CN113065743A - An intelligent logistics loading method - Google Patents

An intelligent logistics loading method Download PDF

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CN113065743A
CN113065743A CN202110236481.6A CN202110236481A CN113065743A CN 113065743 A CN113065743 A CN 113065743A CN 202110236481 A CN202110236481 A CN 202110236481A CN 113065743 A CN113065743 A CN 113065743A
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曹小伍
雷铭轩
缪林泽
邵草品
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Hangzhou Xiangyi Technology Co Ltd
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Abstract

本发明涉及一种智能化物流装载方法，包括以下步骤：S1：获取物流模块所需要的输入；S2：以限制区域维度，筛选各区域的待配送订单及可用运力信息；S3：按照限制区域优先级选择待规划区域；S4：对待配送区域的订单集实施聚类；S5：对订单进行车辆分配和装载。本发明的优点是：可以在较短时间内完成物流的智能化装载规划，获得运输成本最低的可行解决方案。

Figure 202110236481

The present invention relates to an intelligent logistics loading method, which includes the following steps: S1: obtaining the input required by the logistics module; S2: screening the orders to be delivered and available capacity information in each area according to the dimension of the restricted area; S3: giving priority to the restricted area Level selects the area to be planned; S4: implements clustering of the order set in the area to be delivered; S5: allocates and loads the order by vehicle. The advantages of the invention are that the intelligent loading planning of the logistics can be completed in a relatively short time, and a feasible solution with the lowest transportation cost can be obtained.

