CN111950950A - Order distribution path planning method and device, computer medium and electronic equipment - Google Patents

Order distribution path planning method and device, computer medium and electronic equipment Download PDF

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CN111950950A
CN111950950A CN201910412862.8A CN201910412862A CN111950950A CN 111950950 A CN111950950 A CN 111950950A CN 201910412862 A CN201910412862 A CN 201910412862A CN 111950950 A CN111950950 A CN 111950950A
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path set
path
point
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郭震
董红宇
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Beijing Jingdong Zhenshi Information Technology Co Ltd
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Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
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Abstract

The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for planning an order distribution path, and a computer-readable storage medium and an electronic device for implementing the method for planning an order distribution path. Wherein, the method comprises the following steps: acquiring first actual state information on a node and second actual state information on a delivery vehicle, the node including: a distribution center site, an energy adding point and an order delivery point; determining a primary selection path set according to the first actual state information and the second actual state information; and optimizing and processing order delivery points in at least one path in the primary selection path set through a destroy reconstruction algorithm so as to plan the primary selection path set to obtain an optimized path set. The technical scheme is beneficial to improving the accuracy of the path planning result and reducing the distribution cost as much as possible, thereby providing a solution to the EVRP problem suitable for practical application.

Description

Order distribution path planning method and device, computer medium and electronic equipment
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for planning an order distribution path, and a computer-readable storage medium and an electronic device for implementing the method for planning an order distribution path.
Background
The Vehicle Routing Problem (VRP) refers to a certain number of customers, each having a different number of goods required, and a distribution center provides the customers with goods, and a fleet of vehicles is responsible for distributing the goods and organizing appropriate driving paths, with the goal of satisfying the customer's requirements and achieving the goals of shortest distance, minimum cost, minimum time consumption and the like under certain constraint conditions.
With the enhancement of environmental awareness of people, green travel is a advocated travel mode. For example, fuel-efficient vehicle cargo distribution is gradually progressing toward clean-type vehicle (e.g., electric vehicle, clean energy vehicle, etc.) cargo distribution. Therefore, the following description may take the VRP in the process of delivering orders (goods and goods) by using an Electric Vehicle as a customer as an example, and may also be referred to as an Electric Vehicle Routing Problem (EVRP). The electric vehicle is used for keeping the sufficient transportation capacity of the electric vehicle by means of energy addition in the order distribution process, and further ensuring the smooth delivery of orders. Therefore, the travel of the electric vehicle with energy adding points (such as charging piles and the like) is a novel business scene, and the planning of the vehicle travel path which meets the business scene as optimally as possible has great significance on the transportation capacity cost, the distribution timeliness and the like.
However, there are few patents and documents available in the related art that are directed to a distribution route solution for large-scale new energy electric vehicles, and particularly, to route planning for electric vehicles with energy addition points (e.g., charging piles, etc.). Meanwhile, the existing related technology does not relate to the large-scale EVRP problem in the actual scene and the actual project application. Therefore, a solution to the EVRP problem suitable for practical use is urgently needed.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the embodiments of the present disclosure is to provide an order distribution path planning method, an order distribution path planning apparatus, and a computer-readable storage medium and an electronic device for implementing the order distribution path planning method, thereby providing at least one solution to the EVRP problem suitable for practical applications.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of the embodiments of the present disclosure, there is provided a method for planning an order delivery path, including:
acquiring first actual state information on a node and second actual state information on a delivery vehicle, the node including: a distribution center site, an energy adding point and an order delivery point;
determining a primary selection path set according to the first actual state information and the second actual state information;
and optimizing and processing order delivery points in at least one path in the primary selection path set through a destroy reconstruction algorithm so as to plan the primary selection path set to obtain an optimized path set.
In some embodiments of the present disclosure, based on the foregoing scheme, the distribution vehicle is an electric vehicle, and the energy addition point is a charging station including at least one charging pile.
In some embodiments of the present disclosure, based on the foregoing scheme, the first actual state information includes: at least one of navigation information, distance information, time information required for reaching different nodes and time window constraint information of the nodes among the nodes;
the second actual state information includes: at least one of a maximum load capacity, and a range of the delivery vehicle.
In some embodiments of the present disclosure, based on the foregoing scheme, determining a primary selection path set according to the first actual state information and the second actual state information includes:
determining first vector information related to the first actual state information and second vector information related to the second actual state information;
and inputting the first vector information and the second vector information into a greedy algorithm, and determining an initial selection path set according to the output of the greedy algorithm.
In some embodiments of the present disclosure, based on the foregoing scheme, optimizing and processing an order arrival point in at least one path in the primary selection path set by using a destroy reconstruction algorithm to plan the primary selection path set to obtain an optimized path set, including:
determining a preset number of destroying points in order delivery points in the primary selection path set according to a density-based clustering algorithm, and removing the destroying points from the primary selection path set through a removing operation to obtain an intermediate path set;
and inserting the destroy point into the intermediate path set based on a preset strategy so as to complete the planning of the primary selection path set and obtain an optimized path set.
