CN115727861A - Vehicle path planning method and device, computer equipment and storage medium - Google Patents
Vehicle path planning method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a vehicle path planning method, a vehicle path planning device, a computer device and a storage medium. The method comprises the following steps: acquiring navigation information and cost information; determining the proximity relation of each item supplier on the geographical position according to the navigation information; determining the direct-route relationship of each item supplier on the geographic position according to the navigation information; planning an initial vehicle path according to the goods supplier based on the navigation information, the cost information, the proximity relationship and the road following relationship; and optimizing the initial vehicle path to obtain a target vehicle path. By adopting the method, the vehicle planning efficiency and effect can be considered, and the effect of reducing cost and improving efficiency for enterprises is achieved.
Description
Technical Field
The present application relates to the field of supply chain technologies, and in particular, to a vehicle path planning method, apparatus, computer device, and storage medium.
Background
With the development of supply chain technology, the transport mode of the Milk Run (circular goods taking) is gradually developed, and the method is widely applied to material collection in industries such as automobile manufacturing enterprises and retail industries. The Milk Run transportation mode is a transportation mode that a transportation vehicle starts from a factory or a distribution center, sequentially receives goods from different suppliers according to a set route and time, unloads an empty container from which the goods are received last time, and finally sends all the goods to the factory or the distribution center. The Milk Run transportation mode can be well matched with a JIT (Just In Time) supply mode and a standardized production mode, the JIT supply mode pursues zero stock, the inventory cost is reduced, the standardized production mode can balance the production capacity, the equipment benefit is improved, the Milk Run transportation mode of a vehicle path is reasonably planned, and the enterprise can be helped to realize cost reduction and efficiency improvement by matching the JIT supply mode and the standardized production mode. Thus, how to implement vehicle path planning in the Milk Run transportation mode is a significant issue.
At present, the conventional vehicle path planning mode has the problems of high space-time complexity or poor cost reduction and efficiency improvement effects which can be achieved by the planned vehicle path, namely the problem that the vehicle path planning efficiency and the vehicle path planning effects cannot be considered at the same time.
Disclosure of Invention
In view of the above, it is necessary to provide a vehicle path planning method, device, computer device and storage medium that can achieve both vehicle planning efficiency and effect.
A vehicle path planning method, the method comprising:
acquiring navigation information and cost information;
determining the proximity relation of each item supplier on the geographical position according to the navigation information;
determining the direct-route relationship of each item supplier on the geographical position according to the navigation information;
planning an initial vehicle path according to the goods supplier based on the navigation information, the cost information, the proximity relationship and the road following relationship;
and optimizing the initial vehicle path to obtain a target vehicle path.
In one embodiment, the determining the proximity relationship of each item supplier in the geographic location according to the navigation information includes:
constructing an initial adjacent point set of each item supplier on the geographical position according to the navigation information;
screening the article suppliers in the initial adjacent point set according to a preset adjacent distance threshold value to obtain a target adjacent point set;
and determining the proximity relation of the corresponding item supplier on the geographical position according to the target proximity point set.
In one embodiment, the determining the road-following relationship of each item supplier in the geographic location according to the navigation information includes:
determining a first vehicle path where each item supplier is located according to the navigation information;
merging the first vehicle paths of any two article suppliers;
and when the reduced transportation distance after combination meets the preset road passing condition, judging that the corresponding two article suppliers have a road passing relation on the geographical position.
In one embodiment, said planning an initial vehicle path according to said item provider based on said navigation information, said cost information, said proximity relationship, and said following relationship comprises:
determining a corresponding homologable path set of each item supplier based on the proximity relation and the road following relation;
and planning an initial vehicle path according to the item supplier, the navigation information, the cost information and the same path set.
In one embodiment, the optimizing the initial vehicle path to obtain the target vehicle path includes:
constructing various neighborhood structures according to the same path set, the navigation information and the cost information;
and optimizing the initial vehicle path according to the neighborhood structure to obtain a target vehicle path.
In one embodiment, the optimizing the initial vehicle path according to the neighborhood structure to obtain a target vehicle path includes:
optimizing the initial vehicle path according to the neighborhood structure to obtain a candidate vehicle path;
a traveler problem is determined for each candidate vehicle path, and a target vehicle path is obtained by optimizing the corresponding candidate vehicle path by solving the traveler problem.
In one embodiment, the acquiring navigation information includes:
acquiring article supplier information, logistics supplier information and target transportation point information;
and obtaining navigation information according to the article supplier information, the logistics supplier information and the target transportation point information.
