WO2020118891A1 - 一种航空货邮配送方法 - Google Patents

一种航空货邮配送方法 Download PDF

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WO2020118891A1
WO2020118891A1 PCT/CN2019/075370 CN2019075370W WO2020118891A1 WO 2020118891 A1 WO2020118891 A1 WO 2020118891A1 CN 2019075370 W CN2019075370 W CN 2019075370W WO 2020118891 A1 WO2020118891 A1 WO 2020118891A1
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cargo
flight
mail
segment
delivery
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PCT/CN2019/075370
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王璞
彭洋
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中南大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/109—Time management, e.g. calendars, reminders, meetings or time accounting
    • G06Q10/1093—Calendar-based scheduling for persons or groups

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  • the invention belongs to the field of aviation technology, and particularly relates to an air cargo and mail delivery method.
  • the value of flight operations is uncertain. Under normal circumstances, the capacity of the flight (the total weight that can be carried in the belly cabin of the passenger aircraft) is fixed, and the weight of the items checked by the passengers of the aircraft is related to the flight operation and the capacity of the flight. The weight of passengers' checked items is inversely related to the availability of flights. Airlines generally estimate the availability of flights based on historical experience.
  • the airline will sign an order contract with each freight forwarding company every year.
  • the agent contracts part of the mail volume at a certain price, and the remaining cargo volume is placed on the market for free sales.
  • the ratio of agent sales to free sales of cargo and mail orders is generally judged by airlines through experience, and has certain limitations on the routes of complicated sectors.
  • the technical problem solved by the present invention is to propose a method of air cargo and mail delivery in view of the deficiencies of the existing technology, with the urgency of the delivery of cargo and mail orders as the secondary goal, and the balance of flight utilization rates on the same sector as the main Objective, establish the objective function, and solve the air cargo and mail delivery plan, and achieve the effect of multi-objective optimization.
  • An air cargo and mail delivery method includes the following steps:
  • Step 1 Obtain flight data and cargo and mail order data
  • Step 2 Sort the flights on the same flight segment on the same day in the order of departure time
  • Step 3 For the delivery of all cargo and mail orders on the same flight segment on the same day, establish a secondary target planning model that targets the urgency of the shipment:
  • V yK 0 or 1
  • Step 4 Iteratively adjust the value of ⁇ in the constraints of the secondary target programming model, and record the ⁇ value obtained after the mth iteration as ⁇ (m) . Perform the following operations after each iteration:
  • Variance reflects the degree of dispersion in the utilization rate of each flight in this segment, and is used to measure the balance of flight utilization;
  • Step 5 After several iterations of adjustment, solve the main target planning model that aims at the balance of flight utilization:
  • ⁇ (m) corresponding to f is the optimal value of ⁇ , the corresponding That is the best cargo and mail delivery plan.
  • the flight data and the cargo and mail order data are obtained, and the invalid and null data in the two types of data are removed to extract valid information;
  • the valid information in the flight data includes the flight number, flight date, Take-off location, arrival location and take-off time;
  • the valid information in the cargo and mail order data includes the main waybill number corresponding to each cargo and mail order, the weight of the goods, the urgency of delivery, and the flight information to which the cargo and mail order was originally assigned, including Flight number, flight date, place of departure, place of arrival and operation are available.
  • the total available operation of all flights of the same flight segment on the same day and the total weight of goods corresponding to all cargo and mail orders of the same flight segment on the same day are calculated to calculate the flight of the flight segment Average utilization.
  • the present invention provides an air cargo and mail order allocation method, which is different from the traditional operations research method.
  • This method is based on multiple programming, which combines linear programming and dynamic programming to solve the optimization of the multi-objective multi-stage decision-making process.
  • It uses dynamic programming as the basic framework.
  • the objective function f of dynamic programming is the main goal of multiple programming.
  • Linear programming is used to determine the variables of dynamic programming. The range of values is limited, that is, the optimal "definition domain" of the decision variable X K is determined on the basis of achieving the secondary goal max Z of multiple planning; the multi-stage decision problem is transformed into a series of interrelated single-stage problems, and then one by one Be resolved to achieve the ultimate goal.
