("寻找最优解时间：",best_T-start_time,file=doc)
print("总算法时间：",T,file=doc)
print("supplier",best_S,file=doc)
print("order",best_I.order,file=doc)
print("M",best_I.M, file=doc)
print("starttime",best_I.starttime, file=doc)
print("finishtime", finishtime, file=doc)
print("duration", duration, file=doc)
print("order_time",best_I.R_order_time, file=doc)
best=instance.project_cost(best_I,best_S,best_I.R_order_time)
print("best_cost",best,file=doc)
print("best_ruba",ruba,file=doc)
doc.close()
def main(file):
instance = Suppliers_MRCPSP.Instance()
f_p = file
f_CN = "./data/CNfks.xlsx"
f_O = "./data/Ofs.xlsx"
qfs = "./data/qfs.xlsx"
n = 0
# M=[1, 3, 3, 3, 1, 3, 2, 3, 2, 2, 3, 2, 3, 2, 3, 1]
# starttime=[0, 12, 2, 4, 35, 27, 42, 44, 55, 54, 67, 43, 64, 78, 53, 91]
instance.loadData(file_project=f_p,
file_CNfks=f_CN,
file_Ofs=f_O,
file_qfs=qfs)
supplier = instance.initial_Supplier()
for i in range(500):
ind = GA_Suppliers_MRCPSP.GA_RCPSP_ruba(instance, 100, supplier)
# sup,ind=GA_Suppliers_MRCPSP.GA_RCPSP_cost(instance,1,ind,i)
sup, ind = GA_Suppliers_MRCPSP.Greedy_cost(instance, ind)
supplier = sup
cost = [
instance.project_cost(instance.best_ind[g], instance.best_sup[g],
instance.best_ind[g].R_order_time)
for g in range(len(instance.best_sup))
]
best_cost = min(cost)
best_index = cost.index(best_cost)
print(cost)
best_S = instance.best_sup[best_index]
best_I = instance.best_ind[best_index]
ruba = instance.project_ruba(best_I, best_S)
finishtime = []
duration = []
for i in range(len(best_I.M)):
finishtime.append(best_I.starttime[i] +
instance.job_model_duration[i + 1][best_I.M[i]])
duration.append(instance.job_model_duration[i + 1][best_I.M[i]])
doc = open("best.txt", "w")
print("-------------------最终结果----------------------", file=doc)
print("supplier", best_S, file=doc)
print("order", best_I.order, file=doc)
print("M", best_I.M, file=doc)
print("starttime", best_I.starttime, file=doc)
print("finishtime", finishtime, file=doc)
print("duration", duration, file=doc)
print("order_time", best_I.R_order_time, file=doc)
best = instance.project_cost(best_I, best_S, best_I.R_order_time)
print("best_cost", best, file=doc)
print("best_ruba", ruba, file=doc)
doc.close()
def mutate_ruba(ins, pop, pop_Supplier):
offsprings = []
for ind in pop:
if random.random() < ins.mutate_pb:
move_job1 = random.choice(ind.order[1:14])
ava_move_job = []
order_copy = copy.copy(ind.order)
index_pre = []
for i in ins.job_predecessors[move_job1 - 1]:
index_pre.append(order_copy.index(i))
premove_maxindex = max(index_pre)
index_aft = []
for j in ins.job_successors[move_job1 - 1]:
index_aft.append(order_copy.index(j))
aftmove_minindex = min(index_aft)
position = random.randint(premove_maxindex + 1,
aftmove_minindex - 1)
ind.order.insert(position, move_job1)
if position < order_copy.index(move_job1):
del ind.order[order_copy.index(move_job1) + 1]
else:
del ind.order[order_copy.index(move_job1)]
offspring_order = ind.order
mutate_job = random.randint(1, 15)
while True:
a = random.choice([1, 2, 3])
if a != ind.M[mutate_job]:
break
ind.M[mutate_job] = a
offspring_M = ind.M
starttime, finishtime = ins.job_start_time(ind.order, ind.M)
offspring_order_time = ins.initial_ordertime(
starttime, offspring_M, pop_Supplier)
offspring = Suppliers_MRCPSP.Indivadul(offspring_order,
offspring_M, starttime,
offspring_order_time)
offsprings.append(offspring)
else:
offsprings.append(ind)
return offsprings
def mate_ruba(ins, pop, plist, pop_Supplier):
new_offspring = []
offspring_p_list = list(zip(pop, plist))
for i in range(int(ins.popSize / 2)):
sum1_p = 0
for ind1, p in offspring_p_list:
sum1_p += p
if random.random() < sum1_p: break
child_order1 = ind1.order
child_M1 = ind1.M
sum2_p = 0
for ind2, p in offspring_p_list:
sum2_p += p
if random.random() < sum2_p: break
child_order2 = ind2.order
child_M2 = ind2.M
#order
if random.random() < ins.mate_pb:
offspring_order1 = [0 for _ in range(len(child_order1))]
offspring_order2 = [0 for _ in range(len(child_order2))]
x = sorted(random.sample(range(len(child_order1)), 2))
for i in range(x[0]):
offspring_order1[i] = child_order1[i]
offspring_order2[i] = child_order2[i]