A vehicle path planning apparatus, the apparatus comprising:
the acquisition module is used for acquiring navigation information and cost information;
the proximity relation determining module is used for determining the proximity relation of each item supplier on the geographical position according to the navigation information;
the forward-route relation determining module is used for determining the forward-route relation of each article supplier on the geographical position according to the navigation information;
a path planning module for planning an initial vehicle path according to the goods supplier based on the navigation information, the cost information, the proximity relation and the road following relation;
and the path optimization module is used for optimizing the initial vehicle path to obtain a target vehicle path.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring navigation information and cost information;
determining the proximity relation of each item supplier on the geographical position according to the navigation information;
determining the direct-route relationship of each item supplier on the geographical position according to the navigation information;
planning an initial vehicle path according to the goods supplier based on the navigation information, the cost information, the proximity relationship and the road following relationship;
and optimizing the initial vehicle path to obtain a target vehicle path.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring navigation information and cost information;
determining the proximity relation of each item supplier on the geographical position according to the navigation information;
determining the direct-route relationship of each item supplier on the geographical position according to the navigation information;
planning an initial vehicle path according to the goods supplier based on the navigation information, the cost information, the proximity relationship and the road following relationship;
and optimizing the initial vehicle path to obtain a target vehicle path.
According to the vehicle path planning method, the vehicle path planning device, the computer equipment and the storage medium, after navigation information and cost information for vehicle path planning are obtained, the adjacent relation and the forward-route relation of each article supplier in the geographic position are respectively determined according to the navigation information, the cost information and the adjacent relation and the forward-route relation of each article supplier are used as constraints, an initial vehicle path is quickly planned according to each article supplier, and the initial vehicle path is optimized to obtain an approximately optimal target vehicle path. Therefore, the adjacent relation and the on-road relation of the goods suppliers on the geographical position can be determined quickly and accurately based on the navigation information, the cost information, the adjacent relation and the on-road relation are used as constraints, a better initial vehicle path can be planned quickly, and a more effective target vehicle planned path can be obtained by optimizing the initial vehicle path, so that the approximately optimal target vehicle path can be obtained under the condition of ensuring the vehicle planning efficiency, and the aim of reducing cost and improving efficiency of enterprises is fulfilled.
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FIG. 1 is a schematic flow chart diagram of a vehicle path planning method in one embodiment;
FIG. 2 is a schematic diagram of a vehicle path planning method in one embodiment;
FIG. 3 is a schematic flow chart diagram of a vehicle path planning method in another embodiment;
FIG. 4 is a block diagram of a vehicle path planning apparatus according to an embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In an embodiment, as shown in fig. 1, a vehicle path planning method is provided, and this embodiment is illustrated by applying the method to a terminal, and it is to be understood that the method may also be applied to a server, and may also be applied to a system including the terminal and the server, and is implemented by interaction between the terminal and the server. In this embodiment, the method includes the steps of:
step 102, navigation information and cost information are obtained.
The navigation information is a reference basis for vehicle path planning, can be used for determining the adjacent relation and the road following relation between the goods suppliers on the geographical position, and can also be used as one of the constraint conditions for vehicle path planning. The navigation information may specifically include, but is not limited to, the geographic distance between any two article providers, the travel time between different types of transportation vehicles, the geographic distance between each article provider and the logistics provider/target transportation point, the travel time between each article provider and the logistics provider/target transportation point, the supply information, the geographic location, the vehicle type requirement, and other article provider information of each article provider, the available vehicle type, the geographic location, and other logistics provider information of each logistics provider, and the geographic location, the unloading service time window, and the unloading efficiency, and other target transportation point information of the target transportation point. The cost information refers to reference information for calculating transportation cost, and can also be understood as pricing information or pricing means, including but not limited to mileage, vehicle depreciation, driver wage, tax rate, and the like. The target transportation point refers to a transportation point where the item/good needs to be acquired or eventually received, and includes, but is not limited to, factories and distribution centers.
Specifically, the terminal obtains a vehicle path planning request, determines an article supplier, a logistics supplier and a target transportation point to be subjected to path planning according to the vehicle path planning request, respectively obtains corresponding article supplier information, logistics supplier information and target transportation point information from the article supplier, the logistics supplier and the target transportation point, and determines navigation information and cost information according to the article supplier information, the logistics supplier information and the target transportation point information.
In one embodiment, obtaining navigation information includes: acquiring article supplier information, logistics supplier information and target transportation point information; and obtaining navigation information according to the information of the goods supplier, the information of the logistics supplier and the information of the target transportation point.