Figure 202110236481

Description

Intelligent logistics loading method
Technical Field
The invention relates to the field of logistics transportation, in particular to an intelligent logistics loading method.
Background
The traditional loading planning has small data volume, and can obtain an optimal solution within a limited time by using dynamic planning, but the current scene is to uniformly load orders of a city, the optimal solution is obtained by np problem, and the whole solving process is required to return within second level, so that the simple dynamic planning cannot adapt to the rapid return of high-quality solution of the data of the city volume. The traditional method for solving the large-volume problem mostly uses a genetic variation algorithm, and the actual genetic variation algorithm has too much randomness in the large np problem and cannot obtain a good result.
Disclosure of Invention
The invention mainly solves the problems that the existing logistics planning scheme can not adapt to the rapid return of high-quality solutions of the data of one city body mass, too much randomness exists, and high-quality solutions can not be realized, and provides an intelligent logistics loading method which uses a clustering algorithm to preprocess all order point sets, obtains n clusters which are distributed relatively and concentrated in a short time, and then constructs feasible solutions meeting loading constraint conditions for each order set in sequence.
The technical scheme adopted by the invention for solving the technical problem is that the intelligent logistics loading method comprises the following steps:
s1: acquiring input required by a logistics module;
s2: screening the order to be distributed and the available transport capacity information of each area by limiting the dimension of the area;
s3: selecting a region to be planned according to the priority of the restricted region;
s4: clustering the order sets of the areas to be distributed;
s5: vehicle allocation and loading is performed on the order.
As a preferable mode of the above-mentioned solution, in step S1, the information including the order to be delivered, the available transportation capacity information, and the restricted area information is input, and the information including the order ID, the commodity information and the required quantity of the order, the place of the order placing shop, and the area to which the order placing shop belongs; the available capacity information includes an ID, a name, a familiar area, a location, a vehicle type corresponding to the driver, and a loadable amount of the vehicle type of the available driver, wherein the location information is expressed by two-dimensional coordinates.
As a preferable scheme of the above scheme, the restricted area includes a restricted area, a limited number area, and a general area.
As a preferable mode of the above, the step S5 includes the steps of:
s51: selecting a cluster with the largest order quantity from the clustered cluster set as a cluster set O of orders to be distributed;
s52: selecting a vehicle of a certain vehicle type from the available transport capacity information as a vehicle to be loaded according to a large vehicle priority principle;
s53: in the order cluster set O to be distributed, randomly selecting a certain order meeting loading constraint as a first loading order of the vehicle of the selected vehicle type according to the corresponding loading capacity of the selected vehicle type;
s54: removing the order from the order cluster O to be delivered;
s55: judging whether the vehicle has a loading space, if so, entering the step S56; if not, the user can not select the specific application,
step S52 is entered;
s56: acquiring a corresponding order set to be distributed from the order set to be distributed O according to the residual loading space of the vehicle;
s57: judging whether the order set to be distributed is empty, if so, loading a cluster set adjacent to the order set O to be distributed; if not, calculating the target function of the order set to be distributed, screening the target order processing device corresponding to the minimum value of the target function, and entering the step S55 after deleting the target order.
As a preferable embodiment of the foregoing solution, the loading, in step S57, a neighboring cluster of the cluster O to be dispatched, includes the following steps:
s571: acquiring a neighboring cluster of a to-be-distributed order cluster O as a to-be-distributed order cluster L;
s572: judging whether the order cluster L to be distributed is empty, if so, entering the step S3; if not, acquiring a corresponding order set to be distributed from the order set to be distributed L according to the residual loading space of the vehicle, and entering the step S57.
As a preferable solution of the above solution, the neighboring cluster is a cluster having a smallest straight-line distance from a center of a currently planned cluster to a center of a remaining cluster to be planned.
As a preferable solution of the above solution, the objective function is:
F0＝-dxy+dxz+dzy
wherein, F0For the amount of distance change after order insertion, dxyIs the distance between original order x and original order y, dxzDistance, d, from original order x to insert order zzyThe distance from the original order y to the insert order z.
As a preferable scheme of the above scheme, when step S3 is executed, if the area with plan is empty, it indicates that the order of each area is planned; otherwise, the process proceeds to step S4.
The invention has the advantages that: the intelligent loading planning of logistics can be completed in a short time, and a feasible solution with the lowest transportation cost is obtained.