  • This method adopts multiple planning methods to achieve the distribution of air cargo and mail orders.
  • the delivery priority of cargo and mail orders is set as a secondary target, and the utilization ratio of each flight segment is set as the main target, and it is reflected by the minimum variance.
  • This method works well and satisfies the requirements of the balance of all flights in the same flight segment to a great extent.
  • This method meets the secondary goal while satisfying the optimal decision set.
  • the optimal decision set based on the decision variables determined in the linear programming stage is determined, and the optimal goal to be achieved is determined in the dynamic planning stage.
  • the application of this method to air cargo and mail orders is conducive to promoting the development of air cargo and mail transportation business, reducing the flight empty load rate, meeting the people's demand for air cargo speed, optimizing the flight utilization rate of the same flight segment, and reducing the cost of individual flights.
  • the load consumption based on this method, can provide a reasonable distribution plan for the distribution of future cargo and mail orders, which is of great significance to the development of the overall logistics industry.
  • Figure 1 is a schematic diagram of the process of the present invention
  • Figure 3 is a comparison diagram of the probability distribution of flight utilization variance before and after the use of this method for January-March 2018 cargo and mail orders.
  • Figures 3(a) and 3(b) are the cargo and mail order utilization books for January 2018, respectively.
  • Comparison graphs of the probability distribution of the variance of flight utilization before and after method optimization are the comparison graphs of the probability distribution of variance of flight utilization before and after the optimization of the February 2018 cargo and mail order using this method;
  • Figure 3 (e) and Figure 3(f) are comparison graphs of the probability distribution of variance of flight utilization before and after the optimization of the cargo and mail orders in March 2018 using this method.
  • the present invention proposes an air cargo and mail order delivery method.
  • the following describes the present invention in further detail with reference to the accompanying drawings and specific embodiments, but is not intended to limit the present invention.
  • the specific implementation of the present invention is shown in FIG. 1 and includes the following steps .
  • Step 1 Obtain the flight data and cargo and mail order data of Civil Aviation from January to March 2018, and remove the invalid and null data from the two types of data to extract valid information.
  • the valid information in the flight data includes the flight number, flight date, departure location, arrival location, departure time, and arrival time;
  • the valid information in the cargo and mail order data includes the main waybill number, product code, and product corresponding to each cargo and mail order
  • flight segment The interval formed by one take-off point and one arrival point is called a flight segment.
  • Step 2 Calculate the average flight utilization rate of each flight segment
  • P is the sum of the weight of the goods corresponding to all cargo and mail orders of that segment on that day
  • T is the sum of the available operations of all flights of that segment on that day.
  • G K is available for the Kth flight;
  • each flight of each flight segment is regarded as one stage, and there are N flights in a certain flight segment.
  • state variable S K represents the total cargo volume initially owned by the Kth flight
  • the Kth flight is one of the N flights, which is expressed as:
  • Step three according to the order's urgency, determine the priority shipping factor B of the same day's cargo and mail order and the 0-1 variable V to determine whether the order is loaded.
  • the yth cargo and mail order is one of the d orders.
  • the weight of the y-th order is W y kg, W y ⁇ 0, and the priority shipping factor of the y-th order is defined as B y to indicate the priority of the delivery of the cargo and mail order.
  • V yK indicates whether the yth cargo and mail order is loaded into the Kth flight
  • V yK 0 indicates that the yth cargo and mail order is not loaded Enter the Kth flight.
  • Step 4 Establish a secondary goal planning model that targets the urgency of transportation:
  • V yK 0 or 1
  • V yK 0 or 1
  • ⁇ ′ is the adjustment parameter
  • ⁇ is the variance of the utilization rate of each flight in this segment when it is not optimized
  • Q K represents the utilization rate of the Kth flight without optimization, It represents the sum of the weight of the goods corresponding to the original (not optimized) cargo and mail orders assigned to the Kth flight;
  • Step 5 Iteratively adjust ⁇ ′ to find the optimal target for the entire flight segment; the specific steps are:
  • Variance reflects the degree of dispersion in the utilization rate of each flight in this segment, and is used to measure the balance of flight utilization.
