CN113128744A - Distribution planning method and device - Google Patents
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Abstract
The invention discloses a distribution planning method and a distribution planning device, and relates to the technical field of computers. The method comprises the following steps: constructing a distribution network model according to the order information to be distributed; wherein the distribution network model is composed of nodes and edges between the nodes; the nodes are used for representing the distribution points of the orders, and edges among the nodes are used for representing the connection relation among the distribution points of the orders; clustering nodes in the distribution network model to obtain a plurality of clusters; and taking the range covered by each of the clusters as a distribution area to obtain a plurality of distribution areas. Through the steps, the distribution areas can be dynamically and reasonably divided according to the order information to be distributed, so that the distribution orders in each distribution area tend to be balanced, and the overall distribution efficiency is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a distribution planning method and a distribution planning device.
Background
In a logistics distribution scene, distribution areas need to be divided, and corresponding distribution personnel are assigned to each distribution area. In the prior art, the distribution area is always kept fixed after being divided, and a courier sorts and distributes goods according to the distributed distribution area.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art: first, since the number of orders in each delivery area is dynamically changed, it is likely that the delivery amount and the delivery time of different delivery areas are extremely unbalanced, and even the delivery time difference between some couriers reaches 2 to 3 hours. Second, during the current delivery process, the courier often empirically determines the delivery route. When the delivery range is small, delivery by experience may have little effect on delivery efficiency. However, when the distribution range becomes large, the distribution is performed empirically, which causes problems such as unreasonable distribution route planning and low distribution efficiency.
Disclosure of Invention
In view of the above, the present invention provides a distribution planning method and apparatus, which can dynamically and reasonably divide distribution areas according to order information to be distributed, so that the distribution orders in each distribution area tend to be balanced, and further, the distribution planning method and apparatus are helpful to improve the distribution efficiency as a whole. Furthermore, path planning is carried out in the divided distribution areas based on a heuristic algorithm, and distribution efficiency can be further improved.
To achieve the above object, according to one aspect of the present invention, a delivery planning method is provided.
The distribution planning method of the invention comprises the following steps: constructing a distribution network model according to the order information to be distributed; wherein the distribution network model is composed of nodes and edges between the nodes; the nodes are used for representing the distribution points of the orders, and edges among the nodes are used for representing the connection relation among the distribution points of the orders; clustering nodes in the distribution network model to obtain a plurality of clusters; and taking the range covered by each of the clusters as a distribution area to obtain a plurality of distribution areas.
Optionally, the constructing a delivery network model according to the order information to be delivered includes: the method comprises the steps of converting distribution point coordinates of orders into nodes in a distribution network model, converting connection relations among the order distribution points into edges among the nodes, and converting logistics distribution difficulty comprehensive scores among the order distribution points into weights of the edges among the nodes.
Optionally, the clustering the nodes in the distribution network model to obtain a plurality of clusters includes: determining initial positions of K cluster center points; calculating the distance between each node in the distribution network model and the central point of each cluster, and distributing the nodes to the cluster with the minimum distance so as to realize the updating of the clusters; calculating the position of the updated cluster center point until an iteration stop condition is met; wherein K is an integer greater than 1.
Optionally, the calculating the distance between each node in the distribution network model and each cluster center point includes: and calculating the distance between a node and the cluster center point according to the weight of each edge between the node and the cluster center point in the distribution network model and the real path length corresponding to each edge.
Optionally, the method comprises: and after obtaining a plurality of distribution areas, planning an order distribution path in the distribution areas according to a heuristic algorithm, and returning the planned order distribution path to the user terminal.
Optionally, the planning of order distribution paths in the distribution area according to a heuristic algorithm includes: determining a candidate order distribution path in the distribution area, and calculating the total distribution cost of the candidate distribution path according to the total vehicle dispatching cost and the total driving cost corresponding to the candidate order distribution path; and optimizing the candidate order distribution path by taking the minimum distribution total cost as an objective function until a planned order distribution path is obtained.
Optionally, the method comprises: determining a logistics distribution difficulty comprehensive score among the order distribution points according to at least one of the following influence factors: real path length between order distribution points, road conditions between order distribution points, ease of parking between order distribution points, weight of on-board cargo between order distribution points.
To achieve the above object, according to another aspect of the present invention, a delivery planning apparatus is provided.
