def GetOUTPUTDATA(ind,
M_max=4,
c_op=c_op,
t_convey=t_convey,
t_pack=t_pack,
c_od=c_od,
lm=lm):
"""
ind:例如[0,3,19,8,7,0,10,6,4,14,12,9,16,0,13,11,18,20,2,0,17,1,15,5,0]
M_max:分区数
"""
batch, batch_item = getBatch(ind)
batch_Quantity = [len(i) for i in batch_item]
data = getBatchPicktime(batch_item)
batch_packingtime = [t_pack * i for i in batch_Quantity]
ODtime = ODtimeTable(np.array(batch)) #每个客户点交付时间
ODtime = [i[-1] * 10 for i in ODtime] #每个批次最后一个客户点交付时间*10
op_duetime = [(lm - i) for i in ODtime]
"""求解BSP"""
N_max = data.shape[1]
index = list(range(1, N_max + 1))
#SPT
data1 = data[1:, :].sum(axis=0)
tup = list(zip(index, data1))
tup.sort(key=lambda x: x[1])
Seq_SPT = np.array([i[0] for i in tup])
data_best_all_SPT = data[:, Seq_SPT - 1]
TPicktime_SPT = makespan(data_best_all_SPT)[-1, :].sum()
#LDT
tup = list(zip(index, ODtime))
tup.sort(key=lambda x: x[1], reverse=0)
Seq_LDT = np.array([i[0] for i in tup])
data_best_all_LDT = data[:, Seq_LDT - 1]
TPicktime_LDT = makespan(data_best_all_LDT)[-1, :].sum()
#择优
if TPicktime_SPT <= TPicktime_LDT:
Seq = Seq_SPT
data_best_all = data_best_all_SPT
else:
Seq = Seq_LDT
data_best_all = data_best_all_LDT
order = np.array(data_best_all[0, :]) - 1
return Seq, data_best_all, batch_packingtime, order, op_duetime, ODtime
def calOPCost(ind,
M_max=4,
c_op=c_op,
t_convey=t_convey,
t_pack=t_pack,
c_od=c_od,
lm=lm):
"""
ind:例如[0,3,19,8,7,0,10,6,4,14,12,9,16,0,13,11,18,20,2,0,17,1,15,5,0]
M_max:分区数
"""
batch, batch_item = getBatch(ind)
batch_Quantity = [len(i) for i in batch_item]
data = getBatchPicktime(batch_item)
batch_packingtime = [t_pack * i for i in batch_Quantity]
ODtime = ODtimeTable(np.array(batch))
op_duetime = [lm - i[-1] * 10 for i in ODtime]
B_max = data.shape[1]
B = range(B_max) #批次订单b集合
M = range(M_max) #分区m集合
G = 99
Pick_time = data[1:, :]
"""对每一个ind的BSP用Gurobi求解"""
MODEL = Model("Order picking Schedule")
# 创建变量
y = MODEL.addVars(range(B_max), range(B_max), vtype=GRB.BINARY,
name="y") #y_bk
c = MODEL.addVars(range(B_max),
range(M_max),
vtype=GRB.CONTINUOUS,
name="c") #c_km
Z = MODEL.addVar(vtype=GRB.CONTINUOUS, name="Z")
z = MODEL.addVars(range(B_max), vtype=GRB.CONTINUOUS,
name="z") #各批次的违约时间z_k
# 更新变量环境
MODEL.update()
# 创建目标函数CONTINUOUS
MODEL.setObjective(
c_op / 10 *
(quicksum(c[k, M_max - 1]
for k in B) + B_max * t_convey + sum(batch_packingtime)) + Z,
sense=GRB.MINIMIZE)
# 创建约束条件
MODEL.addConstrs((quicksum(y[b, k] for k in B) == 1 for b in B),
name="s.t.4")
MODEL.addConstrs((quicksum(y[b, k] for b in B) == 1 for k in B),
name="s.t.5")
MODEL.addConstr(
(c[0, 0] >= quicksum(y[b, 0] * Pick_time[0][b] for b in B)),
name="s.t.6")
MODEL.addConstrs((c[0, m] >= quicksum(y[b, 0] * Pick_time[m][b]
for b in B) + c[0, m - 1] + t_convey
for m in range(1, M_max)))
MODEL.addConstrs((c[k, 0] >= quicksum(y[b, k] * Pick_time[0][b]
for b in B) + c[k - 1, 0]
for k in range(1, B_max)))
MODEL.addConstrs(
(c[k, m] >= c[k - 1, m] + quicksum(y[b, k] * Pick_time[m][b]
for b in B)
for k in range(1, B_max) for m in range(1, M_max)))
MODEL.addConstrs(
(c[k, m] >= c[k, m - 1] + t_convey + quicksum(y[b, k] * Pick_time[m][b]
for b in B)
for k in range(1, B_max) for m in range(1, M_max)))
MODEL.addConstrs((c[k, m] >= 0 for k in B for m in M), name="s.t.22")
MODEL.addConstrs((z[k] >= 0 for k in B), name="s.t.24")