  • Step 6 Solve the main goal planning model aiming at the balance of flight utilization:
  • ⁇ (m) corresponding to f is the optimal value of ⁇ , the corresponding That is the best cargo and mail delivery plan.
  • the method of the present invention is used to reallocate historical cargo and mail orders and compare it with the original distribution plan. The results are shown in Figures 2 and 3.
  • 1 hour is the length of the time window (24 time windows a day) , Divide the departure time of the flight carrying cargo and mail into 24 time windows, calculate the probability distribution by the ratio of the number of orders in the unit time window to the total number of orders, and make the historical cargo and mail orders and the orders distributed by using the invention Compare the probability distribution graph.
  • the expedited orders are delivered from the original daily flights from 6 to 24 o'clock. After being distributed through this method, they are mainly concentrated on the daily flights from 6 to 9 o'clock in the morning, and all deliveries can be delivered before 19:00 complete.
  • Figure 3 is a comparison chart of the probability distribution of flight utilization variance between January and March 2018 before and after the use of this method for cargo and mail orders.
  • 0.05 is used as the sub-interval length, and each segment of the flight is used every day.
  • the variance of the rate is divided into sub-intervals between [minimum variance, maximum variance], and the probability distribution is calculated by the ratio of the number of variances falling within each sub-interval to the total number of variances.
  • Figure 3(a) and Figure 3(b) are the comparison graphs of the probability distribution of the variance of flight utilization before and after the optimization of the cargo and mail orders in January 2018 using this method, from Figure 3(a) and Figure 3(b). It can be seen that the variance in January was up to 1.12 from the original, and the highest variance dropped to 0.32 after optimization, and