def main_flow(plan_name, area_name):
def count_notzero_rate(series):
not_zero = series[series > 0]
rate = (len(not_zero) / len(series)) * 1.00
return rate
print('商品信息表 sku_info_df read over!')
skudata = sku_info_1.skuData(
file_path='../data/{}/sku信息：{}.xlsx'.format(plan_name, area_name))
RDC_period = 7
FDC_period = 2
service_level = 0.985
rdc_bp = 20
fdc_bp = 5
nrtk = 1.0
shop_period = 0
shop_service_level = 0
shop_bp = 0
shop_nrtk = 0
parameter = {
'RDC补货周期': RDC_period,
'FDC调拨周期': FDC_period,
'服务水平': service_level,
'RDC_BP': rdc_bp,
'FDC_BP': fdc_bp,
'nrtk': nrtk,
'shop调拨周期': shop_period,
'shop服务水平': shop_service_level,
'shop_BP': shop_bp,
'shop_nrtk': shop_nrtk
}
# mlk
duration_info = duration.dutation(
file_path='../data/{}/仓网门店数据1：CDC-RDC.xlsx'.format(plan_name))
CDC_RDC_info = duration_info.cdc_rdc
RDC_FDC_info = duration_info.rdc_fdc
RDC_list = CDC_RDC_info['RDC'].drop_duplicates().tolist()
FAC_CDC_info = duration_info.fac_cdc
FAC_RDC_info = duration_info.fac_rdc
# print('商品布局方案:{}'.format(goods_layout))
if not os.path.exists(
'E:/meilinkai/data/goods_layout_{}/'.format(plan_name)):
os.mkdir('E:/meilinkai/data/goods_layout_{}/'.format(plan_name))
os.mkdir('E:/meilinkai/data/goods_layout_{}/sale/'.format(plan_name))
os.mkdir('E:/meilinkai/data/goods_layout_{}/stock/'.format(plan_name))
os.mkdir(
'E:/meilinkai/data/goods_layout_{}/repleshment/'.format(plan_name))
RDC_FDC_info.to_csv(
'../data/goods_layout_{}/RDC_FDC.csv'.format(plan_name), index=False)
print(parameter)
saledata_toc = SaleData('../data/{}/覆盖范围.csv'.format(plan_name),
'../data/{}/订单数据：{}.xlsx'.format(
plan_name, area_name),
plan_name,
storeORshop='store_city')
turnover_dict = {}
shop_turnover_dict = {}
xianhuo_day = pd.DataFrame(index=saledata_toc.get_daterange())
xianhuo_sku = pd.DataFrame(index=saledata_toc.get_sku())
accuracy_sku = pd.DataFrame(index=saledata_toc.get_sku())
# shopdata_toc = SaleData('../data/goods_layout_{}/shop_toc_statistic_match.csv'.format(goods_layout),
# storeORshop='shop_id')
# shop_xianhuo_day = pd.DataFrame(index=shopdata_toc.get_daterange())
# shop_xianhuo_sku = pd.DataFrame(index=shopdata_toc.get_sku())
# shop_accuracy_sku = pd.DataFrame(index=shopdata_toc.get_sku())
eclp_stock_sum = 0
sale_sum = 0
stock_sum = 0
data_cdc_hangzhou_toc = saledata_toc.get_sales(
'杭州', storeORshop='store_city').fillna(0)
# sale_sum += data_cdc_hangzhou_toc.values.sum()
# print(sale_sum)
if plan_name == '10、华中：杭州-武汉2':
cdc_guangzhou_sale = data_cdc_hangzhou_toc
else:
cdc_guangzhou_sale = pd.DataFrame(index=data_cdc_hangzhou_toc.index)
for RDC in RDC_list:
data_rdc_toc = saledata_toc.get_sales(
RDC, storeORshop='store_city').fillna(0)
sale_sum += data_rdc_toc.values.sum()
# print(sale_sum)
rdc_sale = data_rdc_toc.copy()
print('RDC：{} 销售统计！时间:{}'.format(
RDC,
datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
if RDC in RDC_FDC_info['RDC'].drop_duplicates().tolist():
for fdc_dic in RDC_FDC_info[RDC_FDC_info['RDC'] == RDC][[
'FDC', 'vlt(hour)'
]].to_dict('records'):
data_toc_1 = saledata_toc.get_sales(
fdc_dic['FDC'], storeORshop='store_city').fillna(0)