The dispensing device of the present invention comprises: the construction module is used for constructing a distribution network model according to the order information to be distributed; wherein the distribution network model is composed of nodes and edges between the nodes; the nodes are used for representing the distribution points of the orders, and edges among the nodes are used for representing the connection relation among the distribution points of the orders; the clustering module is used for clustering the nodes in the distribution network model to obtain a plurality of clusters; and the system is further configured to regard a range covered by each of the plurality of clusters as a distribution area to obtain a plurality of distribution areas.
To achieve the above object, according to still another aspect of the present invention, there is provided an electronic apparatus.
The electronic device of the present invention includes: one or more processors; and storage means for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement the delivery planning method of the present invention.
To achieve the above object, according to still another aspect of the present invention, there is provided a computer-readable medium.
The computer-readable medium of the invention, on which a computer program is stored which, when being executed by a processor, carries out the delivery planning method of the invention.
One embodiment of the above invention has the following advantages or benefits: the method comprises the steps of constructing a distribution network model according to order information to be distributed, clustering nodes in the distribution network model to obtain a plurality of clusters, and taking a range covered by each cluster as a distribution area to obtain a plurality of distribution areas.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
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The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a schematic main flow chart of a delivery planning method according to a first embodiment of the present invention;
fig. 2 is a schematic main flow chart of a delivery planning method according to a second embodiment of the present invention;
FIG. 3 is a schematic diagram of a distribution network model constructed in accordance with an embodiment of the invention;
FIG. 4 is a schematic diagram of a position relationship between a node and a cluster center point according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of the main modules of a delivery planning apparatus according to a third embodiment of the present invention;
fig. 6 is a schematic diagram of the main modules of a delivery planning apparatus according to a fourth embodiment of the present invention;
FIG. 7 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
FIG. 8 is a schematic block diagram of a computer system suitable for use with the electronic device to implement an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a main flow chart of a delivery planning method according to a first embodiment of the present invention. As shown in fig. 1, the delivery planning method according to the embodiment of the present invention includes:
and step S101, constructing a distribution network model according to the order information to be distributed.
Wherein the distribution network model is composed of nodes and edges between the nodes; the nodes are used for representing delivery points of orders (the order delivery points may also be referred to as "order receiving points", or "express delivery receiving points", etc.), and edges between the nodes are used for representing connection relations between the order delivery points. In addition, the edges between the nodes can also be provided with corresponding weight values.
In one example, the delivery network model may be denoted as G ═ V, E. Where V represents a set of nodes in the distribution network model and E represents a set of edges between the nodes in the distribution network model.
In another example, for ease of computation, the distribution network model may also be represented by an adjacency matrix a. In the adjacency matrix A, the element AijIs 1 or 0. Wherein A isijThe value of (A) is 1, which means that a connection relationship exists between the node i and the node j (namely, an edge exists between the node i and the node j), AijThe value of (1) is 0, which indicates that there is no connection relation between the node i and the node j (i.e. there is no edge between the node i and the node j).
In yet another example, for ease of calculation, the distribution network model may also be represented by a weighted adjacency matrix a'. In weight adjacency matrix A ', element A'ijIs taken as the weighted value omegaijOr 0. Wherein A isijWhen the value of (a) is a weight value, it indicates that an edge exists between the node i and the node j, and the weight value of the edge is omegaij，AijThe value of (1) is 0, which indicates that there is no connection relation between the node i and the node j (i.e. there is no edge between the node i and the node j).
In constructing the distribution network model, theoretically, there may be connections between the various order distribution points. In a specific implementation, to simplify the network model, for order distribution points in the same cell or the same parcel, n (n may be set according to a requirement, for example, set to be 2 or 3) order distribution points closest to the order distribution points may be selected to connect, and a connection may be made between two cells or two parcels, for example, two order distribution points with the shortest distance between two parcels may be selected to connect.
Step S102, clustering nodes in the distribution network model to obtain a plurality of clusters; and taking the range covered by each of the clusters as a distribution area to obtain a plurality of distribution areas.
For example, a k-means clustering algorithm or other clustering algorithm may be used to cluster the nodes in the distribution network model.