The goods provider information includes, but is not limited to, supply information, geographic location, vehicle type requirements, and other information. The logistics provider information includes but is not limited to available vehicle types, geographic locations and pricing means. The target transportation point information includes, but is not limited to, geographical location, unloading service time window, unloading efficiency, and the like.
Specifically, after acquiring the article provider information, the logistics provider information and the target transportation point information, the terminal may determine corresponding navigation information according to a preset navigation information determination mode according to the article provider information, the logistics provider information and the target transportation point information, may also send the article provider information, the logistics provider information and the target transportation point information to the navigation device, and receives navigation information determined and fed back by the navigation device based on the article provider information, the logistics provider information and the target transportation point information. The navigation device refers to a terminal or a server providing navigation services.
And step 104, determining the proximity relation of each item supplier on the geographical position according to the navigation information.
Wherein the proximity relation is used to characterize whether two item suppliers are geographically close. If the two item suppliers have a proximity relationship, the two item suppliers are characterized to be adjacent to each other in geographic position. It will be appreciated that a set of all item providers participating in vehicle path planning may be determined as an item provider set.
Specifically, the terminal determines the distance between any two item suppliers in the item supplier set in the geographic position according to the navigation information, and for each item supplier, selects the item supplier adjacent to the item supplier from the item supplier set according to the corresponding distance, and establishes the proximity relationship between the item supplier and the selected item supplier. Thereby, the proximity relation of each item provider in the geographical position can be determined based on the navigation information.
In one embodiment, the terminal clusters the item providers in the item provider set according to the navigation information, and determines the proximity relation of each item provider in the geographic position according to the clustering result. It can be understood that the terminal may perform Clustering on each item provider by using an existing Clustering manner, such as hierarchical Clustering, DBSCAN (Density-Based Clustering of Applications with Noise, a Density-Based Clustering algorithm), and KNN (K-nearest neighbor algorithm), which are not described herein again.
In one embodiment, the clustering result is generally that all the article suppliers are divided into a plurality of clusters, and article suppliers in the same cluster are adjacent to each other, so that an adjacent relationship is established for any two article suppliers in each cluster.
And step 106, determining the road relation of each item supplier on the geographical position according to the navigation information.
Wherein the direct-route relationship is used for representing whether two article suppliers are direct-route in geographic positions. If there is a direct relationship between two item suppliers, it is indicated that the two item suppliers are geographically direct, i.e. that the two item suppliers may be located on one vehicle path.
Specifically, the terminal determines the distance between any two article suppliers on the geographical position according to the navigation information, the distance between each article supplier and the logistics supplier on the geographical position, and the distance between each article supplier and the target transportation point on the geographical position, determines other article suppliers which are on the way with each article supplier on the geographical position from the article supplier set according to the determined distances, and establishes the way relationship between the two article suppliers on the way on the geographical position.
And step 108, planning an initial vehicle path according to the goods supplier based on the navigation information, the cost information, the proximity relation and the road following relation.
Specifically, the terminal takes the navigation information and the cost information, and the proximity relation and the road following relation of each article supplier on the geographical position determined based on the navigation information as constraints, and plans the vehicle path according to the article suppliers in the article supplier set to obtain an initial vehicle path.
And step 110, optimizing the initial vehicle path to obtain a target vehicle path.
Specifically, after an initial vehicle path serving as an initial solution of vehicle path planning is obtained through planning, the terminal optimizes the initial vehicle path to obtain a target vehicle path based on navigation information and cost information, and the adjacent relation and the forward-route relation of each article supplier in the geographic position.
According to the vehicle path planning method, after navigation information and cost information for vehicle path planning are obtained, the adjacent relation and the forward-route relation of each article supplier on the geographic position are respectively determined according to the navigation information, the cost information and the adjacent relation and the forward-route relation among the article suppliers are used as constraints, an initial vehicle path is rapidly planned according to each article supplier, and the initial vehicle path is optimized to obtain an approximately optimal target vehicle path. Therefore, the adjacent relation and the following relation of the goods suppliers on the geographical position can be determined quickly and accurately based on the navigation information, the cost information, the adjacent relation and the following relation are taken as constraints, a better initial vehicle path can be planned quickly, and a more effective target vehicle planned path can be obtained by optimizing the initial vehicle path, so that the approximately optimal target vehicle path can be obtained under the condition of ensuring the vehicle planning efficiency, and the aim of reducing the cost and improving the efficiency of enterprises is fulfilled.