print('>>>>>>>{}对应FDC:{}，模拟补货！时间：{}'.format(
RDC, fdc_dic['FDC'],
datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
stock_toc_1 = pd.DataFrame(index=data_toc_1.index)
replenish_toc_1 = pd.DataFrame(index=data_toc_1.index)
for sku in saledata_toc.get_sku(fdc_dic['FDC'],
storeORshop='store_city'):
if skudata.get_factory(sku) == '启东':
print(fdc_dic['FDC'] + sku + '危险品')
stock_toc_1[sku], replenish_toc_1[sku], accuracy_sku.loc[
sku, fdc_dic['FDC']] = inventoryCal.stock_simulation_2(
data_toc_1[sku],
FDC_period,
math.ceil(fdc_dic['vlt(hour)'] / 24),
k=service_level,
bp=fdc_bp,
nrtk=nrtk,
min_except=1)
stock_sum += stock_toc_1.values.sum()
sale_sum += data_toc_1.values.sum()
print(stock_sum, sale_sum)
turnover_dict[fdc_dic['FDC']] = stock_toc_1.fillna(
0).values.sum() / data_toc_1.fillna(0).values.sum()
data_toc_1.to_csv(
'../data/goods_layout_{}/sale/FDC_{}.csv'.format(
plan_name, fdc_dic['FDC']))
stock_toc_1.to_csv(
'../data/goods_layout_{}/stock/FDC_{}.csv'.format(
plan_name, fdc_dic['FDC']))
replenish_toc_1.to_csv(
'../data/goods_layout_{}/repleshment/FDC_{}.csv'.format(
plan_name, fdc_dic['FDC']))
print(' >>>>>>>{}对应FDC:{}计算完毕！平均周转天数为：{:.2f}'.format(
RDC, fdc_dic['FDC'], turnover_dict[fdc_dic['FDC']]))
xianhuo_day[fdc_dic['FDC']] = stock_toc_1.apply(
count_notzero_rate, axis=1)
xianhuo_sku[fdc_dic['FDC']] = stock_toc_1.apply(
count_notzero_rate, axis=0)
rdc_sale = rdc_sale.add(replenish_toc_1, fill_value=0)
rdc_sale = rdc_sale.fillna(0)
stock_df = pd.DataFrame(index=rdc_sale.index)
replenish_df = pd.DataFrame(index=rdc_sale.index)
print('RDC：{} 正在模拟库存！'.format(RDC))
for sku in list(rdc_sale.columns):
# stock_df[sku], _, accuracy_sku.loc[sku, RDC] = inventoryCal.stock_simulation_2(rdc_sale[sku],
# RDC_period, 4,
# k=service_level, bp=bp,
# nrtk=nrtk, min_except=1)
vlt = CDC_RDC_info.set_index(['总仓', 'RDC']).loc[('杭州市', RDC),
'vlt(hour)']
stock_df[sku], replenish_df[sku], accuracy_sku.loc[
sku,
RDC] = inventoryCal.stock_simulation_2(rdc_sale[sku],
RDC_period,
math.ceil(vlt / 24),
k=service_level,
bp=rdc_bp,
nrtk=nrtk,
min_except=1)
cdc_guangzhou_sale = cdc_guangzhou_sale.add(replenish_df[[sku]],
fill_value=0)
print("开始保存")
# if not os.path.exists('E:/DepartmentProject/mlk/data/goods_layout_{}/'.format(plan_name)):
# os.mkdir('E:/DepartmentProject/mlk/data/goods_layout_{}/'.format(plan_name))
# os.mkdir('E:/DepartmentProject/mlk/data/goods_layout_{}/sale/'.format(plan_name))
# os.mkdir('E:/DepartmentProject/mlk/data/goods_layout_{}/stock/'.format(plan_name))
# os.mkdir('E:/DepartmentProject/mlk/data/goods_layout_{}/repleshment/'.format(plan_name))
rdc_sale.to_csv('../data/goods_layout_{}/sale/RDC_{}.csv'.format(
plan_name, RDC))
stock_df.to_csv('../data/goods_layout_{}/stock/RDC_{}.csv'.format(
plan_name, RDC))
replenish_df.to_csv(
'../data/goods_layout_{}/repleshment/RDC_{}.csv'.format(
plan_name, RDC))
stock_sum += int(stock_df.fillna(0).values.sum())
if RDC_FDC_info.empty == False:
sale_sum += rdc_sale.values.sum()
print(stock_df.fillna(0).values.sum(), rdc_sale.fillna(0).values.sum())