In an optional embodiment, clustering the nodes in the distribution network model by using a k-means clustering algorithm to obtain a plurality of clusters includes: determining initial positions of K cluster center points; calculating the distance between each node in the distribution network model and the central point of each cluster, and distributing the nodes to the cluster with the minimum distance so as to realize the updating of the clusters; and calculating the position of the updated cluster center point until the iteration stop condition is met. Wherein K is an integer greater than 1. In specific implementation, the value of K can be flexibly set. For example, the value of K may be determined according to the total amount of orders to be delivered and the average value of the delivery amount of the historical region.
In the embodiment of the invention, the distribution network model is constructed according to the order information to be distributed, the nodes in the distribution network model are clustered to obtain a plurality of clusters, and the range covered by each cluster is used as a distribution area to obtain a plurality of distribution areas.
Fig. 2 is a main flow chart of a delivery planning method according to a second embodiment of the present invention. As shown in fig. 2, the delivery planning method according to the embodiment of the present invention includes:
step S201, a distribution network model is built according to the order information to be distributed.
As shown in fig. 3, the distribution network model constructed by the embodiment of the present invention is composed of nodes and edges between the nodes. The nodes are used for representing the distribution points of the orders, and the edges among the nodes are used for representing the connection relation among the distribution points of the orders. In addition, the edges between the nodes can also be provided with corresponding weight values.
Further, for ease of computation, the distribution network model may be represented by a weighted adjacency matrix a'. In weight adjacency matrix A ', element A'ijIs taken as the weighted value omegaijOr 0. Wherein A isijWhen the value of (a) is a weight value, it indicates that an edge exists between the node i and the node j, and the weight value of the edge is omegaij，AijThe value of (1) is 0, which indicates that there is no connection relation between the node i and the node j (i.e. there is no edge between the node i and the node j).
In constructing the distribution network model, theoretically, there may be connections between the various order distribution points. In a specific implementation, to simplify the network model, for order distribution points in the same cell or the same parcel, n (n may be set according to a requirement, for example, set to be 2 or 3) order distribution points closest to the order distribution points may be selected to connect, and a connection may be made between two cells or two parcels, for example, two order distribution points with the shortest distance between two parcels may be selected to connect.
In an optional example, step S201 may specifically include: the method comprises the steps of converting distribution point coordinates of orders into nodes in a distribution network model, converting connection relations among the order distribution points into edges among the nodes, and converting logistics distribution difficulty comprehensive scores among the order distribution points into weights of the edges among the nodes.
Wherein the logistics distribution difficulty comprehensive score among the order distribution points can be determined according to at least one of the following influence factors: real path length between order distribution points, road conditions between order distribution points, ease of parking between order distribution points, weight of on-board cargo between order distribution points. For example, in one specific example, the logistics distribution difficulty composite score between the order distribution points can be determined according to the real path length between the order distribution points, the road conditions between the order distribution points, the parking difficulty between the order distribution points and the vehicle-mounted cargo weight between the order distribution points. Table 1 shows the scoring of these four influencing factors.
TABLE 1
Further, in this particular example, the weights of these four influencing factors are w respectivelyi(i is 1,2,3,4), and
score of each influencing factor is ci(i is 1,3,5), and the logistics distribution difficulty between the order distribution point i and the order distribution point j is comprehensively scored as
In specific implementation, the values of the weights of the four influencing factors can be flexibly set according to an application scene.
Step S202, clustering nodes in the distribution network model to obtain a plurality of clusters; and taking the range covered by each of the clusters as a distribution area to obtain a plurality of distribution areas.
In an optional example, step S202 specifically includes: step 1 to step 3.
In this step, X may be selected from the set of order distribution points X ═ Xi|xiRandomly drawing K points from the e-R, i-1, 21,z2,...,zkThe K clusters can be denoted as W1,W2,...,Wk. Wherein K is an integer greater than 1. In specific implementation, the value of K can be flexibly set. For example, the value of K may be determined according to the total amount of orders to be delivered and the average value of the delivery amount of the historical region. Furthermore, in determiningAfter the initial positions of the K cluster central points, m order distribution points (the value of m can be set according to the requirement, for example, set to be 2 or 3) nearest to the cluster central point can be selected to connect with the cluster central point.
And 2, calculating the distance between each node in the distribution network model and each cluster center point, and distributing the nodes to the cluster with the minimum distance so as to realize cluster updating.
For example, in this step, the distance between a node and a cluster center point may be calculated according to the weight of each edge between the node and the cluster center point in the distribution network model and the real path length corresponding to each edge.
