作者：下里巴人

来自南京审计大学

前言：

本篇文章主要记录一下本次慧发发项目中，实现根据货物目的地自动判定物流园选择的矩阵并进行优化的过程。

算法原理

原理图如下：

我们需要完成的是，在一张表中，显示出所有符合要求的方案，也就是输入货物目的地，最终输出 Cij，以便后续的规划算法。

算法实现

第一版算法如下

Models

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#初始化货物类
class Goods:
    
    def __init__(self,destination,deadline,weight,volume):
        #定义了该批货品的目的地
        self.destination = destination
        #定义了货品的发货截止日期
        self.deadline = deadline
        #定义了货品的重量
        self.weight = weight
        #定义了货品的体积
        self.volume = volume

#初始化物流园类
class Logistics:

    def __init__(self,name,Latitude_and_longitude,rates):
        #定义了物流园的名称
        self.name = name
        #定义了经纬度
        self.Latitude_and_longitude = Latitude_and_longitude
        #定义了物流园中的商户（多个，类型为列表）
        self.commercial_tenants = []
        #定义了物流园的进场费
        self.rates = rates
    
    def add_commercial_tenant(self,commercial_tenant):
        #定义添加物流园专线的方法
        self.commercial_tenants.append(commercial_tenant)

#初始化物流园专线
class Commercial_tenant:

    def __init__(self,site,price,time_consuming,interval):
        #定义了物流园的目的地
        self.site = site
        #定义了发货单价
        self.price = price
        #定义了发货耗时
        self.time_consuming = time_consuming
        #定义了发货间隔
        self.interval = interval

#创建两个物流商户实例
chengdu_site1 = Commercial_tenant('chengdu',15,4,3)
changsha_site1 = Commercial_tenant('changsha',12,4,2)
wuhan_site1 = Commercial_tenant('wuhan',8,3,2)
nanchang_site1 = Commercial_tenant('nanchang',15,3,2)

chengdu_site2 = Commercial_tenant('chengdu',7,4,3)
changsha_site2 = Commercial_tenant('changsha',16,4,2)
luoyang_site2 = Commercial_tenant('luoyang',8,3,2)
zhenzhou_site2 = Commercial_tenant('zhenzhou',15,3,2)

chengdu_site3 = Commercial_tenant('chengdu',41,4,3)
changsha_site3 = Commercial_tenant('changsha',17,4,2)
nanyang_site3 = Commercial_tenant('nanyang',8,3,2)
hefei_site4 = Commercial_tenant('heifei',15,3,2)

chengdu_site4 = Commercial_tenant('chengdu',21,4,3)
changsha_site4 = Commercial_tenant('changsha',13,4,2)
beijin_site4 = Commercial_tenant('beijin',8,3,2)
nancahng_site4 = Commercial_tenant('nanchang',15,3,2)

#创建一个物流园实例
wuliuyuan1 = Logistics(1,'13.173;31.321',15)
wuliuyuan2 = Logistics(2,'12.125;35.321',10)
wuliuyuan3 = Logistics(3,'13.553;34,321',15)
wuliuyuan4 = Logistics(4,'12.123;34.111',10)

#将物流园专线添加至实例中
wuliuyuan1.add_commercial_tenant(chengdu_site1)
wuliuyuan1.add_commercial_tenant(changsha_site1)
wuliuyuan1.add_commercial_tenant(wuhan_site1)
wuliuyuan1.add_commercial_tenant(nanchang_site1)

wuliuyuan2.add_commercial_tenant(chengdu_site2)
wuliuyuan2.add_commercial_tenant(changsha_site2)
wuliuyuan2.add_commercial_tenant(luoyang_site2)
wuliuyuan2.add_commercial_tenant(zhenzhou_site2)

wuliuyuan3.add_commercial_tenant(chengdu_site3)
wuliuyuan3.add_commercial_tenant(changsha_site3)
wuliuyuan3.add_commercial_tenant(nanyang_site3)
wuliuyuan3.add_commercial_tenant(hefei_site4)

wuliuyuan4.add_commercial_tenant(chengdu_site4)
wuliuyuan4.add_commercial_tenant(changsha_site4)
wuliuyuan4.add_commercial_tenant(beijin_site4)
wuliuyuan4.add_commercial_tenant(nancahng_site4)

#添加货物实例destination,deadline,weight,volume
huowu1 = Goods('chengdu','2023/3/10',1,1)
huowu2 = Goods('changsha','2023/3/10',1,1)
huowu3 = Goods('nanyang','2023/3/10',1,1)