In one embodiment, step 104 comprises: constructing an initial adjacent point set of each item supplier on the geographical position according to the navigation information; screening the article suppliers in the initial adjacent point set according to a preset adjacent distance threshold value to obtain a target adjacent point set; and determining the proximity relation of the corresponding item supplier on the geographical position according to the target proximity point set.
Specifically, the terminal selects K item suppliers closest to each item supplier according to the navigation information by adopting a KNN algorithm based on the set K value, and the K item suppliers serve as the item suppliers adjacent to the item suppliers in the geographical position to obtain an initial adjacent point set corresponding to the item suppliers. And for each item provider, the terminal eliminates the item providers with the distance from the geographical position of the item provider to the geographical position larger than a preset proximity distance threshold value from the corresponding initial proximity point set to obtain a target proximity point set, and each item provider in the target proximity point set is adjacent to the item provider in the geographical position, so that the proximity relation between the item provider and each item provider in the corresponding target proximity point set is established. It is understood that the value of K can be customized according to requirements.
In the above embodiment, the initial neighboring point set of each item provider is determined based on the navigation information, the item providers farther away from the corresponding item provider in the initial neighboring point set are screened out based on the preset neighboring distance threshold to obtain the target neighboring point set, and each item provider in the target neighboring point set is determined as a neighboring point of the corresponding item provider, so as to ensure that the two item providers establishing the neighboring relationship are not too far away from each other in the geographic location.
In one embodiment, step 106, comprises: determining a first vehicle path of each item supplier according to the navigation information, and merging the first vehicle paths of any two item suppliers; and when the reduced transportation distance after combination meets the preset road-following condition, judging that the corresponding two article suppliers have a road-following relationship on the geographical position.
The first vehicle path where the item supplier is located may be a vehicle path constructed based on the item supplier, the corresponding logistics supplier and the target transportation point, for example, if the logistics supplier a and the target transportation point B exist, the first vehicle path constructed for the item supplier X1 is a-X1-B, or may be a vehicle path constructed based on the item supplier and the corresponding target transportation point, for example, if the target transportation point B exists, the first vehicle path constructed for the item supplier X1 is B-X1-B, which may be specifically determined by a preconfigured first vehicle path construction manner. The preset forward condition is a condition or basis for determining whether the two goods suppliers are forward in geographic locations, and specifically may be that a quotient of the reduced transportation distance after merging and the original total transportation distance is greater than a preset percentage threshold, and the percentage threshold may be self-defined according to a requirement, for example, 0.5.
Specifically, the terminal constructs a first vehicle path where each item supplier is located according to the navigation information, and determines a first transportation distance corresponding to each first vehicle path according to the navigation information. Further, the terminal traverses the article suppliers in the article supplier set, merges the first vehicle path where the currently traversed article supplier is located with the first vehicle path where each other article supplier in the article supplier set is located respectively to obtain corresponding second vehicle paths, determines a second transportation distance corresponding to each second vehicle path according to the navigation information, sums the first transportation distances corresponding to the first vehicle paths where the corresponding two article suppliers are located respectively aiming at each second vehicle path to obtain an original total transportation distance, subtracts the second transportation path corresponding to the second vehicle path from the original total transportation distance to obtain a reduced transportation distance after the first vehicle paths are merged, compares the transportation distance with a preset in-line condition, and determines that the in-line relation exists between the corresponding two article suppliers on the geographic position when the transportation distance meets the preset in-line condition. It can be understood that when the quotient of the reduced transportation distance after merging and the corresponding original total transportation distance is greater than the preset percentage threshold, the terminal determines that the transportation distance meets the preset road condition, and further determines that the corresponding two article suppliers have a road relationship.
For example, assuming that the first vehicle route constructed for the item supplier X1 is B-X1-B and the first vehicle route constructed for the item supplier X2 is B-X2-B, the first vehicle route in which the item supplier X1 and the item supplier X2 are respectively located is merged, and the obtained second vehicle route is B-X1-X2-B or B-X2-X1-B, and the first transportation distance corresponding to the first vehicle route B-X1-B can be obtained according to the distance between the target transportation point B and the item supplier X1 in the navigation information, and similarly, the first transportation distance corresponding to the first vehicle route B-X2-B can be obtained and the second transportation distance corresponding to the second vehicle route B-X1-X2-B (or B-X2-X1-B) can be obtained, and thus, based on the obtained first transportation distance and second transportation distance, whether the item relation between the item supplier X1 and the item supplier X2 exists in the manner described above can be determined.