In an alternative embodiment of this example, when only one connecting road (the connecting road is a road formed by the existing edges) exists between a certain node i and a cluster center point k, the distance between the node i and the cluster center point k may be calculated according to the following formula:
wherein d isikIs the distance between node i and the cluster center point k, lrIs the true path length, C, of the r-th edge existing between the node i and the cluster center point krIs the weight of the r-th edge existing between node i to the cluster center point k. Further, when two or more connecting channel paths exist between a certain node i and a cluster central point k, a plurality of distances can be calculated through the formula, and the minimum value of the distances is used as the distance between the node i and the cluster central point k.
After calculating the distance of each node from the center point of each cluster, the node can be assigned to the cluster with the smallest distance. For example, assuming that three clusters a, b, and c are provided, the distance from the node 1 to the center point of the cluster a is 35, the distance from the node 1 to the center point of the cluster b is 25, and the distance from the node 1 to the center point of the cluster c is 40, the node 1 is classified into the cluster b.
And 3, calculating the position of the updated cluster center point until the iteration stop condition is met.
In this step, the position of the cluster center point may be calculated according to the updated coordinates of each node in the cluster, and the following formula may be specifically adopted:
wherein z iskAs the position of the cluster center point, nkFor the number of nodes contained in the updated cluster, x represents the cluster WkAny one of the nodes.
Wherein the iteration stop condition is flexibly set. For example, the iteration stop condition may be set to: the situation that the nodes are distributed to different cluster centers does not exist; the iteration stop condition may also be set to: the position of the cluster center point is unchanged or the error is smaller than a specified threshold value, and the like.
And step S203, planning an order distribution path in the distribution area according to a heuristic algorithm.
Illustratively, the planning of the order distribution route in the distribution area according to a heuristic algorithm comprises: determining a candidate order distribution path in the distribution area, and calculating the total distribution cost of the candidate distribution path according to the total vehicle dispatching cost and the total driving cost corresponding to the candidate order distribution path; and optimizing the candidate order distribution path by taking the minimum distribution total cost as an objective function until a planned order distribution path is obtained. In specific implementation, heuristic algorithms such as a simulated annealing algorithm and a variable-field dispersion search algorithm can be adopted for order distribution path planning.
In a practical application scenario, order delivery is performed while the return demand and the delivery demand of the customer are satisfied. When planning a distribution path for this practical application scenario, the following objective function may be set:
further, when planning a delivery path for this actual application scenario, constraints as shown in table 2 may be set.
TABLE 2
The following describes parameters involved in the objective function and the constraint, and the meaning of the constraint. Wherein,
v: a point set indicating distribution of express, V ═ V' ueq {0}, V ═ 1,2,. n.
i, j: an index representing the order delivery point.
k: indicating an index of the delivery vehicle.
Kt: a set of types t representing delivery vehicles.
T: representing a collection of delivery vehicles.
t: an index indicating the type of delivery vehicle.
Qt: indicating the vehicle capacity of the delivery vehicle type t.
Ztk: indicating the cost of dispatching the kth vehicle of delivery vehicle type t.
eij: indicating the driving costs of order delivery point i and order delivery point j.
pi: indicating the reverse demand (i.e., return demand) of the customer corresponding to the order delivery point i.
di: and the sending demand of the customer i corresponding to the order distribution point i is shown.
αijtk: and after the k-th vehicle with the delivery vehicle type t finishes delivering the order delivery point i, continuing to go to the order delivery point j to be 1, and otherwise, continuing to go to be 0.
γitk: the k-th vehicle responsible order delivery point i, which represents the delivery vehicle type t, is 1, and vice versa is 0.
εi: and the delivery vehicle pick-up quantities of the order delivery point i and the order delivery point j are represented.
Wherein the objective function represents an order distribution route for which the sum of the regional dispatch cost and the travel cost is expected to be minimized. The constraint condition (1) is used for ensuring that all customers have the service of the delivery vehicle; the constraint condition (2) is used for ensuring that only one vehicle serves between the point i and the point j; the constraint condition (3) is used for ensuring that the distribution vehicle is not overloaded; the constraint conditions (4) and (5) are used for constraining the loading of the delivery and pickup trucks; the constraint condition (6) is used for ensuring that the delivery vehicle returns to the delivery center after completing the delivery task; the constraint (7) is used for the constraint of the total number of the delivery vehicles; the constraints (8) to (11) are used to indicate symbol ranges.