Sizer

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from Models import *
import math
import time
import datetime


# 初始化一个i*j的二维列表，i为物流园实例数，j为此次货物实例数，值均为M（无限大）
table = [[math.inf for i in range(4)] for j in range(3)]

#货物目的地列表
V = [huowu1.destination,huowu2.destination,huowu3.destination]
#定义了一个时间格式化的函数
def format_time(x):

    formattime = datetime.datetime.strptime(x,r'%Y/%m/%d')

    return formattime
#货物截止时间列表
T = list(map(format_time,[huowu1.deadline,huowu2.deadline,huowu3.deadline]))
#货物目的地对应截止日期的字典
V_T = dict(zip(V,T))
#每个物流园商户具有的地点列表
S1_site = [s.site for s in wuliuyuan1.commercial_tenants]
S2_site = [s.site for s in wuliuyuan2.commercial_tenants]
S3_site = [s.site for s in wuliuyuan3.commercial_tenants]
S4_site = [s.site for s in wuliuyuan4.commercial_tenants]
#定义每个物流园商户具有的价格列表
S1_price = [s.price for s in wuliuyuan1.commercial_tenants]
S2_price = [s.price for s in wuliuyuan2.commercial_tenants]
S3_price = [s.price for s in wuliuyuan3.commercial_tenants]
S4_price = [s.price for s in wuliuyuan4.commercial_tenants]
#定义了每个物流园商户对应的地点和价格字典
S_P1 = dict(zip(S1_site,S1_price))
S_P2 = dict(zip(S2_site,S2_price))
S_P3 = dict(zip(S3_site,S3_price))
S_P4 = dict(zip(S4_site,S4_price))
#定义总地点列表
S_site = [S1_site,S2_site,S3_site,S4_site]
#定义总地点价格对应列表
S_P = [S_P1,S_P2,S_P3,S_P4]
for s , m in zip(S_site,S_P):
    
    if set(s) & set(V) != []:

        intersection = list(set(s) & set(V))

        for i in intersection:

            if i == 'nanyang':

                row = 0

                col = S_site.index(s)
                
                value = m[i]

                table[row][col] = value

            elif i == 'changsha':

                row = 1

                col = S_site.index(s)

                value = m[i]

                table[row][col] = value

            elif i == 'chengdu':

                row = 2

                col =  S_site.index(s)

                value = m[i]

                table[row][col] = value
            
            else:
                pass
    else:

        pass
print(table)

缺陷

  • 数据结构简单，为列表，导致逻辑过于繁杂
  • 代码死板，无法实现自动化

根据老师建议，做出如下调整：

  • 数据结构替换为DataFrame
  • 重构代码，实现自动化

Models

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#初始化货物类
class Goods:
    
    def __init__(self,destination,deadline,weight,volume):
        #定义了该批货品的目的地
        self.destination = destination
        #定义了货品的发货截止日期
        self.deadline = deadline
        #定义了货品的重量
        self.weight = weight
        #定义了货品的体积
        self.volume = volume

#初始化物流园类
class Logistics:

    def __init__(self,name,Latitude_and_longitude,rates):
        #定义了物流园的名称
        self.name = name
        #定义了经纬度
        self.Latitude_and_longitude = Latitude_and_longitude
        #定义了物流园中的商户（多个，类型为列表）
        self.commercial_tenants = []
        #定义了物流园的进场费
        self.rates = rates
    
    def add_commercial_tenant(self,commercial_tenant):
        #定义添加物流园专线的方法
        self.commercial_tenants.append(commercial_tenant)

#初始化物流园专线
class Commercial_tenant:

    def __init__(self,site,price,time_consuming,interval):
        #定义了物流园的目的地
        self.site = site
        #定义了发货单价
        self.price = price
        #定义了发货耗时
        self.time_consuming = time_consuming
        #定义了发货间隔
        self.interval = interval

#创建两个物流商户实例
chengdu_site1 = Commercial_tenant('chengdu',15,4,3)
changsha_site1 = Commercial_tenant('changsha',12,4,2)
wuhan_site1 = Commercial_tenant('wuhan',8,3,2)
nanchang_site1 = Commercial_tenant('nanchang',15,3,2)

chengdu_site2 = Commercial_tenant('chengdu',7,4,3)
changsha_site2 = Commercial_tenant('changsha',16,4,2)
luoyang_site2 = Commercial_tenant('luoyang',8,3,2)
zhenzhou_site2 = Commercial_tenant('zhenzhou',15,3,2)

chengdu_site3 = Commercial_tenant('chengdu',41,4,3)
changsha_site3 = Commercial_tenant('changsha',17,4,2)
nanyang_site3 = Commercial_tenant('nanyang',8,3,2)
hefei_site4 = Commercial_tenant('heifei',15,3,2)

chengdu_site4 = Commercial_tenant('chengdu',21,4,3)
changsha_site4 = Commercial_tenant('changsha',13,4,2)
beijin_site4 = Commercial_tenant('beijin',8,3,2)
nancahng_site4 = Commercial_tenant('nanchang',15,3,2)