In the above embodiment, by merging the first vehicle paths where any two independent article suppliers are respectively located, if the reduced transportation distance after merging is large enough, it is determined that the two article suppliers have a direct-road relationship.
In one embodiment, step 108 includes: determining a homonymous path set corresponding to each article supplier based on the proximity relation and the forward-route relation; and planning an initial vehicle path according to the item supplier, the navigation information, the cost information and the same path set.
The set of the same paths corresponding to the item suppliers is a set composed of the item suppliers in the item supplier set, which can be in the same vehicle path with the item supplier, and specifically may be a set composed of the item suppliers having a proximity relationship and/or a direct-route relationship with the item suppliers.
Specifically, for each article provider in the article provider set, the terminal screens the article providers adjacent to the article provider from the article provider set according to the adjacent relationship corresponding to the article provider, screens the article providers along the way with the article provider from the article provider set according to the along-way relationship corresponding to the article provider, and obtains the same path set corresponding to the article provider according to the screened article provider. Further, the terminal plans the initial vehicle path according to the item suppliers in the item supplier set by taking the same path set corresponding to each item supplier, the navigation information and the cost information as constraint conditions. It can be understood that, in the vehicle path planning process, for each item provider, if any item provider does not exist in a planned vehicle path and is in the set of the paths that can be shared by the item provider, it is determined that the item provider cannot be added to the vehicle path.
In an embodiment, the terminal constructs an initial vehicle path planning solution by using an existing sweep scanning algorithm (a heuristic construction algorithm) based on the constraint conditions to obtain an initial vehicle path, which is not described herein again.
In the above embodiment, the same path set determined based on the proximity relationship and the forward path relationship, and the navigation information and the cost information are used as constraint conditions, so that an initial vehicle path with a better effect can be quickly planned.
In one embodiment, step 110, comprises: constructing various neighborhood structures according to the same path set, the navigation information and the cost information; and optimizing the initial vehicle path according to the neighborhood structure to obtain a target vehicle path.
The neighborhood structure refers to a set of all solutions that can be obtained by performing an operation (the operation is called a neighborhood action) on the current solution.
Specifically, the terminal takes navigation information, cost information and a set of similar paths corresponding to each article supplier as constraint conditions, adopts a VNS algorithm (Variable neighbor Search algorithm) to construct various Neighborhood structures based on the constraint conditions, and optimizes an initial vehicle path based on the constructed Neighborhood structures to obtain a target vehicle path. It should be noted that the terminal adopts the existing VNS algorithm to construct the neighborhood structure to continuously optimize the initial vehicle path, which is not described herein again.
In the above embodiment, the set of the same path is used as one of the constraint conditions, and the size of the optional solution set of the neighborhood structure which is optimized in a larger range can be limited, so that the space-time complexity can be reduced. Moreover, the set of the homonymous paths is determined based on the proximity relation and the direct-route relation among the goods suppliers, and meets the common requirements of enterprises in path planning. In addition, the vehicle path is further optimized on the basis of the initial vehicle path, and a target vehicle path which is approximate to an optimal solution can be optimized in a short time.
In one embodiment, optimizing the initial vehicle path according to the neighborhood structure to obtain the target vehicle path includes: optimizing the initial vehicle path according to the neighborhood structure to obtain a candidate vehicle path; a traveler problem is determined for each candidate vehicle path, and a target vehicle path is obtained by solving the traveler problem and optimizing the corresponding candidate vehicle path.
Among them, the traveler Problem is a TSP (tracking Salesman publishing) Problem, and the optimization of each initial vehicle path is regarded as an independent TSP Problem, and thus, the process of solving the TSP Problem is a process of optimizing the initial vehicle path.
Specifically, the terminal further optimizes the initial vehicle path to obtain corresponding candidate vehicle paths according to the constructed multiple neighborhood structures by adopting a VNS algorithm, and optimizes each candidate vehicle path as an independent traveler problem so as to optimize the corresponding candidate vehicle paths to obtain the final target vehicle path by solving each traveler problem.
In one embodiment, the terminal takes each candidate vehicle path as an independent traveler problem, and solves the traveler problems in parallel by adopting a concurrent execution mode to obtain respective corresponding target vehicle paths.
In one embodiment, the terminal uses an existing traveler problem solving algorithm to solve the traveler problem corresponding to each candidate vehicle path, including but not limited to a simulated annealing algorithm and a tabu search algorithm, which are not described herein again.