by calculating the measurement index ⁇ of the optimization degree of flight utilization balance, the average optimization degree of flight utilization balance in January was 33.22 %; Figure 3(c) and Figure 3(d) are the comparison graphs of the probability distribution of the variance of flight utilization before and after the optimization of the February 2018 cargo and mail orders using this method.

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Abstract

一种航空货邮配送方法，包括以下步骤：步骤一，获取航班数据和货邮订单数据；步骤二，以起飞时间的先后为顺序，对同一天同航段的航班进行排序；步骤三，针对同一天同航段所有货邮订单配送，建立以运送的缓急程度为目标的次要目标规划模型，其约束条件中包含调节参数β；步骤四，迭代调整β的取值，每次迭代之后对次要目标规划模型进行求解；并建立以航班利用率的均衡性为目标的主要目标规划模型，以判断迭代过程中求解得出的最优方案。该方法综合考虑了货邮订单运送的缓急程度和同航段航班利用率的均衡性，实现了航空货邮配送过程中的双目标优化。

Description

一种航空货邮配送方法 技术领域
本发明属于航空技术领域，具体涉及一种航空货邮配送方法。
背景技术
伴随着经济全球化的发展，航空运输业务也得到了极大的发展。根据2017年民航行业发展统计公报的数据显示，截至2017年年底，中国共有颁证运输机场229个，比上年年底增加11个；2017年货邮运输收入水平达1.48元/吨公里，比上年提高0.15元/吨公里。中国航空货邮运输业务快速发展的同时必然会出现许多不可忽视的问题。在目前的航空运输机队里，中国民航货机总数为143架，仅占运输飞机总数的4.34％，客机占运输飞机总数的95.66％，从货邮的载运方式可以看出，客机的腹舱载货是我国航空货邮业务的主要运载方式，但客机的腹舱载货方式也受到一些条件的限制：
(1)航班运行可供值不确定。通常情况下航班的可供运力(客运飞机腹舱能承载的总重量)是固定的，本机乘客托运物品的重量与航班运行可供的和为本次航班的可供运力。乘客托运物品的重量与航班运行可供成负相关。航空公司一般会根据历史经验对航班的运行可供进行预估。
(2)存在飞机起飞时间固定而货邮订单装载时间不固定的情况。由于是客机，飞机会在预定起飞时间起飞，不会因为本次航班存在剩余业载，或者剩余业载过大而推迟起飞时间，未赶上装载的货物只能装载到下一班飞机的腹舱内。
(3)航空公司每年会与各货运代理公司签订订单合同，代理商按一定价格承包部分货邮量，而剩余的货邮量放在市场上进行自由销售。货邮订单代理销售和自由销售的比例一般由航空公司通过经验判断，对繁杂航段的线路具有一定的局限性。
(4)航空货邮订单中未对快件、急件等高附加值的货邮订单进行优先配送。
对货邮的配送方式的合理性直接会影响物流业整体的发展，现有的方法多是通过对货邮代理商的研究来建立航空货邮配送模型，没有考虑自由销售订单对航空货邮业务的影响；并且实现的目标较为单一，以收益最大为主，无法对同航段航班利用率的均衡性进行优化，其中航班利用率＝航班货物总重量/航班运行可供；订单种类庞杂，没有对货邮订单进行分类，没有对时间价值较高的同批货物进行优先配送，其中时间价值较高的同批货物指的是在同一天同一航段所运送的货物中，单位时间内价值比较高的货物，即时效性较高的货物；在航空货物中，一般急件、快件的时间价值最高，易腐货物的附加值次高。解决优先性和均衡性的问题是航空货邮业务良性发展的重要保证。
因此，有必要提供一种考虑货邮订单运送优先级和同航段航班利用率的均衡性的航空货邮 配送方法。
发明内容
本发明所解决的技术问题是，针对现有技术的不足，提出一种航空货邮配送的方法，以货邮订单运送的缓急程度为次要目标，同航段航班利用率的均衡性为主要目标，建立目标函数，求解航空货邮配送方案，达到了多目标优化的效果。
一种航空货邮配送方法，包括以下步骤：
步骤一，获取航班数据和货邮订单数据；
步骤二，以起飞时间的先后为顺序，对同一天同航段的航班进行排序；
步骤三，针对同一天同航段所有货邮订单配送，建立以运送的缓急程度为目标的次要目标规划模型：
Figure PCTCN2019075370-appb-000001
约束条件为：