In some embodiments of the present disclosure, based on the foregoing scheme, inserting the destroy point into the intermediate path set based on a preset policy includes:
based on an iterative computation strategy, inserting the destruction point into the intermediate path set according to a comparison result of distribution cost change conditions of the path set before and after the ith insertion operation; wherein i is a positive number which is greater than or equal to 1 and less than or equal to the preset iteration times.
In some embodiments of the present disclosure, based on the foregoing scheme, inserting the destroy point into the intermediate path set according to a comparison result of distribution cost variation conditions of the path set before and after the ith insertion operation includes:
calculating the distribution cost as a standard value for the initial selection path set;
inserting the destroy point into the intermediate path set for the ith time to obtain a path set to be evaluated after the insertion operation for the ith time;
calculating distribution cost as an alternative value for the path set to be evaluated after the ith insertion operation;
in response to the alternative value being greater than the standard value, then:
recording a path set to be evaluated after the ith insertion operation;
and updating the standard value by using the alternative value, inserting the (i + 1) th time of the destruction point into the intermediate path set until the iteration number reaches the preset iteration number, and returning the recorded path set to be evaluated as an optimized path set.
In some embodiments of the present disclosure, based on the foregoing scheme, the method for planning an order distribution route further includes:
in response to the alternate value being less than or equal to the standard value, the point of destruction is inserted into the intermediate path set an (i + 1) th time.
In some embodiments of the present disclosure, based on the foregoing scheme, inserting the destroy point into the intermediate path set based on a preset policy includes:
for any point to be inserted, performing insertion scoring according to the following formula, and inserting each point to be inserted into the intermediate path set according to the insertion scoring;
Figure BDA0002063394130000041
wherein, f (x | x)p,w0,w1) Representing the intermediate path set score,
x denotes the point of destruction to be inserted, xpIndicating the priority of the point of destruction x to be inserted,
w0、w1respectively representing the optimal distribution cost value and the suboptimal distribution cost value of the path before the point x of destruction and satisfying w0<w1，
y、
Figure BDA0002063394130000042
Is an empirical value, wherein y takes the value of 11,
s(x,w0) For the point of destruction x and the current optimal distribution cost value w0Cost calculation subfunction of, and w0The corresponding path starting position, path ending station position, time window distance and time window parameter are related.
According to a second aspect of the embodiments of the present disclosure, there is provided an order distribution path planning apparatus, including:
a status information acquisition module for acquiring first actual status information about a node and second actual status information about a delivery vehicle, the node comprising: a distribution center site, an energy adding point and an order delivery point;
a primary selection path set determining module, configured to determine a primary selection path set according to the first actual state information and the second actual state information;
and the path optimization module is used for optimizing and processing order delivery points in at least one path in the primary selection path set through a destroy reconstruction algorithm so as to plan the primary selection path set to obtain an optimized path set.
According to a third aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the method for planning an order delivery path according to the first aspect of the embodiments.
According to a fourth aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including: one or more processors; a storage device, configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the method for planning an order delivery route according to the first aspect of the embodiments.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
in the technical scheme provided by some embodiments of the present disclosure, on one hand, an initial route set is determined according to actual state information about nodes (including a distribution center site, an energy addition point and an order arrival point) and actual state information about distribution vehicles, and the route set determined by the actual information is beneficial to improving the accuracy and applicability of a route planning result, so that a high-quality route planning scheme is provided for practical application. On the other hand, after the initial selection path set is determined, the order delivery points in the initial selection path set are optimized through a destroy reconstruction algorithm so as to achieve the effect of optimizing the path set, and the distribution cost can be further reduced on the basis of ensuring the feasibility of the path through the optimized path set.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty. In the drawings:
FIG. 1 is a schematic diagram illustrating a prior art method for planning an order delivery path;
FIG. 2 schematically illustrates a usage scenario diagram of EVRP according to an embodiment of the present disclosure;
FIG. 3 schematically shows a flowchart of a method for determining a set of primary paths according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow diagram of a path optimization method according to an embodiment of the disclosure;
FIG. 5 schematically illustrates a cluster distribution diagram of order delivery points, according to an embodiment of the disclosure;
FIG. 6 is a flow chart diagram schematically illustrating a method for determining a set of primary paths according to an embodiment of the present disclosure;
fig. 7 schematically shows a structural diagram of an order distribution path planning apparatus according to an embodiment of the present disclosure;
fig. 8 schematically illustrates a computer-readable storage medium for implementing the above-described order delivery path planning method; and the number of the first and second groups,
fig. 9 schematically shows an example block diagram of an electronic device for implementing the above-described order delivery path planning method.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
Fig. 1 schematically shows a flow chart of an order distribution path planning method according to an embodiment of the present disclosure, which may provide at least one solution to the EVRP problem suitable for practical applications.