Drawings
Fig. 1 is a schematic flow chart of an intelligent logistics loading method in the embodiment.
Detailed Description
The technical solution of the present invention is further described below by way of examples with reference to the accompanying drawings.
Example (b):
the intelligent logistics loading method of the embodiment, as shown in fig. 1, includes the following steps:
s1: acquiring input required by a logistics module; inputting order information to be distributed, available transport capacity information and limited area information, wherein the order information to be distributed comprises an order ID, commodity information and required quantity of the order, the position of an order placing shop and the area of the order placing shop; the available capacity information includes an ID, a name, a familiar area, a location, a vehicle type corresponding to the driver, and a loadable amount of the vehicle type of the available driver. Wherein the position information is represented by two-dimensional coordinates.
S2: screening the order to be distributed and the available transport capacity information of each area by limiting the dimension of the area; the restricted area includes a restricted area, a restricted number area and a general area, and the distribution area is influenced by practical factors such as restricted lines and restricted numbers, and the restricted area is required to be used as a dimension to divide corresponding orders to be distributed and available capacity information.
S3: selecting a region to be planned according to the priority of the restricted region; the priority of the restricted areas is preset according to a preset rule, and all the restricted areas are sequentially acquired from high to low according to the priority and are used as areas to be planned for loading planning. When step S3 is executed, if the area with plan is empty, it indicates that the order of each area is planned; otherwise, the process proceeds to step S4.
S4: clustering the order sets of the areas to be distributed; in the step, the orders to be distributed are clustered according to the distance by using a clustering algorithm such as kmeans, the clustered clusters can be sequentially selected to be loaded when vehicles are specifically distributed in the subsequent step, the orders with smaller relative distance can be distributed on one vehicle to a certain extent, and the quality of an initial solution is provided.
S5: vehicle allocation and loading of orders comprising the steps of:
s51: selecting a cluster with the largest order quantity from the clustered cluster set as a cluster set O of orders to be distributed;
s52: selecting a vehicle of a certain vehicle type from the available transport capacity information as a vehicle to be loaded according to a large vehicle priority principle; and calculating the total volume of the order points contained in each cluster set after clustering, selecting the order point set of the cluster with the largest total volume as the initial order set to be delivered, preferentially loading larger vehicle types, ensuring that the orders loaded by the larger vehicle types are relatively concentrated, and reducing logistics cost.
S53: in the order cluster set O to be distributed, randomly selecting a certain order meeting loading constraint as a first loading order of the vehicle of the selected vehicle type according to the corresponding loading capacity of the selected vehicle type;
s54: removing the order from the order cluster O to be delivered;
s55: judging whether the vehicle has a loading space, if so, entering the step S56; if not, go to step S52;
s56: acquiring a corresponding order set to be distributed from the order set to be distributed O according to the residual loading space of the vehicle;
s57: judging whether the order set to be distributed is empty, if so, loading a cluster set adjacent to the order set O to be distributed; if not, calculating the target function of the order set to be distributed, screening the target order processing device corresponding to the minimum value of the target function, and entering the step S55 after deleting the target order.
Loading a neighbor cluster of a cluster of orders to be dispatched O, comprising the steps of:
s571: acquiring a neighboring cluster set of a to-be-distributed order cluster set O as a to-be-distributed order cluster set L, wherein the neighboring cluster set is a cluster set with the minimum straight-line distance from the currently planned cluster set center to the central point of the rest to-be-planned cluster sets;
s572: judging whether the order cluster L to be distributed is empty, if so, entering the step S3; if not, acquiring a corresponding order set to be distributed from the order set to be distributed L according to the residual loading space of the vehicle, and entering the step S57.
The objective function in step S57 is:
F0＝-dxy+dxz+dzy
wherein, F0For the amount of distance change after order insertion, dxyIs the distance between original order x and original order y, dxzDistance, d, from original order x to insert order zzyThe distance from the original order y to the insert order z. If the existing order distribution routes are set to be 0, 1 and 2 and the order 3 is inserted now, the distance variation F between the order 3 inserted into 0 and 1 is calculated in sequence0‘＝-d01+d03+d13And the distance variation F between order 3 insertion 1 and 20‘’＝-d12+d13+d23Selecting F0‘And F0‘’The solution corresponding to the smaller value of the solution regenerates the route, assuming F0‘At a smaller value, the regenerated routes are 0, 3, 1, 2.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.