MODEL.addConstrs((z[k] >= c_od / 10 * quicksum(
y[b, k] *
(c[k, M_max - 1] + t_convey + batch_packingtime[b] - op_duetime[b])
for b in B) for k in B),
name="s.t.25")
MODEL.addConstr(Z == quicksum(z[k] for k in B) * c_od / 10, name="s.t.26")
#运行参数调整
MODEL.Params.TimeLimit = 3 #设置求解时间最长为3s
MODEL.Params.LogToConsole = 0 #log记录不在控制台显示
# 执行最优化
MODEL.optimize()
return round(MODEL.objVal, 2)
def calOPCost(ind,
M_max=4,
c_op=c_op,
t_convey=t_convey,
t_pack=t_pack,
c_od=c_od,
lm=lm):
"""
ind:例如[0,3,19,8,7,0,10,6,4,14,12,9,16,0,13,11,18,20,2,0,17,1,15,5,0]
M_max:分区数
"""
batch, batch_item = getBatch(ind)
batch_Quantity = [len(i) for i in batch_item]
data = getBatchPicktime(batch_item)
batch_packingtime = [t_pack * i for i in batch_Quantity] #b属于B
ODtime = ODtimeTable(np.array(batch))
ODtime = [i[-1] * 10 for i in ODtime] #每个批次最后一个客户点交付时间*10
op_duetime = [(lm - i) for i in ODtime]
"""求解BSP"""
N_max = data.shape[1]
index = list(range(1, N_max + 1))
#SPT
data1 = data[1:, :].sum(axis=0)
tup = list(zip(index, data1))
tup.sort(key=lambda x: x[1])
Seq_SPT = np.array([i[0] for i in tup])
data_best_all_SPT = data[:, Seq_SPT - 1]
TPicktime_SPT = makespan(data_best_all_SPT)[-1, :].sum()
#LDT
tup = list(zip(index, op_duetime))
tup.sort(key=lambda x: x[1])
Seq_LDT = np.array([i[0] for i in tup])
data_best_all_LDT = data[:, Seq_LDT - 1]
TPicktime_LDT = makespan(data_best_all_LDT)[-1, :].sum()
#择优
if TPicktime_SPT <= TPicktime_LDT:
Seq = Seq_SPT
data_best_all = data_best_all_SPT
else:
Seq = Seq_LDT
data_best_all = data_best_all_LDT
order = np.array(data_best_all[0, :]) - 1
"""时间类"""
#各分区订单抵达后台时间
batch_arrive_time = np.array([
i + t_convey for i in (makespan(data_best_all, t_convey)[-1, :])
]) #b 属于pi，拣货顺序批次
# 批次订单开始配送时间=抵达后台时间+打包分类时间
batch_delivery_start_time = batch_arrive_time + np.array(
[batch_packingtime[i] for i in order])
#批次订单违约时间
batch_over_duetime = []
for j in list(
np.array(batch_delivery_start_time) -
np.array([op_duetime[i] for i in order])):
if j < 0:
batch_over_duetime.append(0)
else:
batch_over_duetime.append(j)
"""成本类：将时间/10"""
#各批次订单拣选成本
batch_op_cost = np.array(batch_delivery_start_time) / 10 * c_op
#各批次订单违约成本
batch_overduetime_cost = np.array(batch_over_duetime) / 10 * c_od
#订单总拣选成本+违约成本
TC = batch_op_cost.sum() + batch_overduetime_cost.sum()
return TC
from time import time
from numpy import *
from Order_batching import getBatch
from pick_time_S_shape import getBatchPicktime
from Parameter import t_convey, c_op, c_od, c_d, delivery_cost_perMin, lm, t_pack
ind_from = 1 #0--from ortools;1--from GA ；2--gurobi
""" ind ：例如[0,1,2,5,6,0,4,7,8,0,9,11,12,0]"""
if ind_from == 2:
from CVRPDV_Gurobi import route, var_a, C_d
ind = route
batch, batch_item = getBatch(ind)
last_order_perBatch = [i[-2] for i in batch]
ODtime = [var_a[i - 1] for i in last_order_perBatch]
C_d_Bat = 0 #待确认
elif ind_from == 0:
from CVRPDV_ortools import vehicle_routing, batch_delivery_time, batch_route_dis
ind = vehicle_routing
batch, batch_item = getBatch(ind)
ODtime = batch_delivery_time #每个批次订单最后一个订单的交付时间
C_d = sum(batch_route_dis) * c_d
C_d_Bat = array(batch_route_dis) * c_d
else:
from CVRPDV_GA import bestInd, ODtime, C_d, C_d_Bat
ind = bestInd
batch, batch_item = getBatch(ind)
batch_Quantity = [len(i) for i in batch_item]
data = getBatchPicktime(batch_item)