for j in range(x[1], len(child_order1)):
offspring_order1[j] = child_order1[j]
offspring_order2[j] = child_order2[j]
pos1 = x[0]
pos2 = x[0]
for gene1 in child_order2:
if gene1 not in offspring_order1:
offspring_order1[pos1] = gene1
pos1 += 1
for gene2 in child_order1:
if gene2 not in offspring_order2:
offspring_order2[pos2] = gene2
pos2 += 1
else:
offspring_order1 = child_order1
offspring_order2 = child_order2
#M
if random.random() < ins.mate_pb:
offspring_M1 = [0 for _ in range(len(child_order1))]
offspring_M2 = [0 for _ in range(len(child_order2))]
x = random.randint(1, 15)
for i in range(x):
offspring_M1[i] = child_M1[i]
offspring_M2[i] = child_M2[i]
for j in range(x, len(child_M1)):
offspring_M1[j] = child_M2[j]
offspring_M2[j] = child_M1[j]
else:
offspring_M1 = child_M1
offspring_M2 = child_M2
#order time
start_time1, finish_time1 = ins.job_start_time(offspring_order1,
offspring_M1)
start_time2, finish_time2 = ins.job_start_time(offspring_order2,
offspring_M2)
offspring_order_time1 = ins.initial_ordertime(start_time1,
offspring_M1,
pop_Supplier)
offspring_order_time2 = ins.initial_ordertime(start_time2,
offspring_M2,
pop_Supplier)
new_ind1 = Suppliers_MRCPSP.Indivadul(offspring_order1, offspring_M1,
start_time1,
offspring_order_time1)
new_ind2 = Suppliers_MRCPSP.Indivadul(offspring_order2, offspring_M2,
start_time2,
offspring_order_time2)
new_offspring.append(new_ind1)
new_offspring.append(new_ind2)
return new_offspring
def main(file):
instance = Suppliers_MRCPSP.Instance()
file_temp = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
f_p = file
f_CN = file_temp+"\data\CNfks.xlsx"
f_O =file_temp+ "\data\Ofs.xlsx"
qfs =file_temp+ "\data\qfs.xlsx"
n=0
# M=[1, 3, 3, 3, 1, 3, 2, 3, 2, 2, 3, 2, 3, 2, 3, 1]
# starttime=[0, 12, 2, 4, 35, 27, 42, 44, 55, 54, 67, 43, 64, 78, 53, 91]
doc = open("best.txt_%s" % ("J" + str(instance.number_job)), "w")
instance.loadData(file_project=f_p, file_CNfks=f_CN, file_Ofs=f_O, file_qfs=qfs)
supplier=instance.initial_Supplier()
IRM=[]
IC=[]
ruba_com=[]
cost_com=[]
for z in range(5):
instance.best_sup=[]
instance.best_ind=[]
for i in range(50):
ind=GA_Suppliers_MRCPSP.GA_RCPSP_ruba(instance,50,supplier)
# sup,ind=GA_Suppliers_MRCPSP.GA_RCPSP_cost(instance,1,ind,i)
sup, ind = GA_Suppliers_MRCPSP.Greedy_cost(instance, ind)
supplier=sup
cost=[instance.project_cost(instance.best_ind[g],instance.best_sup[g],instance.best_ind[g].R_order_time) for g in range(len(instance.best_sup))]
best_cost=min(cost)
best_index=cost.index(best_cost)
print(cost)
best_S=instance.best_sup[best_index]
best_I=instance.best_ind[best_index]
ruba=instance.project_ruba(best_I,best_S)
finishtime=[]
duration=[]
for i in range(len(best_I.M)):
finishtime.append(best_I.starttime[i] + instance.job_model_duration[i + 1][best_I.M[i]])
duration.append(instance.job_model_duration[i + 1][best_I.M[i]])
doc=open("best.txt_%s"%("J"+str(instance.number_job)),"a")
print("-------------------最终结果----------------------",file=doc)
print("supplier",best_S,file=doc)
print("order",best_I.order,file=doc)
print("M",best_I.M, file=doc)
print("starttime",best_I.starttime, file=doc)
print("finishtime", finishtime, file=doc)
print("duration", duration, file=doc)
print("order_time",best_I.R_order_time, file=doc)
best=instance.project_cost(best_I,best_S,best_I.R_order_time)
print("best_cost",best,file=doc)
print("best_ruba",ruba,file=doc)
doc.close()
e=copy.copy(ruba)
t=copy.copy(best)
IRM.append(e)
IC.append(t)
a, b, c, d = instance.job_start_time_2(best_I.order, best_I.M)
best_I.starttime=a
ordertime=instance.initial_ordertime(a,best_I.M,best_S)
ruba_com.append(instance.project_ruba(best_I,best_S))
cost_com.append(instance.project_cost(best_I,best_S,ordertime))
print("\n")
# print(IRM)
# print(IC)
# print("\n")
#
# print(ruba_com)
# print(cost_com)
IRM_rate=[(IRM[i]-ruba_com[i])/ruba_com[i] for i in range(len(IRM))]
IC_rate = [( cost_com[i]- IC[i]) / IC[i] for i in range(len(IC))]
print(IRM_rate)
print(IC_rate)
print(max(IRM_rate),min(IRM_rate),sum(IRM_rate)/len(IRM))
print(max(IC_rate), min(IC_rate), sum(IC_rate) / len(IC))
doc.close()