Claims (8)

1.一种智能化物流装载方法，其特征是：包括以下步骤：1. an intelligent logistics loading method, is characterized in that: comprise the following steps: S1：获取物流模块所需要的输入；S1: Obtain the input required by the logistics module; S2：以限制区域维度，筛选各区域的待配送订单及可用运力信息；S2: Screen the orders to be delivered and the available capacity information in each area based on the restricted area dimension; S3：按照限制区域优先级选择待规划区域；S3: Select the area to be planned according to the priority of the restricted area; S4：对待配送区域的订单集实施聚类；S4: Implement clustering for the order set in the delivery area; S5：对订单进行车辆分配和装载。S5: Vehicle allocation and loading of the order. 2.根据权利要求1所述的一种智能化物流装载方法，其特征是：所述步骤S1中，输入包括待配送订单信息、可用运力信息和限制区域信息，待配送订单信息包括订单ID、订单的商品信息和需求数量、下单店铺位置及下单店铺所属区域；可用运力信息包括可用司机的ID、名称、熟悉区域、所在位置、司机对应的车型和该车型的可装载量，其中位置信息用二维坐标表示。2. An intelligent logistics loading method according to claim 1, characterized in that: in step S1, the input includes order information to be delivered, available capacity information and restricted area information, and the order information to be delivered includes order ID, The product information and quantity demanded of the order, the location of the store where the order is placed, and the area of the store where the order is placed; the available capacity information includes the ID, name, familiar area, location of the available driver, the model corresponding to the driver and the loadable capacity of the model, where the location Information is represented by two-dimensional coordinates. 3.根据权利要求1或2所述的一种智能化物流装载方法，其特征是：所述限制区域包括限行区域、限号区域及一般区域。3. An intelligent logistics loading method according to claim 1 or 2, wherein the restricted area includes a restricted area, a restricted number area and a general area. 4.根据权利要求1所述的一种智能化物流装载方法，其特征是：所述步骤S5包括以下步骤：4. a kind of intelligent logistics loading method according to claim 1 is characterized in that: described step S5 comprises the following steps: S51：从聚类的簇集中选择订单体量最大的簇作为待配送订单簇集O；S51: Select the cluster with the largest order volume from the clustered cluster set as the order cluster O to be delivered; S52：从可用运力信息中按照大车优先原则选择某车型的车辆作为待装载车辆；S52: Select a vehicle of a certain model as the vehicle to be loaded from the available capacity information according to the principle of priority for large vehicles; S53：在待配送订单簇集O中，根据选定的车型对应的可装载体积，随机选择某个满足装载约束的订单作为选定车型车辆的首个装载订单；S53: In the order cluster O to be delivered, according to the loadable volume corresponding to the selected vehicle model, randomly select an order that satisfies the loading constraint as the first loading order of the vehicle of the selected model; S54：从待配送订单簇集O移除该订单；S54: remove the order from the order cluster O to be delivered; S55：判断该车是否存在装载空间，若是进入步骤S56；若否，则进入步骤S52；S55: Determine whether the vehicle has a loading space, if it is, go to step S56; if not, go to step S52; S56：根据车辆剩余装载空间从待配送订单簇集O中获取相应的待配送订单集；S56: Obtain the corresponding order set to be delivered from the cluster set of orders to be delivered according to the remaining loading space of the vehicle; S57：判断待配送订单集是否为空，若是，对待配送订单簇集O的邻近簇集进行装载；若否，则计算待配送订单集的目标函数并筛选目标函数最小值对应的目标订单进行装置，删除目标订单后进入步骤S55。S57: Determine whether the set of orders to be delivered is empty, if so, load the adjacent clusters of the cluster of orders to be delivered O; , after deleting the target order, go to step S55. 5.根据权利要求4所述的一种智能化物流装载方法，其特征是：所述步骤S57中，对待配送订单簇集O的邻近簇集进行装载，包括以下步骤：5. A kind of intelligent logistics loading method according to claim 4, is characterized in that: in described step S57, the adjacent cluster set of the order cluster set O to be delivered is loaded, comprising the following steps: S571：获取待配送订单簇集O的邻近簇集作为待配送订单簇集L；S571: Obtain the adjacent cluster set of the order cluster set O to be delivered as the order cluster set L to be delivered; S572：判断待配送订单簇集L是否为空，若是则进入步骤S3；若否，则根据车辆剩余装载空间从待配送订单簇集L中获取相应的待配送订单集，进入步骤S57。S572: Determine whether the order cluster L to be delivered is empty, and if so, go to step S3; 6.根据权利要求4或5所述的一种智能化物流装载方法，其特征是：所述邻近簇集为当前规划的簇集中心至剩余待规划簇集的中心点的直线距离最小的簇集。6. A kind of intelligent logistics loading method according to claim 4 or 5, it is characterized in that: described adjacent cluster is the cluster with the smallest linear distance from the center of the currently planned cluster to the center point of the remaining to-be-planned cluster set. 7.根据权利要求4所述的一种智能化物流装载方法，其特征是：所述目标函数为：7. A kind of intelligent logistics loading method according to claim 4, is characterized in that: described objective function is: F0＝-dxy+dxz+dzy F 0 =-d xy +d xz +d zy 其中，F0为订单插入后的距离变化量，dxy为原订单x至原订单y之间的距离，dxz为原订单x到插入订单z的距离，dzy为原订单y到插入订单z的距离。Among them, F 0 is the distance change after order insertion, d xy is the distance from the original order x to the original order y, d xz is the distance from the original order x to the inserted order z, and d zy is the original order y to the inserted order. z distance. 8.根据权利要求1或2或4或5所述的一种智能化物流装载方法，其特征是：执行步骤S3时，若带规划区域为空，则表示各区域订单均规划完毕；反之则进入步骤S4。8. An intelligent logistics loading method according to claim 1 or 2 or 4 or 5, characterized in that: when step S3 is executed, if the planning area is empty, it means that the orders in each area are planned; Go to step S4.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107330655A (en) * 2017-07-11 2017-11-07 南京邮电大学 A kind of intelligent distribution paths planning method based on time reservation
CN111160690A (en) * 2019-11-15 2020-05-15 杭州拼便宜网络科技有限公司 Vehicle loading planning method and device and storage medium
CN111428991A (en) * 2020-03-20 2020-07-17 北京百度网讯科技有限公司 Method and device for determining delivery vehicles

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107330655A (en) * 2017-07-11 2017-11-07 南京邮电大学 A kind of intelligent distribution paths planning method based on time reservation
CN111160690A (en) * 2019-11-15 2020-05-15 杭州拼便宜网络科技有限公司 Vehicle loading planning method and device and storage medium
CN111428991A (en) * 2020-03-20 2020-07-17 北京百度网讯科技有限公司 Method and device for determining delivery vehicles

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