And step S204, returning the planned order distribution path to the user terminal.
For example, the planned order delivery path may include: and sequencing the orders to be delivered according to the delivery sequence. For example, assuming that there are 200 orders to be delivered in a certain delivery area and 4 delivery vehicles, the order sequence in which each of the planned delivery vehicles is responsible and arranged according to the delivery sequence may be returned to the user terminal.
In specific implementation, when the courier finishes the delivery task and returns to the terminal end, or when the courier encounters a condition such as customer rejection, a trigger instruction can be input through the user terminal, so as to execute step S203 again and perform path planning again.
In the embodiment of the invention, the distribution areas can be dynamically and reasonably divided according to the order information to be distributed through the steps, so that the distribution orders in each distribution area tend to be balanced, and the distribution efficiency is further improved on the whole. Furthermore, path planning is carried out in the divided distribution areas based on a heuristic algorithm, and distribution efficiency can be further improved.
Fig. 4 is a schematic diagram of a position relationship between a node and a cluster center point according to an embodiment of the present invention. How to calculate the distance between the node and the cluster center point is described in detail below with reference to fig. 4.
As shown in fig. 4, two connecting path paths exist between the node 3 in the distribution network model and the cluster center point shown in the figure, one connecting path consists of an edge between the node 3 and the node 1, an edge between the node 1 and the cluster center point, and the other connecting path consists of an edge between the node 3 and the node 2, and an edge between the node 2 and the cluster center point. In calculating the distance between node 3 and the cluster center point, the following formula may be used:
d3z＝min{l31·C31+l1z·C1z,l32·C32+l2z·C2z}
wherein d is3zRepresents the distance, l, of node 3 from the cluster center point31Representing the true path length of the edge between node 3 and node 1, C31Weight, l, representing the edge between node 3 and node 11zRepresenting the true path length of node 1 from the edge of the cluster center point, C1zWeight of edge representing node 1 and the center point of the cluster, l32Representing the true path length of the edge between node 3 and node 2, C32Weight, l, representing the edge between node 3 and node 22zRepresenting the true path length of the edge of node 2 from the cluster center point, C2zRepresenting the weight of node 2 to the edge of the cluster center point. The above formula represents; taking the minimum distance value in the two connecting channel paths as the distance d between the node 3 and the cluster central point3z。
Fig. 5 is a schematic diagram of main blocks of a delivery planning apparatus according to a third embodiment of the present invention. As shown in fig. 5, a delivery planning apparatus 500 according to an embodiment of the present invention includes: a building module 501 and a clustering module 502.
A building module 501, configured to build a distribution network model according to the order information to be distributed.
Wherein the distribution network model is composed of nodes and edges between the nodes; the nodes are used for representing delivery points of orders (the order delivery points may also be referred to as "order receiving points", or "express delivery receiving points", etc.), and edges between the nodes are used for representing connection relations between the order delivery points. In addition, the edges between the nodes can also be provided with corresponding weight values.
In one example, the delivery network model may be denoted as G ═ V, E. Where V represents a set of nodes in the distribution network model and E represents a set of edges between the nodes in the distribution network model.
In another example, for ease of computation, the distribution network model may also be represented by an adjacency matrix a. In the adjacency matrix A, the element AijIs 1 or 0. Wherein A isijThe value of (A) is 1, which means that a connection relationship exists between the node i and the node j (namely, an edge exists between the node i and the node j), AijThe value of (1) is 0, which indicates that there is no connection relation between the node i and the node j (i.e. there is no edge between the node i and the node j).
In yet another example, for ease of calculation, the distribution network model may also be represented by a weighted adjacency matrix a'. In weight adjacency matrix A ', element A'ijIs taken as the weighted value omegaijOr 0. Wherein A isijWhen the value of (a) is a weight value, it indicates that an edge exists between the node i and the node j, and the weight value of the edge is omegaij，AijThe value of (1) is 0, which indicates that there is no connection relation between the node i and the node j (i.e. there is no edge between the node i and the node j).
In specific implementation, to simplify the network model, for order distribution points in the same cell or the same parcel, n (n may be set according to requirements, for example, set to be 2 or 3) order distribution points closest to the order distribution points may be selected to connect, and a connection may be made between two cells or two parcels, for example, two order distribution points with the shortest distance between two parcels may be selected to connect.