Figure PCTCN2019075370-appb-000002
V yK＝0或1且
Figure PCTCN2019075370-appb-000003
其中，d为该天该航段的货邮订单总数，N为该天该航段的航班总数，B y表示第y个货邮订单的优先运送系数(表示货邮订单运送的优先度，其值根据货邮订单运送的缓急程度不同确定)，B y为正整数；V yK表示第y个货邮订单是否被装入第K个航班，V yK＝1表示第y个货邮订单被装入第K个航班，V yK＝0表示第y个货邮订单不装入第K个航班；V yK为待求解的参数，由V yK(y＝1，2，...，d；K＝1，2，...，N)构成配送矩阵V，
Figure PCTCN2019075370-appb-000004
W y为第y个货邮订单对应的商品重量，W y≥0，单位为千克；G K为第K个航班的运行可供，β是对于
Figure PCTCN2019075370-appb-000005
的调节参数，β的取值范围为
Figure PCTCN2019075370-appb-000006
为该航段的航班平均利用率，
Figure PCTCN2019075370-appb-000007
P为该天该航段所有货邮订单对应的商品重量总和，T为该天该航段所有航班的运行可供总和，
Figure PCTCN2019075370-appb-000008
步骤四，迭代调整次要目标规划模型约束条件中β的取值，记第m次迭代之后得到的β值为β (m)，每次迭代之后进行以下操作：
1)求解次要目标规划模型，得到
Figure PCTCN2019075370-appb-000009
的取值，记为
Figure PCTCN2019075370-appb-000010
2)根据
Figure PCTCN2019075370-appb-000011
的取值，计算第m次迭代过程中分配到第K个航班的合计货邮
Figure PCTCN2019075370-appb-000012
Figure PCTCN2019075370-appb-000013
3)计算第K个航班的利用率
Figure PCTCN2019075370-appb-000014
Figure PCTCN2019075370-appb-000015
4)计算反映该航段第K个航班利用率与该航段所有航班平均利用率的离散程度的指标函数：
Figure PCTCN2019075370-appb-000016
5)计算航段中各航班利用率的方差：
Figure PCTCN2019075370-appb-000017
方差反映该航段各航班利用率离散程度，用于衡量航班利用率的均衡性；
步骤五，若干次迭代调整之后，求解以航班利用率的均衡性为目标的主要目标规划模型：
f＝min{δ (0)，δ (1)，δ (2)，...}；
f对应的β (m)即为β的最优取值，相应的
Figure PCTCN2019075370-appb-000018
即为最优货邮配送方案。
进一步地，所述步骤一中，获取航班数据和货邮订单数据，将两种数据中的无效、空值数据进行剔除处理后提取有效信息；航班数据中的有效信息包括航班号、航班日期、起飞地点、到达地点和起飞时间；货邮订单数据中的有效信息包括每个货邮订单对应的主运单号、商品重量、运送的缓急程度以及该货邮订单原始分配至的航班信息，包括航班号、航班日期、起飞地点、到达地点和运行可供。
进一步地，根据货邮订单数据中的有效信息，统计得到同一天同航段所有航班的运行可供总和，以及同一天同航段所有货邮订单对应的商品重量总和，进而计算航段的航班平均利用率。
进一步地，根据货邮订单运送的缓急程度将优先运送系数划分为三个等级：第一等级B y＝3，为加急订单的优先运送系数；第二等级B y＝2，为易腐类订单运的优先运送系数；第三等级B y＝1，为普通订单的优先运送系数。
进一步地，所述步骤四中，β的迭代调整方法为：设置β的初始值为
Figure PCTCN2019075370-appb-000019
迭代精度为
Figure PCTCN2019075370-appb-000020
即令第m次迭代之后得到的β值
Figure PCTCN2019075370-appb-000021
m＝1，2，...；当
Figure PCTCN2019075370-appb-000022
时，停止迭代。
有益效果：
本发明提供了一种航空货邮订单的分配方法，不同于传统的运筹学方法，本方法是基于多重规划，将线性规划和动态规划相结合起来，求解多目标多阶段决策过程最优化的一种方法。它以动态规划为基本框架，动态规划的目标函数f即是多重规划主要的实现目标，利用线性规划对动态规划的决策变量
Figure PCTCN2019075370-appb-000023
的取值范围进行限制，即在实现多重规划次要目标max Z的基础上确定决策变量X K的最优“定义域”；把多阶段决策问题变换成一系列相互联系的单阶段问题，然后逐个加以解决，实现最终的目标。
