Vehicle scheduling method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a vehicle scheduling method and device, electronic equipment and a storage medium, and relates to the field of logistics transportation, and the method comprises the steps: determining a target transportation task which comprises a target freight volume between a logistics sorting center and a plurality of stations; according to the target transportation task and a path generation constraint condition, generating a transportation path between the logistics sorting center and the plurality of stations; according to the transportation path and the target freight volume, respectively generating transportation cost data for different types of schedulable vehicles; and according to the transportation cost data and the vehicle scheduling constraint condition, generating a scheduling scheme for the schedulable vehicle based on the transportation path. By adopting the technical scheme provided by the embodiment of the invention, the effect of efficiently determining the vehicle scheduling method with good service and high cost under the scene that the transportation demand is unbalanced and the vehicle resource composition is complex can be achieved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of logistics transportation, and in particular to a vehicle scheduling method and device, an electronic device, and a storage medium. BACKGROUND

[0002] In the daily goods transportation scene of logistics, vehicle scheduling generally plans goods transfer and distribution tasks of terminal sites from a sorting center, and also plans pickup or return tasks when returning from the terminal sites. In the prior art, vehicle path planning and vehicle resource scheduling are considered separately. From the transportation side, although vehicle path planning can solve the problems of optimal path and minimum travel, it lacks consideration of scheduling and scheduling of transport resources. The scheduling of vehicle resources considers the cost problem, but often lacks consideration of the executability of self-operated vehicles or reasonable scheduling of three-party vehicles.

[0003] However, in the process of implementing the present application, it is found that at least the following problems exist in the prior art:

[0004] The existing vehicle scheduling method does not comprehensively consider the cost factor, maximum utilization of vehicle transport resources, and path executability, resulting in poor feasibility of the terminal site vehicle scheduling scheme. SUMMARY

[0005] Embodiments of the present application provide a vehicle scheduling method, device, electronic device, and storage medium to efficiently determine a vehicle scheduling method with good service and low cost in a scene where transportation demand is unbalanced and vehicle resources are complex.

[0006] In a first aspect, embodiments of the present application provide a vehicle scheduling method, which includes:

[0007] determining a target transportation task, the target transportation task including a target freight volume between a logistics sorting center and a plurality of sites;

[0008] generating a transportation path between the logistics sorting center and the plurality of sites according to the target transportation task and path generation constraints;

[0009] generating transportation cost data for different types of schedulable vehicles according to the transportation path and the target freight volume;

[0010] generating a scheduling scheme for the schedulable vehicles based on the transportation path according to the transportation cost data and vehicle scheduling constraints.

[0011] In a second aspect, embodiments of the present application also provide a vehicle scheduling device, which includes:

[0012] The transport task determination module is configured to determine a target transport task, wherein the target transport task comprises target freight volumes between the logistics sorting center and the plurality of stations;

[0013] The transport path generation module is configured to generate transport paths between the logistics sorting center and the plurality of stations according to the target transport task and path generation constraints;

[0014] The transport cost generation module is configured to generate transport cost data for different types of schedulable vehicles respectively according to the transport paths and the target freight volumes;

[0015] The scheduling scheme generation module is configured to generate a scheduling scheme for the schedulable vehicles based on the transport paths according to the transport cost data and vehicle scheduling constraints.

[0016] In a third aspect, an electronic device is provided, and the electronic device comprises:

[0017] One or more processors;

[0018] A memory configured to store one or more programs;

[0019] When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the vehicle scheduling method provided by any of the embodiments of the present application.

[0020] In a fourth aspect, a computer-readable storage medium is provided, and the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the vehicle scheduling method provided by any of the embodiments of the present application.

[0021] The embodiments of the above application have the following advantages or beneficial effects:

[0022] The technical solution of the present application determines a target transport task, wherein the target transport task comprises target freight volumes between a logistics sorting center and a plurality of stations; generates transport paths between the logistics sorting center and the plurality of stations according to the target transport task and path generation constraints; generates transport cost data for different types of schedulable vehicles respectively according to the transport paths and the target freight volumes; and generates a scheduling scheme for the schedulable vehicles based on the transport paths according to the transport cost data and vehicle scheduling constraints. Because the technical means of simultaneously considering the path planning feasibility and the best vehicle resource allocation effect two coupled constraints is adopted, the technical problems that the prior art does not comprehensively consider the cost factors, the maximum utilization of vehicle capacity resources, the path executability, and the like, and the vehicle scheduling result is poor in the scenario where the transport demand is unbalanced and the vehicle resource composition is complex, are overcome, and the technical effect of efficiently determining a vehicle scheduling method with good service cost is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1a A flow chart of a vehicle scheduling method provided by an embodiment of the present application;

[0024] Figure 1b A complete flow chart of a vehicle scheduling method provided by the first embodiment of the present application;

[0025] Figure 2a A flow chart of another vehicle scheduling method provided by an embodiment of the present application;

[0026] Figure 2b A transport path generation flow chart provided by the second embodiment of the present application;

[0027] Figure 3 A structural schematic diagram of a vehicle scheduling device provided by an embodiment of the present application;

[0028] Figure 4 A structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0029] The present application will be further described below in conjunction with the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application. In addition, it should be noted that, for the convenience of description, only the parts related to the present application are shown in the drawings, but not all the structures.