turnover_dict[RDC] = stock_df.fillna(0).values.sum() / rdc_sale.fillna(
0).values.sum()
xianhuo_day[RDC] = stock_df.apply(count_notzero_rate, axis=1)
xianhuo_sku[RDC] = stock_df.apply(count_notzero_rate, axis=0)
print('{}模拟完成！平均周转天数为：{:.2f}'.format(RDC, turnover_dict[RDC]))
del stock_df
del replenish_df
# cdc_guangzhou_sale = cdc_guangzhou_sale.fillna(0)
# cdc_wuhan_sale = cdc_wuhan_sale.fillna(0)
cdc_guangzhou_stock_df = pd.DataFrame(index=cdc_guangzhou_sale.index)
# cdc_wuhan_stock_df = pd.DataFrame(index=cdc_wuhan_sale.index)
cdc_guangzhou_replenish_df = pd.DataFrame(index=cdc_guangzhou_sale.index)
# cdc_wuhan_replenish_df = pd.DataFrame(index=cdc_wuhan_sale.index)
# print('CDC 武汉 正在模拟库存！')
# cdc_wuhan_sale_use = pd.DataFrame(index=cdc_wuhan_sale.index)
# for sku in list(cdc_wuhan_sale.columns):
# cdc_wuhan_sale_use[sku] = cdc_wuhan_sale[sku]
# cdc_wuhan_stock_df[sku], cdc_wuhan_replenish_df[sku], accuracy_sku.loc[
# sku, 'CDC武汉'] = inventoryCal.stock_simulation_2(cdc_wuhan_sale[sku],
# RDC_period,
# math.ceil(
# 42 / 24),
# k=service_level,
# bp=bp, nrtk=nrtk, min_except=1)
#
# cdc_wuhan_sale_use.dropna().to_csv('../data/goods_layout_{}/sale/CDC_{}.csv'.format(goods_layout, '武汉'))
# cdc_wuhan_stock_df.to_csv('../data/goods_layout_{}/stock/CDC_{}.csv'.format(goods_layout, '武汉'))
# cdc_wuhan_replenish_df.to_csv('../data/goods_layout_{}/repleshment/CDC_{}.csv'.format(goods_layout, '武汉'))
# stock_sum += cdc_wuhan_stock_df.values.sum()
# # sale_sum += cdc_wuhan_sale_use.values.sum()
# turnover_dict['cdc武汉'] = cdc_wuhan_stock_df.values.sum() / cdc_wuhan_sale_use.values.sum()
# print('{}模拟完成！平均周转天数为：{:.2f}'.format('cdc武汉', turnover_dict['cdc武汉']))
if plan_name == '10、华中：杭州-武汉2':
print('CDC 杭州 正在模拟库存！')
cdc_guangzhou_sale_use = pd.DataFrame(index=cdc_guangzhou_sale.index)
for sku in list(cdc_guangzhou_sale.columns):
vlt = FAC_CDC_info.set_index(
['工厂', 'CDC']).loc[(skudata.get_factory(sku) + '市', '杭州市'),
'vlt(hour)']
cdc_guangzhou_sale_use[sku] = cdc_guangzhou_sale[sku]
cdc_guangzhou_stock_df[sku], cdc_guangzhou_replenish_df[
sku], accuracy_sku.loc[
sku, 'CDC杭州市'] = inventoryCal.stock_simulation_2(
cdc_guangzhou_sale[sku],
RDC_period,
math.ceil(vlt / 24),
k=service_level,
bp=rdc_bp,
nrtk=nrtk,
min_except=1)
cdc_guangzhou_sale_use.dropna().to_csv(
'../data/goods_layout_{}/sale/CDC_{}.csv'.format(plan_name, '杭州市'))
cdc_guangzhou_stock_df.to_csv(
'../data/goods_layout_{}/stock/CDC_{}.csv'.format(
plan_name, '杭州市'))
cdc_guangzhou_replenish_df.to_csv(
'../data/goods_layout_{}/repleshment/CDC_{}.csv'.format(
plan_name, '杭州市'))
stock_sum += cdc_guangzhou_stock_df.values.sum()
sale_sum += cdc_guangzhou_sale_use.values.sum()
turnover_dict['cdc杭州市'] = cdc_guangzhou_stock_df.values.sum(
) / cdc_guangzhou_sale_use.values.sum()
print('{}模拟完成！平均周转天数为：{:.2f}'.format('cdc杭州市',
turnover_dict['cdc杭州市']))
print(stock_sum, sale_sum)
turnover_dict['总体'] = stock_sum / sale_sum
else:
print(stock_sum, sale_sum)
turnover_dict['总体'] = stock_sum / sale_sum
writer = pd.ExcelWriter(
'../data/goods_layout_{}/RDC_BP{}FDC_BP{}补货{}调拨{}服务水平{}NRTk{}.xlsx'.