本方法采用多重规划的方法来实现航空货邮订单的分配，将货邮订单的运送优先度定为次要目标，各航段利用率均衡定为主要目标，并通过方差最小来体现。该方法效果良好，极大程度地满足了同一航段中所有航班均衡性的要求。本方法在满足最优决策集合的同时达到次要目标，在整个多重规划过程中，基于线性规划阶段确定的决策变量的最优决策集合，在动态规划阶段确定所要达到的最优目标。本方法在航空货邮订单上的应用，有利于促进航空货邮运输业务的发展，降低航班空载率，满足人民对航空货运速度的需求，优化同一航段的航班利用率，降低个别航班的载重消耗，基于本方法，可对未来货邮订单的配送提供合理的配送方案，对整体物流业的发展有着重要的意义。
附图说明
图1为本发明流程示意图
图2为2018年1月-3月加急订单(B y＝3)利用本方法前后的运送时间概率分布对比图，图2(a)、图2(b)和图2(c)分别为2018年1月、2月和3月加急订单(B y＝3)运送时间概率分布对比图。
图3为2018年1月-3月货邮订单利用本方法前后的航班利用率方差概率分布对比图，图3(a)和图3(b)分别为2018年1月的货邮订单利用本方法优化前后的航班利用率方差概率分布对比图；图3(c)和图3(d)分别为2018年2月货邮订单利用本方法优化前后的航班利用率方差概率分布对比图；图3(e)和图3(f)分别为2018年3月货邮订单利用本方法优化前后的航班利用率方差概率分布对比图。
具体实施方式
本发明提出了一种航空货邮订单配送的方法，下面结合附图和具体实施例对本发明作进一步详细描述，但不作为对本发明的限定，本发明具体实施如图1所示，包括以下步骤。
步骤一，获取2018年1月至3月全民航的航班数据和货邮订单数据，将两种数据中的无效、空值数据进行剔除处理后进行有效信息提取。航班数据中的有效信息包括航班号、航班日期、起飞地点、到达地点、起飞时刻、达到时刻；货邮订单数据中的有效信息包括每个货邮订单对应的主运单号、商品代码、商品重量、运送的缓急程度以及该货邮订单原始分配至的航班信息，包括航班号、航班日期、起飞地点、到达地点、可供运力和运行可供。
进一步地，将第一季度同一天的航班以起飞时间的先后为顺序，对同航段的航班进行排序，为后续分配做准备。其中一个起飞地点和一个到达地点所形成的区间叫做一个航段。
步骤二，分别计算出每个航段的航班平均利用率
Figure PCTCN2019075370-appb-000024
将某一天某一航段所有货邮订单分配给该航段的N个航班。该航段的航班平均利用率
Figure PCTCN2019075370-appb-000025
的计算方程为：
Figure PCTCN2019075370-appb-000026
其中，P为该天该航段所有货邮订单对应的商品重量总和，T为该天该航段所有航班的运行可供总和，
Figure PCTCN2019075370-appb-000027
G K为第K个航班的运行可供；
进一步地，在所述步骤二中，把每个航段的每一个航班视为一个阶段，则某一航段共N个航班。设状态变量S K表示第K个航班初始拥有的总合计货邮量，第K个航班是N个航班中的其中一个航班，则表示为：
K＝1，2，...，N
步骤三，根据订单的缓急程度不同，确定同日货邮订单的优先运送系数B以及确定订单是否被装入的0-1变量V。
设该航段共有d个订单，第y个货邮订单是d个订单中的其中一个订单。已知第y个订单的重量为W y千克，W y≥0，并定义第y个订单的优先运送系数为B y，来表示货邮订单运送的优先度。V yK表示第y个货邮订单是否被装入第K个航班，V yK＝1表示第y个货邮订单被装入第K个航班，V yK＝0表示第y个货邮订单不装入第K个航班。
对订单数据中的货邮等级进行划分，本例根据货物运送的缓急程度划分为三个等级：第一等级a 1＝3，为加急订单运送系数值；第二等级a 2＝2，为易腐类订单运送系数值；第三等级a 3＝1，为普通订单运载系数值。
步骤四，建立以运送的缓急程度为目标的次要目标规划模型：
Figure PCTCN2019075370-appb-000028
约束条件为
Figure PCTCN2019075370-appb-000029
V yK＝0或1且
Figure PCTCN2019075370-appb-000030
B y＝1或2或3
V yK＝0或1
其中，β′是调节参数；δ是未优化时该航段各航班利用率的方差，
Figure PCTCN2019075370-appb-000031
式中
Figure PCTCN2019075370-appb-000032
Q K表示未优化时第K个航班的利用率，
Figure PCTCN2019075370-appb-000033
表示原始(未优化时)分配至第K个航班的货邮订单对应的商品重量之和；