The execution subject of the method for planning an order distribution route provided by this embodiment may be a device having a calculation processing function, such as a server.
Referring to fig. 1, the method for planning an order delivery path according to the embodiment includes:
step S101 of acquiring first actual state information on a node including: a distribution center site, an energy adding point and an order delivery point;
step S102, determining a primary selection path set according to the first actual state information and the second actual state information; and the number of the first and second groups,
and S103, optimizing and processing order delivery points in at least one path in the primary selection path set through a destroy reconstruction algorithm to plan the primary selection path set to obtain an optimized path set.
In the technical solution provided by the embodiment shown in fig. 1, on one hand, a primary route set is determined according to actual state information about nodes (including a distribution center station, an energy adding point and an order arrival point) and actual state information about distribution vehicles, and the route set determined by the actual information is beneficial to improving the accuracy and applicability of a route planning result, so that a high-quality route planning scheme is provided for practical application. On the other hand, after the initial selection path set is determined, the order delivery points in the initial selection path set are optimized through a destroy reconstruction algorithm so as to achieve the effect of optimizing the path set, and the distribution cost can be further reduced on the basis of ensuring the feasibility of the path through the optimized path set.
Specific embodiments of the individual steps in the example shown in fig. 1 are explained in detail below.
In an exemplary embodiment, the distribution vehicle is an electric vehicle, and the energy addition point is a charging station including at least one charging pile. In the order distribution process realized by using the electric vehicle, the electric vehicle is charged in time through the charging station pile so as to keep the transport capacity of the electric vehicle sufficient and further ensure the accurate delivery of the order.
Fig. 2 schematically shows a usage scenario diagram of an EVRP according to an embodiment of the present disclosure. Referring to fig. 2, including at least one distribution center site 20, the distribution vehicle loads (orders) from the distribution center site 20, and further, dispatches the orders to the respective order delivery points (order delivery point 21, order delivery points 22, …, order delivery point 28). On the way of the electric vehicles to distribute orders, the electric vehicles can be charged through the energy adding point 21 ', the energy adding point 22 ' or the energy adding point 23 ' to ensure that the orders are smoothly delivered to the corresponding order delivery points, and the electric vehicles have electric quantity to return to the distribution center station 20 after completing the distribution of the orders to the respective corresponding order delivery points.
Note that the number of distribution center sites in the EVRP scenario is not limited to one, and a distribution vehicle may start to distribute orders from one distribution center site and return to any one distribution center site. In the embodiment of the present disclosure, a single distribution center station 20 is taken as an example for explanation, that is, the distribution vehicle starts to distribute the order from the distribution center station 20 and can return to the distribution center station 20.
In the exemplary embodiment, first actual state information about the nodes and second actual state information about the delivery vehicles are acquired in step S101 for use in determining the set of initially selected paths in step S102. Wherein, the first actual state information includes but is not limited to: navigation information among the nodes (e.g., a navigation path from the distribution center station 20 to the order delivery point 21 or a navigation path from the order delivery point 21 to the distribution center station 20 according to actual road conditions), distance information among the nodes (e.g., an actual path distance from the distribution center station 20 to the order delivery point 21 according to actual road conditions, etc.), time information required for reaching different nodes, time window constraint information of different nodes (e.g., a time window for delivering the order to the point 22 is 9:30-11:30, and if the distribution vehicle 9:00 arrives, the earliest time of the distribution vehicle advance time window needs to wait, thereby increasing distribution cost), and the like. For example, the first actual state information may be obtained by: and obtaining the first actual state information through the crawling road condition information of the actual physical road network. The path planning of the EVRP is realized through the actual road condition information, so that the planned path has actual use significance, the applicability is improved, and the use experience of a user is improved.
In an exemplary embodiment, the second actual state information includes, but is not limited to: the maximum load capacity, maximum payload, range, etc. of the delivery vehicle.
In an exemplary embodiment, in step S102, a primary selection path set is determined according to the first actual state information and the second actual state information. Fig. 3 schematically shows a flowchart of a method for determining a primary selection path set according to an embodiment of the present disclosure, which may be used in a specific implementation manner of step S102. Referring to fig. 3, the method includes:
step S301, determining first vector information related to the first actual state information and second vector information related to the second actual state information; and the number of the first and second groups,
step S302, inputting the first vector information and the second vector information into a greedy algorithm, and determining an initial selection path set according to the output of the greedy algorithm.