[0030] Figure 1a A flow chart of a vehicle scheduling method provided by an embodiment of the present application, which can be applicable to the case of vehicle scheduling in the transport of logistics goods. The method can be performed by a vehicle scheduling device integrated in a logistics management system, which can be realized by software and / or hardware. As shown in the figure, the method specifically includes the following steps: Figure 1a

[0031] S110, determining a target transport task, the target transport task including a target freight volume between a logistics sorting center and a plurality of stations.

[0032] Among them, the target transport task can refer to the transport task between the logistics sorting center and the plurality of stations.

[0033] In this embodiment, the target transport task can be a transport task received by the logistics sorting center in an actual logistics scenario; the target transport task can also be a stable transport demand between the logistics sorting center and the plurality of stations determined according to historical transport tasks. This embodiment does not limit this.

[0034] ​In an optional embodiment, determining the target transportation task can include: obtaining historical freight volumes between the logistics sorting center and the plurality of stations; and performing statistical analysis on the historical freight volumes to obtain the target transportation task.

[0035] The historical freight volumes can be a batch of freight volume sample data between the logistics sorting center and the plurality of stations in a historical period (for example, in a historical month), or a certain number (for example, the last 100) of freight volume sample data. This embodiment is not limited in this regard.

[0036] In this embodiment, historical batch freight volume data between the logistics sorting center and the plurality of stations can be obtained as sample data for statistical analysis, and a stable transportation demand can be obtained as the target transportation task between the logistics sorting center and the plurality of stations.

[0037] Based on the above optional embodiment, performing statistical analysis on the historical freight volumes to obtain the target transportation task can include: determining a stable freight volume between the logistics sorting center and each of the plurality of stations according to historical freight volumes between the logistics sorting center and each of the plurality of stations; and determining the target transportation task according to the stable freight volume between the logistics sorting center and each of the plurality of stations.

[0038] In this embodiment, the data dispersion of the historical freight volumes can be statistically analyzed (for example, by calculating the variance and range of the historical freight volumes to analyze the data dispersion thereof), and a stable freight volume between the logistics sorting center and each of the plurality of stations can be determined based on the data dispersion, so as to determine the target transportation task according to the stable freight volume. For example, the logistics sorting center corresponds to station 1, station 2, station 3, station 4, and station 5, and the target transportation task is that the logistics sorting center transports 5 tons of goods to station 1, 6 tons of goods to station 2, 3 tons of goods to station 3, 4 tons of goods to station 4, and 1 ton of goods to station 5 per day; wherein the 5 tons of goods, the 6 tons of goods, the 3 tons of goods, the 4 tons of goods, and the 1 ton of goods are target freight volumes.

[0039] S120, generating a transportation path between the logistics sorting center and the plurality of stations according to the target transportation task and the path generation constraint condition.

[0040] The path generation constraint condition can include a first constraint condition, a second constraint condition, and a third constraint condition. The first constraint condition is a time efficiency connection limit condition between adjacent stations, the second constraint condition is a distance limit condition for empty vehicle driving between adjacent sub-transportation tasks in the execution order in the target transportation task, and the third constraint condition is a regional limit condition for the transportation path between adjacent stations.

[0041] In this embodiment, the transportation path may refer to the path of the vehicle between the logistics sorting center and the stations 1, 2, 3, 4 and 5 for completing the target transportation task.

[0042] For example, to determine whether the transportation path can be planned as station 1→ station 3, the first constraint condition can be used to determine whether the time consumed by the vehicle for completing the loading and unloading operation of the sub-transportation task 1 at station 1 and the time consumed by the vehicle for driving between station 1 and station 3 cannot exceed the latest arrival time of station 3; the second constraint condition can be used to determine whether the distance between the destination station (station 1) of the sub-transportation task 1 and the starting station (station 3) of the sub-transportation task 2 cannot exceed the set distance threshold; and the third constraint condition can be used to determine whether the number of regions (number of cities or number of districts in a city, etc.) involved in the path between station 1 and station 3 cannot exceed the set number threshold.

[0043] In this embodiment, the transportation path between the logistics sorting center and the stations 1, 2, 3, 4 and 5 can be generated according to the target transportation task, the first constraint condition, the second constraint condition and the third constraint condition, such as station 1→ station 3→ station 2→ station 5→ station 4.

[0044] S130, generating transportation cost data for different types of schedulable vehicles according to the transportation path and the target freight volume.

[0045] The schedulable vehicles have different types, such as self-operated or third-party vehicles, electric vehicles or oil vehicles, and small or large vehicles.