In the above embodiment, after the initial vehicle path is optimized to obtain the candidate vehicle paths, the further optimization of each candidate vehicle path is taken as an independent traveler problem, so that the candidate vehicle paths are further optimized to obtain the target vehicle path by solving the traveler problem, and thus, the target vehicle path with better effect can be obtained by optimizing in a shorter time, so that the total mileage of the transportation vehicle driving according to the target vehicle path is shorter, the cost is lower, and the purpose of cost reduction and efficiency improvement for enterprises is achieved.
FIG. 2 is a schematic diagram of a vehicle path planning method in one embodiment. As shown in fig. 2, after a vehicle path planning process is triggered to start planning a vehicle path, a terminal constructs an adjacent relationship between article suppliers through a KNN algorithm, constructs a direct-route relationship between the article suppliers by using a C-W saving algorithm (a construction heuristic algorithm), constructs a vehicle planning path initial solution by using a sweep scanning algorithm based on the adjacent relationship and the direct-route relationship between the article suppliers, continuously optimizes a current solution on the basis of the vehicle planning path initial solution by using a VNS algorithm based on the adjacent relationship and the direct-route relationship between the article suppliers to obtain candidate vehicle paths, further optimizes by using a simulated annealing algorithm to obtain a target vehicle path for a TSP problem formed by each candidate vehicle path, and ends the vehicle path planning process.
In the embodiment, the clustering algorithm, the constructed heuristic algorithm, the VNS algorithm and the TSP problem are comprehensively used for solving, so that the approximate optimal solution of the vehicle path can be planned in a short time, and the aim of reducing cost and improving efficiency of enterprises is fulfilled.
Fig. 3 is a flow chart illustrating a vehicle path planning method according to an embodiment. As shown in fig. 3, the method specifically includes the following steps:
step 302, navigation information and cost information are obtained.
And step 304, constructing an initial adjacent point set of each item supplier on the geographical position according to the navigation information.
And step 306, screening the article suppliers in the initial adjacent point set according to a preset adjacent distance threshold value to obtain a target adjacent point set.
And step 308, determining the proximity relation of the corresponding item supplier on the geographical position according to the target proximity point set.
At step 310, a first vehicle route is determined for each item supplier based on the navigation information.
At step 312, the first vehicle paths of any two item suppliers are merged.
And step 314, when the reduced transportation distance after combination meets the preset road passing condition, judging that the corresponding two article suppliers have a road passing relation on the geographical position.
Step 316, determining a corresponding set of shareable paths for each item supplier based on the proximity relationship and the forward-route relationship.
And step 318, planning an initial vehicle path according to the item supplier, the navigation information, the cost information and the set of the same paths.
Step 320, constructing various neighborhood structures according to the same path set, the navigation information and the cost information.
And 322, optimizing the initial vehicle path according to the neighborhood structure to obtain a candidate vehicle path.
Step 324, a traveler problem is determined for each candidate vehicle path, and the target vehicle path is obtained by solving the traveler problem and optimizing the corresponding candidate vehicle path.
In the above embodiment, the neighborhood relationship and the forward path relationship are determined based on the navigation information, the initial vehicle path is obtained by planning with the neighborhood relationship, the forward path relationship, the navigation information and the cost information as constraint conditions, and the initial vehicle path is subjected to multiple optimization with the neighborhood relationship, the forward path relationship, the navigation information and the cost information as constraint conditions, so as to obtain the target vehicle path with an approximately optimal solution. Therefore, the vehicle path planning result with excellent effect can be planned in a short time, the loading rate of the transport vehicle is improved, the number of used vehicles and the total mileage of the vehicles are reduced, and the effect of reducing cost and improving efficiency for enterprises is achieved. Even if the path planning problem of various constraint parameters such as a time window, a vehicle type requirement, loading limitation and the like exists, the target vehicle path with better effect can still be rapidly planned according to the vehicle path planning method based on the navigation information comprising the constraint parameters.
It should be understood that although the steps in the flowcharts of fig. 1 and 3 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a portion of the steps in fig. 1 and 3 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternatively with other steps or at least a portion of the other steps or stages.
In one embodiment, as shown in fig. 4, there is provided a vehicle path planning apparatus 400 comprising: an obtaining module 401, an adjacent relation determining module 402, a forward-route relation determining module 403, a path planning module 404 and a path optimizing module 405, wherein:
an obtaining module 401, configured to obtain navigation information and cost information;
a proximity relation determining module 402, configured to determine a proximity relation of each item provider in a geographic location according to the navigation information;
a forward-road relationship determining module 403, configured to determine a forward-road relationship of each item provider in the geographic location according to the navigation information;
a path planning module 404 configured to plan an initial vehicle path according to the item provider based on the navigation information, the cost information, the proximity relationship, and the on-road relationship;
and a path optimization module 405, configured to optimize the initial vehicle path to obtain a target vehicle path.