In an exemplary embodiment, the first actual state information/the second actual state information described above is related to constraints that the planned path needs to satisfy. In this embodiment, the first actual state information is converted into related first vector information, and the second actual state information is converted into related second vector information. Thus, the vector information is used as input data of the greedy algorithm. To determine an initial set of primary selection paths according to the output of the greedy algorithm. According to the first actual state information/the second actual state information, the output of the greedy algorithm satisfies a constraint condition:
a performance time window constraint, where a vehicle must wait if it arrives at the earliest time of the time window in advance, thereby increasing distribution costs due to the wait;
a maximum capacity constraint of the vehicle;
maximum load constraint of the vehicle;
the driving mileage of the electric vehicle is restricted, the electric quantity which is larger than 0 is ensured before the vehicle reaches the charging pile, otherwise, the electric vehicle is considered to be an infeasible solution.
For example, referring to FIG. 2, with respect to one of the delivery paths that satisfies the preset constraints: distribution center site 20-path 201-order to point 24-path 202-energy addition point 22' -path 203-order to point 27-path 203-distribution center site 20. If, during this process, the delivery vehicle after the successful delivery of the order to point 24 does not pass through the energy addition point 22' but goes directly to the next order to point 27 via path 205, delivery failure may result because the range cannot be met to the next order to point 27. Therefore, the delivery route corresponding to the order to point 24-route 205-order to point 27 may be considered as infeasible.
In an exemplary embodiment, after the first vector information and the second vector information are input into a greedy algorithm, a set of initial selection paths can be determined according to an output of the greedy algorithm. And each path in the initial path set meets the constraint condition.
In the technical solution provided by the embodiment shown in fig. 3, the initial path set is determined based on a greedy algorithm. Specifically, the constraint condition constructed by the actual state information may be used as an input of a greedy algorithm, so that the initial path set may be determined by an output of the greedy algorithm. The technical scheme provided by this embodiment has good applicability in a large EVRP scene with more than 1500 nodes based on the distance data, navigation data, time data, etc. (first actual state information) of the actual crawling point of the physical road condition information and the nodes, and the performance of the delivery vehicle itself (second actual state information).
The initially selected path set obtained in step S102 may be used as a set of initial solutions to optimize the paths based on the initial solutions (i.e., step S103), so as to achieve the purpose of further saving the distribution cost based on satisfying the constraint.
With continued reference to fig. 1, in step S103, the order delivery point in at least one path in the primary selection path set is optimized and processed through a destroy reconstruction algorithm to plan the primary selection path set to obtain an optimized path set.
Among them, the destructive reconstruction (Ruin and Recreate) algorithm refers to an iterative algorithm that determines a better solution among initial solutions. Specifically, a more optimal solution is searched for in the initial solution through an iterative operation, wherein the iterative process generally includes: the method comprises the steps of selecting a damaged node set, namely a damaged point, through a destroy (Ruin) strategy, and then inserting the damaged point into a path again through a rebuild (Recreate) strategy, so that the result is better (the specific assignment in the technical scheme is lower in transmission cost).
In the technical scheme, the run and trade algorithm is adopted, and specifically comprises the following steps: on the basis of an initial solution (initial path set), a more optimal solution is found through a run strategy and a reproduce strategy, namely, a path set with the lowest delivery cost is searched iteratively, namely, the initial path set is optimized, and an optimized path set is obtained. When the distribution vehicle uses the optimized path set to distribute orders, the preset constraint conditions can be met, and the distribution cost can be reduced as much as possible.
In an exemplary embodiment, fig. 4 schematically illustrates a flowchart of a path optimization method according to an embodiment of the present disclosure, which may be used in one specific implementation manner of step S103. Specifically, on the basis of an initial solution (an initial path set), a better solution is searched through a Ruin and Recreate strategy, an optimal solution is searched iteratively, a better planning path meeting the optimization condition is found, and the iterative process comprises the following steps: and selecting a damaged node set (a point of destruction) through a Ruin strategy, and then inserting the point of destruction into the path again through a Recreate strategy.
With particular reference to fig. 4, the method comprises:
step S401, determining a preset number of destroying points in order delivery points in the primary selection path set according to a density-based clustering algorithm;
step S402, removing the destroying point from the primary path set through removing operation to obtain an intermediate path set; and the number of the first and second groups,
and S403, inserting the destroy point into the intermediate path set based on a preset strategy so as to complete the planning of the primary path set and obtain an optimized path set.
In an exemplary embodiment, the destroy point refers to a part of order delivery points in the primary selection path set. Because the current distribution cost is not optimal due to the distribution positions of the order delivery points in the current path, the destroy points (a part of order delivery points) are determined in the initially selected path set, and then the distribution positions of the destroy points in the path set are updated. After that, it is inserted again to the updated distribution position. Therefore, the optimization of the initial selection path set is realized, and the purpose of reducing the distribution cost is achieved.
The above-mentioned destroying point may be determined in the initial path set by using the above-mentioned clustering algorithm.
In an exemplary embodiment, in step S401, a Noise-Based Density Clustering of Applications with Noise (DBSCAN) is used to determine a preset number of points of destruction among the order delivery points in the initially selected set of paths. Wherein, DBSCAN both can be applicable to convex sample set, also can be applicable to non-convex sample set to can adapt to actual order more and send the distribution state of point, and choose out the point of destroying a ware from it.