步骤五，对β′进行迭代调整，求解出整个航段的最优目标；具体步骤为：
0)设置β′的初始值为
Figure PCTCN2019075370-appb-000034
迭代精度为
Figure PCTCN2019075370-appb-000035
用m表示迭代次数；
1)求解次要目标规划模型，得到V yK(y＝1，2，...，d；K＝1，2，...，N)的取值，记为
Figure PCTCN2019075370-appb-000036
Figure PCTCN2019075370-appb-000037
2)根据
Figure PCTCN2019075370-appb-000038
的取值，计算第m次迭代过程中分配到第K个航班的合计货邮
Figure PCTCN2019075370-appb-000039
Figure PCTCN2019075370-appb-000040
3)计算第K个航班的利用率
Figure PCTCN2019075370-appb-000041
Figure PCTCN2019075370-appb-000042
4)计算反映该航段第K个航班利用率与该航段所有航班平均利用率的离散程度的指标函数：
Figure PCTCN2019075370-appb-000043
5)计算航段中各航班利用率的方差：
Figure PCTCN2019075370-appb-000044
方差反映该航段各航班利用率离散程度，用于衡量航班利用率的均衡性。
6)计算第m次迭代调整之后得到的β′值：
Figure PCTCN2019075370-appb-000045
若
Figure PCTCN2019075370-appb-000046
转步骤1)；否则若
Figure PCTCN2019075370-appb-000047
时，停止迭代，进入步骤六；
步骤六、求解以航班利用率的均衡性为目标的主要目标规划模型：
f＝min{δ (0)，δ (1)，δ (2)，...}；
f对应的β (m)即为β的最优取值，相应的
Figure PCTCN2019075370-appb-000048
即为最优货邮配送方案。
为验证本发明的效果，采用本发明方法对历史货邮订单重新分配，并与原始分配方案进行对比，结果如图2和图3所示。
图2为2018年1月-3月加急订单(B y＝3)利用本方法前后的运送时间概率分布对比图，图2(a)、图2(b)和图2(c)分别为2018年1月、2月和3月的加急订单(B y＝3)运送时间概率分布对比图，在作图2过程中，以1个小时为时间窗长度(一天为24个时间窗)，将载有货邮的航班的起飞时间划分在24个时间窗内，通过单位时间窗内订单数量与总订单数量的比值计算概率分布，作出了历史货邮订单与运用本发明分配后的订单对比概率分布图。从图2可以看出，加急订单由原来每日6点到24点不同航班配送，通过本方法配送后主要集中到每日早上6点到9点的航班，并能在19点前全部配送完毕。
图3为2018年1月-3月货邮订单利用本方法前后的航班利用率方差概率分布对比图，在作图3过程中，以0.05为子区间长度，将每一天每个航段航班利用率的方差划分在[最小方差，最大方差]间的子区间内，并通过落在各子区间内的方差个数与总的方差个数的比值计算概率分布。此外，建立航班利用率均衡性优化程度衡量指标θ：
Figure PCTCN2019075370-appb-000049
其中，δ 优化后即运用本方法获得的f值，δ 优化前为原始分配方案中的同一天同航段航班利用率的方差。
图3(a)和图3(b)分别为2018年1月的货邮订单利用本方法优化前后的航班利用率方差概率分布对比图，从图3(a)和图3(b)中可以看出，1月份的方差由原来最高达1.12，在优化后最高方差下降到0.32，并通过计算航班利用率均衡性优化程度衡量指标θ，得到1 月份航班利用率均衡性的平均优化程度为33.22％；图3(c)和图3(d)分别为2018年2月货邮订单利用本方法优化前后的航班利用率方差概率分布对比图，从图3c和图3d中可以看出，2月份的方差由原来最高达0.44，在优化后最高方差下降到0.16，并通过计算优化程度衡量指标θ，得到2月份航班利用率均衡性平均优化程度为53.78％；图3(e)和图3(f)分别为2018年3月货邮订单利用本方法优化前后的航班利用率方差概率分布对比图，3月份的方差由原来最高达0.69，在优化后最高方差下降到0.33，并通过计算优化程度衡量指标θ，得到3月份航班利用率均衡性平均优化程度为54.32％。

Claims (5)

  1. 一种航空货邮配送方法，其特征在于，包括以下步骤：
    步骤一，获取航班数据和货邮订单数据；
    步骤二，以起飞时间的先后为顺序，对同一天同航段的航班进行排序；
    步骤三，针对同一天同航段所有货邮订单配送，建立以运送的缓急程度为目标的次要目标规划模型：