A clustering module 502, configured to cluster nodes in the distribution network model to obtain multiple clusters; and taking the range covered by each of the clusters as a distribution area to obtain a plurality of distribution areas.
For example, a k-means clustering algorithm or other clustering algorithm may be used to cluster the nodes in the distribution network model.
In an optional embodiment, the clustering module 502 clusters the nodes in the distribution network model by using a k-means clustering algorithm to obtain a plurality of clusters includes: the clustering module 502 determines initial positions of the K cluster center points; the clustering module 502 calculates the distance between each node in the distribution network model and the center point of each cluster, and distributes the node to the cluster with the minimum distance to update the cluster; the clustering module 502 calculates the position of the updated cluster center point until the iteration stop condition is satisfied. Wherein K is an integer greater than 1. In specific implementation, the value of K can be flexibly set. For example, the value of K may be determined according to the total amount of orders to be delivered and the average value of the delivery amount of the historical region.
In the device of the embodiment of the invention, the construction module constructs the distribution network model according to the order information to be distributed, the clustering module clusters the nodes in the distribution network model to obtain a plurality of clusters, the range covered by each cluster is used as a distribution area to obtain a plurality of distribution areas, the distribution areas can be dynamically and reasonably divided according to the order information to be distributed, so that the distribution orders in each distribution area tend to be balanced, and the distribution efficiency is improved integrally.
Fig. 6 is a schematic diagram of main blocks of a delivery planning apparatus according to a fourth embodiment of the present invention. As shown in fig. 6, a delivery planning apparatus 600 according to an embodiment of the present invention includes: a building module 601, a clustering module 602, and a path planning module 603.
The building module 601 is configured to build a distribution network model according to the order information to be distributed.
The distribution network model constructed by the embodiment of the invention consists of nodes and edges among the nodes. The nodes are used for representing the distribution points of the orders, and the edges among the nodes are used for representing the connection relation among the distribution points of the orders. In addition, the edges between the nodes can also be provided with corresponding weight values.
Further, for ease of computation, the distribution network model may be represented by a weighted adjacency matrix a'. In weight adjacency matrix A ', element A'ijIs taken as the weighted value omegaijOr 0. Wherein A isijWhen the value of (a) is a weight value, it indicates that an edge exists between the node i and the node j, and the weight value of the edge is omegaij，AijThe value of (1) is 0, which indicates that there is no connection relation between the node i and the node j (i.e. there is no edge between the node i and the node j).
In constructing the distribution network model, theoretically, there may be connections between the various order distribution points. In a specific implementation, to simplify the network model, for order distribution points in the same cell or the same parcel, n (n may be set according to a requirement, for example, set to be 2 or 3) order distribution points closest to the order distribution points may be selected to connect, and a connection may be made between two cells or two parcels, for example, two order distribution points with the shortest distance between two parcels may be selected to connect.
In an alternative example, the building module 601 building the delivery network model according to the order information to be delivered may specifically include: the building module 601 converts the coordinates of the order distribution points into nodes in the distribution network model, the building module 601 converts the connection relationship between the order distribution points into edges between the nodes, and converts the logistics distribution difficulty comprehensive scores between the order distribution points into the weights of the edges between the nodes.
Wherein the logistics distribution difficulty comprehensive score among the order distribution points can be determined according to at least one of the following influence factors: real path length between order distribution points, road conditions between order distribution points, ease of parking between order distribution points, weight of on-board cargo between order distribution points. For example, in one specific example, the logistics distribution difficulty composite score between the order distribution points can be determined according to the real path length between the order distribution points, the road conditions between the order distribution points, the parking difficulty between the order distribution points and the vehicle-mounted cargo weight between the order distribution points.
A clustering module 602, configured to cluster nodes in the distribution network model to obtain multiple clusters; and the system is further configured to regard a range covered by each of the plurality of clusters as a distribution area to obtain a plurality of distribution areas.
In an optional example, the clustering module 602 performs clustering on the nodes in the distribution network model to obtain a plurality of clusters specifically includes: step 1 to step 3.