The higher the probability that the power demand of the delivery vehicle is high in the area where the order arrival point distribution density is high, relative to the area where the order arrival point distribution density is low. Therefore, the order delivery points with the preset number are selected as the destruction points in the area with the higher order delivery point distribution density, and the electric vehicle can acquire the effective charging pile to supplement the charging pile in the distribution process.
Illustratively, fig. 5 schematically shows a cluster distribution diagram of order delivery points according to an embodiment of the present disclosure. Referring to fig. 5, according to the preset algorithm hyper-parameters: the cluster density radius, and the minimum number of samples (e.g., 2) in the cluster, perform DBSCAN clustering on the order delivery points, and form a cluster 51 (including order delivery point 25 and order delivery point 23), a cluster 52 (including order delivery point 22 and order delivery point 23), and a cluster 53 (including order delivery point 26 and order delivery point 28) with reference to fig. 5.
Where the order delivery point 23 in the upper right corner of fig. 5 belongs to both cluster 51 and cluster 52, the order delivery points 22, 23, 25 can be divided into samples of the same cluster. When selecting the points to be destroyed according to the preset number of the points to be destroyed, assuming that 2 nodes to be destroyed are selected from the cluster at the upper right corner, the order delivery point 25 and the order delivery point 23 may be selected, and the order delivery point 22 and the order delivery point 23 may be selected.
In an exemplary embodiment, when a solution requiring specified destruction (destruction point) is selected by DBSCAN clustering, if an energy addition point is selected, an order delivery point needs to be selected again. If the first iteration selection fails, the second iteration selection is needed until the preset number of the destroying nodes is selected, and if the preset iteration number is reached, the requirement of the preset number of the destroying nodes is not met, the number of the preset destroying nodes is automatically reduced by 1. It can be seen that the run strategy in step S401 considers the particularity of the energy adding points without using them as the destroying points, and in addition, density clustering and dynamically adjusting the number of the destroying points are used to satisfy the requirement of the algorithm for fast calculating response in practical application to obtain a solution with excellent quality, and the algorithm has a good robustness.
In an exemplary embodiment, with continued reference to fig. 4, after determining a destroy point, in step S402, the destroy point is removed from the primary selection path set through a removal operation, so as to obtain an intermediate path set. Further, in step S403, the destroy point is inserted into the intermediate path set based on a preset policy, so as to complete planning of the initially selected path set to obtain an optimized path set.
For example, the preset policy may be an iterative computation policy, and the destroy point is inserted into the intermediate path set according to a comparison result of distribution cost change conditions of the path set before and after the ith insertion operation; wherein i is a positive number which is greater than or equal to 1 and less than or equal to the preset iteration number M.
In an exemplary embodiment, fig. 6 schematically illustrates a flowchart of a method for determining a primary selection path set according to an embodiment of the present disclosure, which may be used in a specific implementation manner that the preset policy in step S403 may be an iterative computation policy. Referring to fig. 6, the method includes steps S601 to S606.
In step S601, for the preliminary selection route set, the delivery cost is calculated as a standard value.
In an exemplary embodiment, for an initial selection path set corresponding to an initial solution output by a greedy algorithm, a delivery cost X based on the initial selection path set is calculated0The distribution cost X can be calculated0Record as the standard value X0. And comparing the distribution cost of the path set (namely the path set to be evaluated) formed by inserting the destroy point into the intermediate path set.
In step S602, the destroy point is inserted into the intermediate path set for the ith time, so as to obtain a path set to be evaluated after the insertion operation for the ith time.
In an exemplary embodiment, whether the preset constraint is violated after the destroy point selected in the above step is inserted into the intermediate path set. For example, if the vehicle maximum mileage constraint, the maximum capacity and load constraint, or the time window constraint of the route on which the node is located is violated, it is determined that the video fails to be inserted. Illustratively, for the same destroy point, if the number of times of continuous failures of the insertion operation is greater than the preset value N, the insertion position meeting the preset constraint cannot be found, and then the iteration is considered to find no feasible solution. The setting of the N value mainly considers factors such as difficulty coefficient, number of path points, number of paths, and the like, and the present embodiment adopts dynamic adjustment to find times exceeding the N value of the parameter, thereby accelerating the search for a better solution.
In an exemplary embodiment, if it is determined that the intermediate path set meets the preset constraint after the destroy point selected in the above step is inserted, the path set to be evaluated after the ith insertion operation is obtained.
In step S603, for the set of paths to be evaluated after the ith insertion operation, the distribution cost is calculated as an alternative value Xi。
In an exemplary embodiment, for the set of paths to be evaluated after the ith insertion operation, a delivery cost X is calculated0And may be used as an alternative to the standard value X of step S6010The comparison of sizes is performed.