    Figure PCTCN2019075370-appb-100001
    约束条件为：
    Figure PCTCN2019075370-appb-100002
    V yK＝0或1且
    Figure PCTCN2019075370-appb-100003
    其中，d为该天该航段的货邮订单总数，N为该天该航段的航班总数，B y表示第y个货邮订单的优先运送系数，其值根据货邮订单运送的缓急程度确定，V yK表示第y个货邮订单是否被装入第K个航班，V yK＝１表示第y个货邮订单被装入第K个航班，V yK＝０表示第y个货邮订单不装入第K个航班；W y为第y个货邮订单对应的商品重量；G K为第K个航班的运行可供，β是对于
    Figure PCTCN2019075370-appb-100004
    的调节参数，β的取值范围为
    Figure PCTCN2019075370-appb-100005
    为该航段的航班平均利用率，
    Figure PCTCN2019075370-appb-100006
    P为该天该航段所有货邮订单对应的商品重量总和，T为该天该航段所有航班的运行可供总和；
    步骤四，迭代调整次要目标规划模型约束条件中β的取值，记第m次迭代之后得到的β值为β (m)，每次迭代之后进行以下操作：
    1)求解次要目标规划模型，得到V yK的取值，记为
    Figure PCTCN2019075370-appb-100007
    2)根据
    Figure PCTCN2019075370-appb-100008
    的取值，计算第m次迭代过程中分配到第K个航班的合计货邮
    Figure PCTCN2019075370-appb-100009
    Figure PCTCN2019075370-appb-100010
    3)计算第K个航班的利用率
    Figure PCTCN2019075370-appb-100011
    Figure PCTCN2019075370-appb-100012
    4)计算反映该航段第K个航班利用率与该航段所有航班平均利用率的离散程度的指标函数：
    Figure PCTCN2019075370-appb-100013
    5)计算航段中各航班利用率的方差：
    Figure PCTCN2019075370-appb-100014
    步骤五，若干次迭代调整之后，求解以航班利用率的均衡性为目标的主要目标规划模型：
    f＝min{δ (0)，δ (1)，δ (2)，...}；
    f对应的β (m)即为β的最优取值，相应的
    Figure PCTCN2019075370-appb-100015
    即为最优货邮配送方案。
  2. 根据权利要求1所述的航空货邮配送方法，其特征在于，所述步骤一中，获取航班数据和货邮订单数据，将两种数据中的无效、空值数据进行剔除处理后提取有效信息；航班数据中的有效信息包括航班号、航班日期、起飞地点、到达地点和起飞时间；货邮订单数据中的有效信息包括每个货邮订单对应的主运单号、商品重量、运送的缓急程度以及该货邮订单原始分配至的航班信息，包括航班号、航班日期、起飞地点、到达地点和运行可供。
  3. 根据权利要求2所述的航空货邮配送方法，其特征在于，根据货邮订单数据中的有效信息，统计得到同一天同航段所有航班的运行可供总和，以及同一天同航段所有货邮订单对应的商品重量总和，进而计算航段的航班平均利用率。
  4. 根据权利要求1所述的航空货邮配送方法，其特征在于，根据货邮订单运送的缓急程度将优先运送系数划分为三个等级：第一等级B y＝3，为加急订单的优先运送系数；第二等级B y＝2，为易腐类订单运的优先运送系数；第三等级B y＝1，为普通订单的优先运送系数。
  5. 根据权利要求1～4中任一项所述的航空货邮配送方法，其特征在于，所述步骤四中，β的迭代调整方法为：设置β的初始值为
    Figure PCTCN2019075370-appb-100016
    迭代精度为
    Figure PCTCN2019075370-appb-100017
    即令第m次迭代之后得到的β值
    Figure PCTCN2019075370-appb-100018
    当
    Figure PCTCN2019075370-appb-100019
    时，停止迭代。
PCT/CN2019/075370 2018-12-10 2019-02-18 一种航空货邮配送方法 WO2020118891A1 (zh)

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