In this step, clustering module 602 may select X from the set of order delivery pointsi|xiRandomly drawing K points from the e-R, i-1, 21,z2,…,zkThe K clusters can be denoted as W1,W2,…,Wk. Wherein K is an integer greater than 1. In specific implementation, the value of K can be flexibly set. For example, the value of K may be determined according to the total amount of orders to be delivered and the average value of the delivery amount of the historical region. In addition, after the initial positions of the K cluster center points are determined, m order distribution points closest to the cluster center points (the value of m can be set according to requirements, for example, set to be 2 or 3) can be selected to be connected with the cluster center points.
For example, in this step, the clustering module 602 may calculate the distance between a node and a cluster center point according to the weight of each edge between the node and the cluster center point in the distribution network model and the real path length corresponding to each edge.
In an alternative embodiment of this example, when there is only one connecting road (the connecting road refers to a road formed by the existing edges) between a certain node i and a cluster central point k, the clustering module 602 may calculate the distance between the node i and the cluster central point k according to the following formula:
wherein d isikIs the distance between node i and the cluster center point k, lrIs the true path length, C, of the r-th edge existing between the node i and the cluster center point krIs the weight of the r-th edge existing between node i to the cluster center point k. Further, when two or more connecting paths exist between a certain node i and a cluster center point k, the clustering module 602 may calculate a plurality of distances through the above formula, and use a minimum value of the plurality of distances as a distance between the node i and the cluster center point k.
After calculating the distance of each node from the center point of each cluster, the node can be assigned to the cluster with the smallest distance. For example, assuming that three clusters a, b, and c are provided, the distance from the node 1 to the center point of the cluster a is 35, the distance from the node 1 to the center point of the cluster b is 25, and the distance from the node 1 to the center point of the cluster c is 40, the node 1 is classified into the cluster b.
And 3, calculating the position of the updated cluster center point by the clustering module 602 until the iteration stop condition is met.
In this step, the clustering module 602 may calculate the position of the cluster center point according to the updated coordinates of each node in the cluster, and may specifically adopt the following formula:
wherein z iskAs the position of the cluster center point, nkFor the number of nodes contained in the updated cluster, x represents the cluster WkAny one of the nodes.
Wherein the iteration stop condition is flexibly set. For example, the iteration stop condition may be set to: the situation that the nodes are distributed to different cluster centers does not exist; the iteration stop condition may also be set to: the position of the cluster center point is unchanged or the error is smaller than a specified threshold value, and the like.
And the path planning module 603 is configured to plan an order distribution path in the distribution area according to a heuristic algorithm, and return the planned order distribution path to the user terminal.
Illustratively, the path planning module 603 performing order distribution path planning in the distribution area according to a heuristic algorithm includes: the path planning module 603 determines a candidate order distribution path in the distribution area, and calculates a total distribution cost of the candidate distribution path according to a total vehicle dispatching cost and a total driving cost corresponding to the candidate order distribution path; and optimizing the candidate order distribution path by taking the minimum distribution total cost as an objective function until a planned order distribution path is obtained. In specific implementation, the path planning module 603 may perform order distribution path planning by using heuristic algorithms such as simulated annealing algorithm and variable-field dispersion search algorithm.
In the device provided by the embodiment of the invention, the distribution areas can be dynamically and reasonably divided according to the order information to be distributed through the building module and the clustering module, so that the distribution orders in each distribution area tend to be balanced, and the distribution efficiency is further improved integrally. Furthermore, the path planning module carries out path planning in the divided distribution areas based on a heuristic algorithm, so that the distribution efficiency can be further improved.
Fig. 7 illustrates an exemplary system architecture 700 to which the delivery planning method or delivery planning apparatus of an embodiment of the present invention may be applied.
As shown in fig. 7, the system architecture 700 may include terminal devices 701, 702, 703, a network 704, and a server 705. The network 704 serves to provide a medium for communication links between the terminal devices 701, 702, 703 and the server 705. Network 704 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may use the terminal devices 701, 702, 703 to interact with a server 705 over a network 704, to receive or send messages or the like. Various communication client applications, such as a logistics distribution management application, a shopping application, a web browser application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like, may be installed on the terminal devices 701, 702, and 703.
The terminal devices 701, 702, 703 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 705 may be a server that provides various services, such as a background management server that supports a logistics distribution management application or a website browsed by a user using the terminal devices 701, 702, and 703. The back-office management server may analyze and perform other processing on the received data such as the delivery planning request, and feed back a processing result (for example, a delivery area division result) to the terminal device.
It should be noted that the distribution planning method provided by the embodiment of the present invention is generally executed by the server 705, and accordingly, the distribution planning apparatus is generally disposed in the server 705.