Illustratively, responsive to said alternative value XiLess than the standard value X0Then steps S605-S607 are performed.
In step S605, the set of paths to be evaluated after the ith insertion operation is recorded.
In step S606, the alternative value X is usediUpdating the standard value X0And returning the recorded path set to be evaluated as an optimization path set until the iteration number reaches the preset iteration number M.
In step S607, i is assigned to i +1, and steps S602 to S604 are executed in a loop. That is to say, the step is executed to insert the (i + 1) th time of the destruction point into the intermediate path set, and further determine the size relationship between the distribution cost inserted into the intermediate path set at the (i + 1) th time and the updated standard value.
Illustratively, responsive to said alternative value XiNot less than the standard value X0Then step S607 is directly performed.
In the technical solution provided by the embodiment shown in fig. 6, a more optimal solution is found through a run and trade strategy on the basis of an initial solution (initial path set), that is, a path set with the lowest delivery cost is iteratively searched, that is, optimization of the initial path is realized, and an optimized path set is obtained. When the distribution vehicle uses the optimized path set to distribute orders, the distribution vehicle can not only meet the preset constraint conditions, but also reduce the distribution cost as much as possible. Thereby being beneficial to improving the user experience.
In an exemplary embodiment, when the destroy point is inserted into the intermediate path set based on a preset strategy, a gradient descent method is used to evaluate the currently inserted destroy point.
For any point of destruction to be inserted, illustratively, the insertion score is made according to the following formula,
Figure BDA0002063394130000131
wherein, f (x | x)p,w0,w1) Representing the intermediate path set score,
x denotes the point of destruction to be inserted, xpIndicating the priority of the point of destruction x to be inserted,
w0、w1respectively representing the optimal distribution cost value and the suboptimal distribution cost value of the path before the point x of destruction and satisfying w0<w1，
y、
Figure BDA0002063394130000132
Is an empirical value, wherein y takes the value of 11,
s(x,w0) For the point of destruction x and the current optimal distribution cost value w0Cost calculation subfunction of, and w0The corresponding path starting position, path ending station position, time window distance and time window parameter are related.
Further, a final insertion position is determined according to insertion scores obtained by currently inserted destroying points at different positions, that is, an optimal insertion position of each destroying point is determined according to the insertion scores, and finally, each destroying point is inserted into the intermediate path set to obtain the path set to be evaluated.
In the technical scheme provided by the embodiment shown in fig. 6, the algorithm designed by the patent solves the problem of large-scale EVRP with an actual application scene, the number of customer points related to the scene reaches at least 1200, the customer points have collecting and distribution, and the distance and time do not meet the triangle property. Furthermore, the problems can be abstracted to be the problems of charging piles, time window constraint, load capacity constraint, load constraint, maximum driving distance constraint, cable and dispatch integration constraint, vehicle number constraint and large-scale VRP with the number of nodes exceeding 1200. The technical scheme can meet the requirement of obtaining a solution with excellent quality by quickly calculating the response of the algorithm in practical application, and can meet the requirement of good robustness of the algorithm.
The following describes an embodiment of the apparatus of the present disclosure, which may be used to perform the above-mentioned order distribution route planning method of the present disclosure.
Fig. 7 schematically shows a structural diagram of an order distribution path planning apparatus according to an embodiment of the present disclosure. Referring to fig. 7, the order distribution route planning apparatus 700 includes: a state information obtaining module 701, a preliminary selection path set determining module 702, and a path optimizing module 703.
A status information obtaining module 701 for obtaining first actual status information on a node and second actual status information on a delivery vehicle, the node comprising: a distribution center site, an energy adding point and an order delivery point;
a primary selection path set determining module 702, configured to determine a primary selection path set according to the first actual state information and the second actual state information;
and the path optimization module 703 is configured to optimize, by using a destroy reconstruction algorithm, an order arrival point in at least one path in the primary selection path set, so as to plan the primary selection path set to obtain an optimized path set.
In an exemplary embodiment, based on the foregoing, the distribution vehicle is an electric vehicle, and the energy addition point is a charging station including at least one charging pile.
In an exemplary embodiment, based on the foregoing scheme, the first actual state information includes, but is not limited to: navigation information among the nodes, distance information, time information required for reaching different nodes, and time window constraint information of the nodes;
the second actual state information includes, but is not limited to: the maximum load capacity, and the range of the distribution vehicle.
In an exemplary embodiment, based on the foregoing scheme, the initial selection path set determining module 702 is specifically configured to:
determining first vector information related to the first actual state information and second vector information related to the second actual state information; and the number of the first and second groups,
and inputting the first vector information and the second vector information into a greedy algorithm, and determining an initial selection path set according to the output of the greedy algorithm.
In an exemplary embodiment, based on the foregoing solution, the path optimization module 703 includes: the system comprises a point of destruction determination unit, a removal unit and an insertion unit.
