Warehouse Rescheduling with Python(Pandas)

# #!/usr/bin/python
# # -*- coding: UTF-8 -*-
"""
  @author:01378722
"""
import itertools

import pandas as pd
import numpy as np

data = pd.read_excel('出库数据.xlsx',sheet_name=1)
du = pd.DataFrame(data)
finpool = du.sort_values(by=['单票最低运费','续重价格'])

capcoln=finpool.loc[:,['始发地代码']]
# capcoln.sort_values(by=['始发地代码'])
caplist=list(set(capcoln['始发地代码'].tolist()))   #去重
caplist.sort()
print(caplist)       #从‘出库数据’中获得始发地省会城市序号的list: 21 beginning cities

demcoln=finpool.loc[:,['目的地代码']]
demcoln.sort_values(by=['目的地代码'])
demlist=list(set(demcoln['目的地代码'].tolist()))   #去重
demlist.sort()
print(demlist)       #从‘出库数据’中获得目的地城市序号的list

data1 = pd.read_excel('出库数据.xlsx', sheet_name=2)
dv = pd.DataFrame(data1)
evalupool = dv.sort_values(by=['单票最低运费','续重价格'])

data2=pd.read_excel('出库数据.xlsx', sheet_name=4)
dk=data2.loc[:, ['始发地代码','目的地代码','周均重量']]
weight_pool = pd.DataFrame(dk)
# print(weight_pool.head())

min_total_cost = 10000000000000000
for i in itertools.combinations(caplist,18):       # 从21个始发仓中筛选出18个仓库发货的情况
    print(i)
    # print(i[0])
    j_sum = 0.00
    for j in demlist:
        dem_info_sec = weight_pool[weight_pool['目的地代码']==j]
        dem_j_weight = dem_info_sec['周均重量'].values
        j_price_sec = evalupool[evalupool['目的地']==j]
        j_min_price = 10000000000000
        for k in range(0,18):
            rd_price = j_price_sec[j_price_sec['始发地']==i[k]]
            bg_weight = rd_price['起始重量'].values
            price_1 = rd_price['单票最低运费'].values
            price_2 = rd_price['续重价格'].values
            if dem_j_weight > bg_weight:
                k_price = price_1 + price_2 * (dem_j_weight - bg_weight)
            else:
                k_price = price_1
            if k_price < j_min_price:
                j_min_price = k_price
        j_sum += j_min_price
    print('用',i,'仓库组合代替最初21个仓发货时，拥有的总成本最小值为：',j_sum)
    if j_sum < min_total_cost:
        min_total_cost = j_sum
print('使总成本最小的方案为用',i,'仓库组合代替最初21个仓发货，最小的总成本为：',min_total_cost)