#创建一个物流园实例
wuliuyuan1 = Logistics(1,'13.173;31.321',15)
wuliuyuan2 = Logistics(2,'12.125;35.321',10)
wuliuyuan3 = Logistics(3,'13.553;34,321',15)
wuliuyuan4 = Logistics(4,'12.123;34.111',10)

#将物流园专线添加至实例中
wuliuyuan1.add_commercial_tenant(chengdu_site1)
wuliuyuan1.add_commercial_tenant(changsha_site1)
wuliuyuan1.add_commercial_tenant(wuhan_site1)
wuliuyuan1.add_commercial_tenant(nanchang_site1)

wuliuyuan2.add_commercial_tenant(chengdu_site2)
wuliuyuan2.add_commercial_tenant(changsha_site2)
wuliuyuan2.add_commercial_tenant(luoyang_site2)
wuliuyuan2.add_commercial_tenant(zhenzhou_site2)

wuliuyuan3.add_commercial_tenant(chengdu_site3)
wuliuyuan3.add_commercial_tenant(changsha_site3)
wuliuyuan3.add_commercial_tenant(nanyang_site3)
wuliuyuan3.add_commercial_tenant(hefei_site4)

wuliuyuan4.add_commercial_tenant(chengdu_site4)
wuliuyuan4.add_commercial_tenant(changsha_site4)
wuliuyuan4.add_commercial_tenant(beijin_site4)
wuliuyuan4.add_commercial_tenant(nancahng_site4)

Sizer2

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from Models import *
import math
import pandas as pd


def create_df():

    table = [[math.inf for i in range(4)] for j in range(len(destination))]

    df = pd.DataFrame(table,columns=['P1','P2','P3','P4'],index=destination)

    return df




if __name__ == "__main__":

    #添加货物实例destination,deadline,weight,volume
    destination = input(str('请输入您此次货物的目的地，用英文,分隔：''请输入您此次货物的目的地，用英文,分隔：')).split(',')

    deadline = input(str('请输入对应货物的截止日期，格式形如：2023/3/21，用英文,分隔：')).split(',')

    weight = input(str('请输入货物对应的重量，单位kg，以英文,分隔：')).split(',')

    volume = input(str('请输入货物的体积，单位m³，以英文,分隔：')).split(',')

    #循环创建动态对象
    for a , b , c , d , goods_name in zip(destination,deadline,weight,volume,destination):

        locals()[goods_name] = Goods(a,b,c,d)

    df = create_df()

    #每个物流园商户具有的地点列表
    S1_site = [s.site for s in wuliuyuan1.commercial_tenants]
    S2_site = [s.site for s in wuliuyuan2.commercial_tenants]
    S3_site = [s.site for s in wuliuyuan3.commercial_tenants]
    S4_site = [s.site for s in wuliuyuan4.commercial_tenants]
    #定义每个物流园商户具有的价格列表
    S1_price = [s.price for s in wuliuyuan1.commercial_tenants]
    S2_price = [s.price for s in wuliuyuan2.commercial_tenants]
    S3_price = [s.price for s in wuliuyuan3.commercial_tenants]
    S4_price = [s.price for s in wuliuyuan4.commercial_tenants]
    #定义了每个物流园商户对应的地点和价格字典
    S_P1 = dict(zip(S1_site,S1_price))
    S_P2 = dict(zip(S2_site,S2_price))
    S_P3 = dict(zip(S3_site,S3_price))
    S_P4 = dict(zip(S4_site,S4_price))
    #定义总地点列表
    S_site = [S1_site,S2_site,S3_site,S4_site]
    #定义总地点价格对应列表
    S_P = [S_P1,S_P2,S_P3,S_P4]
    for s in S_site:

        intersection = list(set(s) & set(destination))

        if intersection != []:

            for p , num in zip(['P1','P2','P3','P4'],S_P):

                for i in intersection:

                    df[p][i] = num.get(i)

        else:

            pass
    print(df)