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Warehouse Rescheduling[combination](only delete 1 warehouse)
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Warehouse Rescheduling[combination](only delete 1 warehouse)
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# #!/usr/bin/python
# # -*- coding: UTF-8 -*-
"""
@author:01378722
"""
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=capcoln['始发地代码'].drop_duplicates().values.tolist() #去重
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=demcoln['目的地代码'].drop_duplicates().values.tolist() #去重
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())
for i in caplist:
cap_section = weight_pool[weight_pool['始发地代码']==i]
i_destlist=list(cap_section['目的地代码'].tolist())
i_sum_oricost = 0.00
# print(i_destlist) # 输出当前每个始发仓覆盖的目的地代码
j_sum_mincost = 0.00
for j in i_destlist:
selected_candidate_info = evalupool[evalupool['目的地']==j]
# print(selected_candidate_info.head())
selected_ori_info = selected_candidate_info[selected_candidate_info['始发地']==i]
ori_quant = selected_ori_info['起始重量'].values
ori_price1 = selected_ori_info['单票最低运费'].values
ori_price2 = selected_ori_info['续重价格'].values
quant_prep = cap_section[cap_section['目的地代码']==j]
current_quant = quant_prep['周均重量'].values
i_sum_oricost = 0.00
if current_quant>ori_quant:
ori_cost_plus = ori_price1 + ori_price2 * (current_quant-ori_quant)
else:
ori_cost_plus = ori_price1
i_sum_oricost += ori_cost_plus
k_mincost = 1000000000000
for k in caplist:
# dest_section = evalupool[evalupool['目的地']==cap_section['目的地代码']]
selected_candidate_info = selected_candidate_info[selected_candidate_info['始发地']==k]
current_oriquant = selected_candidate_info['起始重量'].values
current_price1 = selected_candidate_info['单票最低运费'].values
current_price2 = selected_candidate_info['续重价格'].values
if current_quant>current_oriquant:
candidate_price = current_price1 + current_price2 * (current_quant - current_oriquant)
else:
candidate_price = current_price1
if candidate_price<k_mincost:
k_mincost = candidate_price
j_sum_mincost += k_mincost
print('被取消仓为',i,'时,增加的最小费用为：',(j_sum_mincost - i_sum_oricost))
# Output:
# [371, 595, 28, 451, 23, 27, 532, 10, 29, 24, 431, 571, 411, 20, 25, 755, 7311, 871, 898, 21, 991]
# [377, 379, 396, 370, 371, 373, 374, 375, 378, 391, 393, 394, 395, 398, 376, 596, 592, 593, 594, 595, 591, 28, 816, 817, 818, 825, 830, 831, 833, 834, 838, 453, 459, 23, 451, 452, 454, 458, 464, 455, 467, 468, 469, 717, 719, 27, 710, 711, 716, 724, 728, 539, 531, 543, 22, 533, 534, 537, 546, 631, 635, 917, 29, 530, 532, 535, 536, 633, 911, 415, 24, 412, 414, 416, 417, 418, 419, 421, 427, 429, 431, 432, 433, 434, 435, 436, 437, 438, 439, 570, 571, 572, 573, 576, 577, 574, 575, 578, 579, 580, 411, 20, 25, 513, 514, 515, 663, 759, 510, 511, 512, 516, 517, 518, 519, 523, 527, 750, 752, 755, 756, 757, 760, 769, 736, 7313, 730, 734, 7311, 319, 311, 871, 872, 874, 877, 879, 312, 313, 316, 335, 873, 898, 8981, 315, 310, 561, 551, 553, 558, 10, 21, 931, 937, 351, 352, 353, 354, 356, 357, 791, 797, 951, 971, 851, 52, 858, 771, 772, 773, 991, 994, 996, 476]
# 被取消仓为 371 时,增加的最小费用为： [4263.21525]
# 被取消仓为 595 时,增加的最小费用为： [2314.0565]
# 被取消仓为 28 时,增加的最小费用为： [14307.7305]
# 被取消仓为 451 时,增加的最小费用为： [3729.645]
# 被取消仓为 23 时,增加的最小费用为： [1882.9345] 重庆
# 被取消仓为 27 时,增加的最小费用为： [3276.862]
# 被取消仓为 532 时,增加的最小费用为： [8513.68225]
# 被取消仓为 10 时,增加的最小费用为： [9491.53825]
# 被取消仓为 29 时,增加的最小费用为： [3180.7945]
# 被取消仓为 24 时,增加的最小费用为： [2802.155]
# 被取消仓为 431 时,增加的最小费用为： [2185.11]
# 被取消仓为 571 时,增加的最小费用为： [7823.56925]
# 被取消仓为 411 时,增加的最小费用为： [159.04] 大连
# 被取消仓为 20 时,增加的最小费用为： [4683.78125]
# 被取消仓为 25 时,增加的最小费用为： [7378.467]
# 被取消仓为 755 时,增加的最小费用为： [1949.92] 深圳
# 被取消仓为 7311 时,增加的最小费用为： [2688.316]
# 被取消仓为 871 时,增加的最小费用为： [7197.25375]
# 被取消仓为 898 时,增加的最小费用为： [4012.4825]
# 被取消仓为 21 时,增加的最小费用为： [992.0375] 上海
# 被取消仓为 991 时,增加的最小费用为： [-130.] 乌鲁木齐（考虑取消掉的仓）
# Appendix【response to command ’print(i_destlist)’】获得原来每个始发仓所覆盖的目的地代码
# [351, 352, 353, 354, 356, 357, 370, 371, 373, 374, 375, 376, 377, 378, 379, 391, 393, 394, 395, 396, 398]
# [591, 592, 593, 594, 595, 596, 791, 797]
# [28, 816, 817, 818, 825, 830, 831, 833, 834, 838]
# [451, 452, 453, 454, 455, 458, 459, 464, 467, 468, 469]
# [23]
# [27, 710, 711, 716, 717, 719, 724, 728]
# [530, 531, 532, 533, 534, 535, 536, 537, 539, 543, 546, 631, 633, 635]
# [10, 22, 310, 311, 312, 313, 315, 316, 319, 335]
# [29, 911, 917, 931, 937, 951, 971, 991, 994, 996]
# [24, 412, 414, 415, 416, 417, 418, 419, 421, 427, 429]
# [431, 432, 433, 434, 435, 436, 437, 438, 439]
# [570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580]
# [411]
# [20, 663, 750, 752, 756, 757, 759, 760, 769]
# [25, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 523, 527, 551, 553, 558, 561]
# [755]
# [730, 734, 736, 7311, 7313]
# [52, 771, 772, 773, 851, 858, 871, 872, 873, 874, 877, 879]
# [898, 8981]
# [21]
# [476]