In one embodiment, the proximity relation determining module 402 is further configured to construct an initial set of proximity points of each item supplier in the geographic location according to the navigation information; screening the article suppliers in the initial adjacent point set according to a preset adjacent distance threshold value to obtain a target adjacent point set; and determining the proximity relation of the corresponding item supplier on the geographical position according to the target proximity point set.
In one embodiment, the forward-road relationship determining module 403 is further configured to determine a first vehicle path where each item supplier is located according to the navigation information; merging the first vehicle paths of any two article suppliers; and when the reduced transportation distance after combination meets the preset road-following condition, judging that the corresponding two article suppliers have a road-following relationship on the geographical position.
In one embodiment, the path planning module 404 is further configured to determine a set of shareable paths corresponding to each item supplier based on the proximity relationship and the direct-route relationship; and planning an initial vehicle path according to the item supplier, the navigation information, the cost information and the same path set.
In one embodiment, the path optimization module 405 is further configured to construct a plurality of neighborhood structures according to the set of the same path, the navigation information, and the cost information; and optimizing the initial vehicle path according to the neighborhood structure to obtain a target vehicle path.
In one embodiment, the route optimization module 405 is further configured to optimize the initial vehicle route according to the neighborhood structure to obtain a candidate vehicle route; and determining a traveler problem aiming at each candidate vehicle path, and optimizing the corresponding candidate vehicle path by solving the traveler problem to obtain the target vehicle path.
In one embodiment, the obtaining module 401 is further configured to obtain information of an item supplier, information of a logistics supplier, and information of a target transportation point; and obtaining navigation information according to the information of the goods supplier, the information of the logistics supplier and the information of the target transportation point.
For specific definition of the vehicle path planning device, reference may be made to the above definition of the vehicle path planning method, which is not described herein again. The modules in the vehicle path planning device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 5. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a vehicle path planning method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the configuration shown in fig. 5 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory having a computer program stored therein and a processor that when executing the computer program performs the steps of: acquiring navigation information and cost information; determining the proximity relation of each article supplier on the geographical position according to the navigation information; determining the direct-route relationship of each article supplier on the geographical position according to the navigation information; planning an initial vehicle path according to an article supplier based on the navigation information, the cost information, the proximity relation and the road following relation; and optimizing the initial vehicle path to obtain a target vehicle path.
In one embodiment, the processor when executing the computer program further performs the steps of: constructing an initial adjacent point set of each item supplier on the geographic position according to the navigation information; screening the article suppliers in the initial adjacent point set according to a preset adjacent distance threshold value to obtain a target adjacent point set; and determining the proximity relation of the corresponding item supplier on the geographical position according to the target proximity point set.
In one embodiment, the processor, when executing the computer program, further performs the steps of: determining a first vehicle path where each item supplier is located according to the navigation information; merging the first vehicle paths of any two article suppliers; and when the reduced transportation distance after combination meets the preset road passing condition, judging that the corresponding two article suppliers have a road passing relation on the geographical position.
In one embodiment, the processor when executing the computer program further performs the steps of: determining a homologable path set corresponding to each article supplier based on the proximity relation and the direct-route relation; and planning an initial vehicle path according to the item supplier, the navigation information, the cost information and the same path set.
In one embodiment, the processor, when executing the computer program, further performs the steps of: constructing various neighborhood structures according to the same path set, the navigation information and the cost information; and optimizing the initial vehicle path according to the neighborhood structure to obtain a target vehicle path.
In one embodiment, the processor when executing the computer program further performs the steps of: optimizing the initial vehicle path according to the neighborhood structure to obtain a candidate vehicle path; and determining a traveler problem aiming at each candidate vehicle path, and optimizing the corresponding candidate vehicle path by solving the traveler problem to obtain the target vehicle path.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring article supplier information, logistics supplier information and target transportation point information; and obtaining navigation information according to the information of the goods supplier, the information of the logistics supplier and the information of the target transportation point.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring navigation information and cost information; determining the proximity relation of each item supplier on the geographical position according to the navigation information; determining the direct-route relationship of each article supplier on the geographical position according to the navigation information; planning an initial vehicle path according to an article supplier based on the navigation information, the cost information, the proximity relation and the road following relation; and optimizing the initial vehicle path to obtain a target vehicle path.