format(plan_name, rdc_bp, fdc_bp, RDC_period, FDC_period,
service_level, nrtk))
pd.DataFrame(parameter, index=[
'0',
]).to_excel(writer, sheet_name='参数详情')
xianhuo_day.to_excel(writer, sheet_name='现货率按天')
xianhuo_sku.to_excel(writer, sheet_name='现货率按SKU')
accuracy_sku.to_excel(writer, sheet_name='预测准确率按SKU')
# shop_xianhuo_day.to_excel(writer, sheet_name='门店现货率按天')
# shop_xianhuo_sku.to_excel(writer, sheet_name='门店现货率按SKU')
# shop_accuracy_sku.to_excel(writer, sheet_name='门店预测准确率按SKU')
pd.DataFrame.from_dict(turnover_dict,
orient='index').to_excel(writer,
sheet_name='仓库平均周转天数')
pd.DataFrame.from_dict(shop_turnover_dict,
orient='index').to_excel(writer,
sheet_name='门店平均周转天数')
print('模拟完成！！！时间:{}'.format(datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
# percent_dir1 = r'../data/cal_data/result/eclp/result{}'.format(percent)
# split_pallet_num1 = 2.0
# anli = AnliSimulation.AnliSimulation(percent_dir=percent_dir1, split_pallet_num=split_pallet_num1)
# anli.cal_storage_cost().to_excel(writer,sheet_name='仓储费用')
# anli.cal_transport_cost_1().to_excel(writer,sheet_name='补货以及调拨费用')
writer.save()
if shop_dic['shop_id'] in shopdata_toc.get_stores(
storeORshop='shop_id'):
data_toc_1 = shopdata_toc.get_sales(
shop_dic['shop_id'], storeORshop='shop_id').fillna(0)
print(' -------{}对应shop:{}开始计算'.format(
RDC, shop_dic['shop_id']))
stock_toc_1 = pd.DataFrame(index=data_toc_1.index)
replenish_toc_1 = pd.DataFrame(index=data_toc_1.index)
for sku in shopdata_toc.get_sku(shop_dic['shop_id'],
storeORshop='shop_id'):
stock_toc_1[sku], replenish_toc_1[
sku], shop_accuracy_sku.loc[sku, shop_dic[
'shop_id']] = inventoryCal.stock_simulation_2(
data_toc_1[sku],
shop_period,
int(shop_dic['VLT']),
k=shop_service_level,
bp=shop_bp,
nrtk=shop_nrtk)
stock_sum += stock_toc_1.values.sum()
sale_sum += data_toc_1.values.sum()
shop_turnover_dict[shop_dic[
'shop_id']] = stock_toc_1.fillna(0).values.sum(
) / data_toc_1.fillna(0).values.sum()
data_toc_1.to_csv(
'../data/goods_layout_{}/sale/shop_{}.csv'.format(
goods_layout, shop_dic['shop_id']))
stock_toc_1.to_csv(
'../data/goods_layout_{}/stock/shop_{}.csv'.format(
goods_layout, shop_dic['shop_id']))
replenish_toc_1.to_csv(
fdc_dic['FDC'], storeORshop='store_city').fillna(0)
print('>>>>>>>{}对应FDC:{}，模拟补货！时间：{}'.format(
RDC, fdc_dic['FDC'],
datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
stock_toc_1 = pd.DataFrame(index=data_toc_1.index)
replenish_toc_1 = pd.DataFrame(index=data_toc_1.index)
for sku in saledata_toc.get_sku(fdc_dic['FDC'],
storeORshop='store_city'):
if skudata.get_factory(sku) == '启东':