The system comprises a primary selection path set, a destroy point determining unit and a destroy point determining unit, wherein the destroy point determining unit is used for determining a preset number of destroy points in order delivery points in the primary selection path set according to a density-based clustering algorithm;
the removing unit is used for removing the destroying point from the primary path set through removing operation to obtain an intermediate path set;
and the inserting unit is used for inserting the destroying point into the intermediate path set based on a preset strategy so as to complete the planning of the initially selected path set and obtain an optimized path set.
In an exemplary embodiment, based on the foregoing scheme, the insertion unit is specifically configured to:
based on an iterative computation strategy, inserting the destruction point into the intermediate path set according to a comparison result of distribution cost change conditions of the path set before and after the ith insertion operation; wherein i is a positive number which is greater than or equal to 1 and less than or equal to the preset iteration times.
In an exemplary embodiment, based on the foregoing scheme, the insertion unit includes: the device comprises a first calculation subunit, an insertion subunit, a second calculation subunit and a first response subunit.
The first calculation subunit is configured to calculate, for the initial selection path set, a delivery cost as a standard value;
the insertion subunit is configured to insert the destroy point into the intermediate path set for the ith time to obtain a path set to be evaluated after the insertion operation for the ith time;
a first calculating subunit, configured to calculate, as an alternative value, a distribution cost for the to-be-evaluated path set after the ith insertion operation;
a first response subunit, configured to, in response to the alternative value being greater than the standard value, perform:
recording a path set to be evaluated after the ith insertion operation; and the number of the first and second groups,
and updating the standard value by using the alternative value, inserting the (i + 1) th time of the destruction point into the intermediate path set until the iteration number reaches the preset iteration number, and returning the recorded path set to be evaluated as an optimized path set.
In an exemplary embodiment, based on the foregoing solution, the order distribution route planning apparatus 700 includes: and the second response subunit is used for responding to the condition that the alternative value is less than or equal to the standard value, and inserting the (i + 1) th time of the destruction point into the intermediate path set.
In an exemplary embodiment, based on the foregoing scheme, the insertion subunit is further specifically configured to:
for any point to be inserted, performing insertion scoring according to the following formula, and inserting each point to be inserted into the intermediate path set according to the insertion scoring;
Figure BDA0002063394130000161
wherein, f (x | x)p,w0,w1) Representing the intermediate path set score,
x denotes the point of destruction to be inserted, xpIndicating the priority of the point of destruction x to be inserted,
w0、w1respectively indicate insertionThe optimal and suboptimal delivery cost values of the path before the point x of destruction satisfy w0<w1，
y、
Figure BDA0002063394130000162
Is an empirical value, wherein y takes the value of 11,
s(x,w0) For the point of destruction x and the current optimal distribution cost value w0Cost calculation subfunction of, and w0The corresponding path starting position, path ending station position, time window distance and time window parameter are related.
For details that are not disclosed in the embodiment of the apparatus of the present disclosure, please refer to the above-mentioned embodiment of the index of the present disclosure for the details that are not disclosed in the embodiment of the apparatus of the present disclosure.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the order distribution path planning method according to the embodiments of the present disclosure.
As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the disclosure described in the "exemplary methods" section above of this specification, when the program product is run on the terminal device.
Referring to fig. 8, a program product 800 for implementing the above method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a 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.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, 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.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take a variety of forms, including, but not limited to: an electromagnetic signal, an optical signal, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a 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 readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
An electronic device 900 according to this embodiment of the disclosure is described below with reference to fig. 9. The electronic device 900 shown in fig. 9 is only an example and should not bring any limitations to the functionality or scope of use of the embodiments of the present disclosure.
As shown in fig. 9, the electronic device 900 is embodied in the form of a general purpose computing device. Components of electronic device 900 may include, but are not limited to: the at least one processing unit 910, the at least one memory unit 920, and a bus 930 that couples various system components including the memory unit 920 and the processing unit 910.
Wherein the storage unit stores program code that is executable by the processing unit 910 to cause the processing unit 910 to perform steps according to various exemplary embodiments of the present disclosure described in the above section "exemplary method" of the present specification. For example, the processing unit 910 may perform the following as shown in fig. 1: step S101 of acquiring first actual state information on a node including: a distribution center site, an energy adding point and an order delivery point; step S102, determining a primary selection path set according to the first actual state information and the second actual state information; and S103, optimizing and processing order delivery points in at least one path in the primary selection path set through a destroy reconstruction algorithm to plan the primary selection path set to obtain an optimized path set.
The storage unit 920 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM)9201 and/or a cache memory unit 9202, and may further include a read only memory unit (ROM) 9203.