In one embodiment, the computer program when executed by the processor further performs the steps of: constructing an initial adjacent point set of each item supplier on the geographic position according to the navigation information; screening the article suppliers in the initial adjacent point set according to a preset adjacent distance threshold value to obtain a target adjacent point set; and determining the proximity relation of the corresponding item supplier on the geographical position according to the target proximity point set.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a first vehicle path where each item supplier is located according to the navigation information; merging the first vehicle paths of any two article suppliers; and when the reduced transportation distance after combination meets the preset road-following condition, judging that the corresponding two article suppliers have a road-following relationship on the geographical position.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a homologable path set corresponding to each article supplier based on the proximity relation and the direct-route relation; and planning an initial vehicle path according to the item supplier, the navigation information, the cost information and the same path set.
In one embodiment, the computer program when executed by the processor further performs the steps of: constructing various neighborhood structures according to the same path set, the navigation information and the cost information; and optimizing the initial vehicle path according to the neighborhood structure to obtain a target vehicle path.
In one embodiment, the computer program when executed by the processor further performs the steps of: optimizing the initial vehicle path according to the neighborhood structure to obtain a candidate vehicle path; and determining a traveler problem aiming at each candidate vehicle path, and optimizing the corresponding candidate vehicle path by solving the traveler problem to obtain the target vehicle path.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring article supplier information, logistics supplier information and target transportation point information; and obtaining navigation information according to the information of the article supplier, the information of the logistics supplier and the information of the target transportation point.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is specific and detailed, but not to be understood as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A vehicle path planning method, the method comprising:
acquiring navigation information and cost information;
determining the proximity relation of each item supplier on the geographical position according to the navigation information;
determining the direct-route relationship of each item supplier on the geographical position according to the navigation information;
planning an initial vehicle path according to the item supplier based on the navigation information, the cost information, the proximity relationship and the on-road relationship;
and optimizing the initial vehicle path to obtain a target vehicle path.
2. The method of claim 1, wherein determining the proximity of each item provider to the geographic location based on the navigation information comprises:
constructing an initial adjacent point set of each item supplier on the geographic position according to the navigation information;
screening the article suppliers in the initial adjacent point set according to a preset adjacent distance threshold value to obtain a target adjacent point set;
and determining the proximity relation of the corresponding item supplier on the geographical position according to the target proximity point set.
3. The method of claim 1, wherein said determining a routing relationship for each of said item providers in geographic location based on said navigational information comprises:
determining a first vehicle path where each item supplier is located according to the navigation information;
merging the first vehicle paths of any two article suppliers;
and when the reduced transportation distance after combination meets the preset road-following condition, judging that the corresponding two article suppliers have a road-following relationship on the geographical position.
4. The method of claim 1, wherein planning an initial vehicle path from the item provider based on the navigation information, the cost information, the proximity relationship, and the on-road relationship comprises:
determining a homonymous path set corresponding to each item supplier based on the proximity relation and the forward-route relation;
and planning an initial vehicle path according to the item supplier, the navigation information, the cost information and the same path set.
5. The method of claim 4, wherein said optimizing said initial vehicle path results in a target vehicle path, comprising:
constructing various neighborhood structures according to the same path set, the navigation information and the cost information;
and optimizing the initial vehicle path according to the neighborhood structure to obtain a target vehicle path.
6. The method of claim 5, wherein optimizing the initial vehicle path according to the neighborhood structure to obtain a target vehicle path comprises:
optimizing the initial vehicle path according to the neighborhood structure to obtain a candidate vehicle path;
a traveler problem is determined for each candidate vehicle path, and a target vehicle path is obtained by optimizing the corresponding candidate vehicle path by solving the traveler problem.
7. The method according to any one of claims 1 to 6, wherein the acquiring navigation information comprises:
acquiring article supplier information, logistics supplier information and target transportation point information;
and obtaining navigation information according to the article supplier information, the logistics supplier information and the target transportation point information.
8. A vehicle path planning apparatus, the apparatus comprising:
the acquisition module is used for acquiring navigation information and cost information;
the proximity relation determining module is used for determining the proximity relation of each article supplier on the geographical position according to the navigation information;
the forward-route relation determining module is used for determining the forward-route relation of each article supplier on the geographical position according to the navigation information;
a path planning module for planning an initial vehicle path according to the goods supplier based on the navigation information, the cost information, the proximity relation and the road following relation;
and the path optimization module is used for optimizing the initial vehicle path to obtain a target vehicle path.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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