print(fdc_dic['FDC'] + sku + '危险品')
stock_toc_1[sku], replenish_toc_1[sku], accuracy_sku.loc[
sku, fdc_dic['FDC']] = inventoryCal.stock_simulation_2(
data_toc_1[sku],
FDC_period,
math.ceil(fdc_dic['vlt(hour)'] / 24),
k=service_level,
bp=bp,
nrtk=nrtk,
min_except=1)
stock_sum += stock_toc_1.values.sum()
sale_sum += data_toc_1.values.sum()
turnover_dict[fdc_dic['FDC']] = stock_toc_1.fillna(
0).values.sum() / data_toc_1.fillna(0).values.sum()
data_toc_1.to_csv(
'../data/goods_layout_{}/sale/FDC_{}.csv'.format(
plan_name, fdc_dic['FDC']))
stock_toc_1.to_csv(
'../data/goods_layout_{}/stock/FDC_{}.csv'.format(
plan_name, fdc_dic['FDC']))
replenish_toc_1.to_csv(
'../data/goods_layout_{}/repleshment/FDC_{}.csv'.format(
data_rdc_toc = saledata_toc.get_sales(RDC, storeORshop='start_city').fillna(0)
sale_sum += data_rdc_toc.values.sum()
rdc_sale = data_rdc_toc.copy()
print('RDC：{} 销售统计！时间:{}'.format(RDC, datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
if RDC in shop_info['RDC'].drop_duplicates().tolist():
print('门店销售统计，并模拟计算！')
for shop_dic in shop_info[shop_info['RDC'] == RDC][['shop_id', 'VLT']].to_dict('records'):
if shop_dic['shop_id'] in shopdata_toc.get_stores(storeORshop='shop_id'):
data_toc_1 = shopdata_toc.get_sales(shop_dic['shop_id'], storeORshop='shop_id').fillna(0)
print(' -------{}对应shop:{}开始计算'.format(RDC, shop_dic['shop_id']))
stock_toc_1 = pd.DataFrame(index=data_toc_1.index)
replenish_toc_1 = pd.DataFrame(index=data_toc_1.index)
for sku in shopdata_toc.get_sku(shop_dic['shop_id'], storeORshop='shop_id'):
stock_toc_1[sku], replenish_toc_1[sku], shop_accuracy_sku.loc[
sku, shop_dic['shop_id']] = inventoryCal.stock_simulation_2(data_toc_1[sku], shop_period,
math.ceil(shop_dic['VLT'] / 24),
k=shop_service_level,
bp=shop_bp, nrtk=shop_nrtk)
stock_sum += stock_toc_1.values.sum()
sale_sum += data_toc_1.values.sum()
shop_turnover_dict[shop_dic['shop_id']] = stock_toc_1.fillna(0).values.sum() / data_toc_1.fillna(
0).values.sum()
data_toc_1.to_csv(
'../data/goods_layout_{}/sale/shop_{}.csv'.format(goods_layout, shop_dic['shop_id']))
stock_toc_1.to_csv(
'../data/goods_layout_{}/stock/shop_{}.csv'.format(goods_layout, shop_dic['shop_id']))
replenish_toc_1.to_csv(
'../data/goods_layout_{}/repleshment/shop_{}.csv'.format(goods_layout, shop_dic['shop_id']))
print(' -------{}对应shop:{}计算完毕！平均周转天数为：{:.2f}'.format(RDC, shop_dic['shop_id'],
shop_turnover_dict[shop_dic['shop_id']]))
shop_xianhuo_day[shop_dic['shop_id']] = stock_toc_1.apply(count_notzero_rate, axis=1)
shop_xianhuo_sku[shop_dic['shop_id']] = stock_toc_1.apply(count_notzero_rate, axis=0)