Storage unit 920 may also include a program/utility 9204 having a set (at least one) of program modules 9205, such program modules 9205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Bus 930 can be any of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 900 may also communicate with one or more external devices 1000 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 900, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 900 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interface 950. Also, the electronic device 900 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet) via the network adapter 960. As shown, the network adapter 960 communicates with the other modules of the electronic device 900 via the bus 930. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Furthermore, the above-described figures are merely schematic illustrations of processes included in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims (12)

1. A method for planning an order delivery path is characterized by comprising the following steps:
acquiring first actual state information on a node and second actual state information on a delivery vehicle, the node including: a distribution center site, an energy adding point and an order delivery point;
determining a primary selection path set according to the first actual state information and the second actual state information;
and optimizing and processing order delivery points in at least one path in the primary selection path set through a destroy reconstruction algorithm so as to plan the primary selection path set to obtain an optimized path set.
2. The order distribution path planning method according to claim 1,
the distribution vehicle is an electric vehicle, and the energy addition point is a charging station comprising at least one charging pile.
3. The order distribution path planning method according to claim 1,
the first actual state information includes: at least one of navigation information, distance information, time information required for reaching different nodes and time window constraint information of the nodes among the nodes;
the second actual state information includes: at least one of a maximum load capacity, and a range of the delivery vehicle.
4. The method for planning an order distribution route according to claim 1, wherein determining a set of primary routes according to the first actual status information and the second actual status information comprises:
determining first vector information related to the first actual state information and second vector information related to the second actual state information;
and inputting the first vector information and the second vector information into a greedy algorithm, and determining an initial selection path set according to the output of the greedy algorithm.
5. The method for planning an order delivery path according to any one of claims 1 to 4, wherein the step of optimizing the order delivery point in at least one path in the primary selection path set by using a destruction reconstruction algorithm to plan the primary selection path set to obtain an optimized path set comprises:
determining a preset number of destroying points in order delivery points in the primary selection path set according to a density-based clustering algorithm, and removing the destroying points from the primary selection path set through a removing operation to obtain an intermediate path set;
and inserting the destroy point into the intermediate path set based on a preset strategy so as to complete the planning of the primary selection path set and obtain an optimized path set.
6. The order distribution path planning method according to claim 5, wherein inserting the destruction point into the intermediate path set based on a preset strategy comprises:
based on an iterative computation strategy, inserting the destruction point into the intermediate path set according to a comparison result of distribution cost change conditions of the path set before and after the ith insertion operation; wherein i is a positive number which is greater than or equal to 1 and less than or equal to the preset iteration times.
7. The order distribution path planning method according to claim 6, wherein the inserting the destruction point into the intermediate path set according to a comparison result of distribution cost variation conditions of the path set before and after the ith insertion operation includes:
calculating the distribution cost as a standard value for the initial selection path set;
inserting the destroy point into the intermediate path set for the ith time to obtain a path set to be evaluated after the insertion operation for the ith time;
calculating distribution cost as an alternative value for the path set to be evaluated after the ith insertion operation;
in response to the alternative value being less than the standard value, then:
recording a path set to be evaluated after the ith insertion operation;
and updating the standard value by using the alternative value, inserting the (i + 1) th time of the destruction point into the intermediate path set until the iteration number reaches the preset iteration number, and returning the recorded path set to be evaluated as an optimized path set.
8. The method for planning an order delivery path according to claim 7, further comprising:
in response to the alternate value being greater than or equal to the normalized value, the point of destruction is inserted into the set of intermediate paths an (i + 1) th time.
9. The order distribution path planning method according to claim 5, wherein inserting the destruction point into the intermediate path set based on a preset strategy comprises:
for any point to be inserted, performing insertion scoring according to the following formula, and inserting each point to be inserted into the intermediate path set according to the insertion scoring;
Figure FDA0002063394120000021
wherein, f (x | x)p,w0,w1) Representing the set of intermediate pathsThe score is given to the user in a scoring,
x denotes the point of destruction to be inserted, xpIndicating the priority of the point of destruction x to be inserted,
w0、w1respectively representing the optimal distribution cost value and the suboptimal distribution cost value of the path before the point x of destruction and satisfying w0<w1，
Figure FDA0002063394120000031
Is an empirical value, wherein y takes the value of 11,
s(x,w0) For the point of destruction x and the current optimal distribution cost value w0Cost calculation subfunction of, and w0The corresponding path starting position, path ending station position, time window distance and time window parameter are related.
10. An order distribution route planning apparatus, comprising:
a status information acquisition module for acquiring first actual status information about a node and second actual status information about a delivery vehicle, the node comprising: a distribution center site, an energy adding point and an order delivery point;
a primary selection path set determining module, configured to determine a primary selection path set according to the first actual state information and the second actual state information;
and the path optimization module is used for optimizing and processing order delivery points in at least one path in the primary selection path set through a destroy reconstruction algorithm so as to plan the primary selection path set to obtain an optimized path set.
11. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out a method of planning an order delivery path according to any one of claims 1 to 9.
12. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method for planning an order delivery path according to any one of claims 1 to 9.
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