It should be understood that the number of terminal devices, networks, and servers in fig. 7 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 8, shown is a block diagram of a computer system 800 suitable for use in implementing an electronic device of an embodiment of the present invention. The computer system illustrated in FIG. 8 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the invention.
As shown in fig. 8, the computer system 800 includes a Central Processing Unit (CPU)801 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data necessary for the operation of the system 800 are also stored. The CPU 801, ROM 802, and RAM 803 are connected to each other via a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, a mouse, and the like; an output section 807 including a signal such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. A drive 810 is also connected to the I/O interface 805 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted on the storage section 808 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program executes the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 801.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor comprises a construction module and a clustering module. Where the names of these modules do not in some cases constitute a limitation on the module itself, for example, a building module may also be described as a "module building a distribution network model".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: constructing a distribution network model according to the order information to be distributed; wherein the distribution network model is composed of nodes and edges between the nodes; the nodes are used for representing the distribution points of the orders, and edges among the nodes are used for representing the connection relation among the distribution points of the orders; clustering nodes in the distribution network model to obtain a plurality of clusters; and taking the range covered by each of the clusters as a distribution area to obtain a plurality of distribution areas.
According to the technical scheme of the embodiment of the invention, the distribution network model is built according to the order information to be distributed, the nodes in the distribution network model are clustered to obtain a plurality of clusters, and the range covered by each cluster is used as a distribution area to obtain a plurality of distribution areas.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A delivery planning method, characterized in that the method comprises:
constructing a distribution network model according to the order information to be distributed; wherein the distribution network model is composed of nodes and edges between the nodes; the nodes are used for representing the distribution points of the orders, and edges among the nodes are used for representing the connection relation among the distribution points of the orders;
clustering nodes in the distribution network model to obtain a plurality of clusters; and taking the range covered by each of the clusters as a distribution area to obtain a plurality of distribution areas.
2. The method of claim 1, wherein constructing a delivery network model based on order information to be delivered comprises:
the method comprises the steps of converting distribution point coordinates of orders into nodes in a distribution network model, converting connection relations among the order distribution points into edges among the nodes, and converting logistics distribution difficulty comprehensive scores among the order distribution points into weights of the edges among the nodes.
3. The method of claim 2, wherein clustering nodes in the distribution network model to obtain a plurality of clusters comprises:
determining initial positions of K cluster center points; calculating the distance between each node in the distribution network model and the central point of each cluster, and distributing the nodes to the cluster with the minimum distance so as to realize the updating of the clusters; calculating the position of the updated cluster center point until an iteration stop condition is met; wherein K is an integer greater than 1.
4. The method of claim 3, wherein calculating the distance of each node in the distribution network model from the respective cluster center point comprises:
and calculating the distance between a node and the cluster center point according to the weight of each edge between the node and the cluster center point in the distribution network model and the real path length corresponding to each edge.
5. The method according to claim 1, characterized in that it comprises:
and after obtaining a plurality of distribution areas, planning an order distribution path in the distribution areas according to a heuristic algorithm, and returning the planned order distribution path to the user terminal.
6. The method of claim 5, wherein said planning the order delivery paths within said delivery area according to a heuristic algorithm comprises:
determining a candidate order distribution path in the distribution area, and calculating the total distribution cost of the candidate distribution path according to the total vehicle dispatching cost and the total driving cost corresponding to the candidate order distribution path; and optimizing the candidate order distribution path by taking the minimum distribution total cost as an objective function until a planned order distribution path is obtained.
7. The method of claim 2, wherein the method comprises: determining a logistics distribution difficulty comprehensive score among the order distribution points according to at least one of the following influence factors: real path length between order distribution points, road conditions between order distribution points, ease of parking between order distribution points, weight of on-board cargo between order distribution points.
8. A delivery planning apparatus, comprising:
the construction module is used for constructing a distribution network model according to the order information to be distributed; wherein the distribution network model is composed of nodes and edges between the nodes; the nodes are used for representing the distribution points of the orders, and edges among the nodes are used for representing the connection relation among the distribution points of the orders;
the clustering module is used for clustering the nodes in the distribution network model to obtain a plurality of clusters; and the system is further configured to regard a range covered by each of the plurality of clusters as a distribution area to obtain a plurality of distribution areas.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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