[0046] Optionally, generating transportation cost data for different types of schedulable vehicles according to the transportation path and the target freight volume can include: obtaining a current transportation sub-path from the transportation path, and obtaining a current target freight volume corresponding to the current transportation sub-path; and generating transportation cost data for different types of schedulable vehicles corresponding to a starting station of the current transportation sub-path according to the current target freight volume.

[0047] For example, the transportation sub-paths in the transportation path station 1→ station 3→ station 2→ station 5→ station 4 include station 1→ station 3, station 3→ station 2, station 2→ station 5 and station 5→ station 4; the freight volume corresponding to each transportation sub-path is further obtained; and finally, transportation cost data for various schedulable vehicles is generated according to the corresponding freight volume between station 1 and station 3, between station 3 and station 2, between station 2 and station 5, and between station 5 and station 4.

[0048] S140, generating a scheduling scheme for the schedulable vehicles based on the transportation path according to the transportation cost data and the vehicle scheduling constraint conditions.

[0049] The vehicle scheduling constraint conditions include a fourth constraint condition, a fifth constraint condition, and a sixth constraint condition; the fourth constraint condition is a quantity limit condition of the schedulable vehicles of a specified category, the fifth constraint condition is a limit condition of the number of times of execution of the target transportation task, and the sixth constraint condition is a category limit condition of the schedulable vehicles that execute the target transportation task.

[0050] The fourth constraint condition can refer to that the number of self-operated vehicles of different vehicle types used should not exceed the maximum number of the existing self-operated vehicles of the vehicle type; the fifth constraint condition can refer to that the transportation task of each station can only be executed once; and the sixth constraint condition can refer to that the transportation task of each station can only be executed by one vehicle type.

[0051] As a continuation of the previous example, the schedulable vehicle scheme can be determined for the transportation sub-path of station 1→station 3, the schedulable vehicle scheme can be determined for the transportation sub-path of station 3→station 2, the schedulable vehicle scheme can be determined for the transportation sub-path of station 2→station 5, and the schedulable vehicle scheme can be determined for the transportation sub-path of station 5→station 4 according to the transportation cost data, the fourth constraint condition, the fifth constraint condition, and the sixth constraint condition, so as to achieve the goal of generating a scheduling scheme for the schedulable vehicles based on the transportation path.

[0052] Optionally, generating a scheduling scheme for the schedulable vehicles based on the transportation path according to the transportation cost data and the vehicle scheduling constraint conditions can include: solving a preset vehicle and path scheduling basic model to obtain the scheduling scheme; wherein the preset vehicle and path scheduling basic model takes the minimum transportation cost data of the schedulable vehicles based on the transportation path as a solving target; and the preset vehicle and path scheduling basic model is established with the fourth constraint condition, the fifth constraint condition, and the sixth constraint condition as constraints.

[0053] When the transportation sub-paths corresponding to the station sub-transportation tasks and the transportation cost data of each transportation sub-path in different vehicles are known, how to allocate vehicle resources is a typical NP-hard problem. Because the vehicle resources exist in types such as "self-operated or third party", "electric vehicle or oil vehicle", and "small vehicle type or large vehicle type"; and the station sub-transportation tasks also have requirements such as "whether the oil vehicle is limited" and "whether the vehicle type is limited in height and width". In order to achieve the goal of cost optimization of vehicle path allocation, a mathematical programming model can be constructed to solve the "path-vehicle resource" allocation.

[0054] For example, the preset vehicle and path scheduling basic model in the embodiment can be as follows:

[0055] Algorithm target:

[0056] Constraint expression:

[0057] Wherein, I represents a sub-transportation task set in a target transportation task; M represents a sub-transportation task quantity; K represents a path starting point task set, K belongs to set I; J represents a full-quantity feasible path set generated based on the sub-transportation task set I; C represents a self-operated vehicle quantity; J k represents a set with task k as a starting point, J k belongs to set J; A={a ij} represents that when the path j contains the sub-transportation task i, a ij =1, otherwise, a ij =0; x kj represents that the jth path with task k as a starting point is transported by a self-operated vehicle, x kj =1, otherwise, x kj =0; y kj represents that the jth path with task k as a starting point is transported by a third-party vehicle, y kj =1, otherwise, y kj =0; z j represents that the jth path is used, z j =1, otherwise, z j =0; zy kj represents a cost of the jth path with task k as a starting point transported by a self-operated vehicle; sf kj represents a cost of the jth path with task k as a starting point transported by a third-party vehicle.

[0058] The technical scheme of the present application determines a target transportation task, the target transportation task including a target freight volume between a logistics sorting center and multiple stations; generates a transportation path between the logistics sorting center and the multiple stations according to a constraint condition of the target transportation task and the path; generates transportation cost data for different types of schedulable vehicles according to the transportation path and the target freight volume; and generates a scheduling scheme for the schedulable vehicles based on the transportation path according to the transportation cost data and a vehicle scheduling constraint condition. Because the technical means of simultaneously considering the path planning feasibility and the vehicle resource allocation effect best two coupled constraints are adopted, the technical problems that the prior art does not comprehensively consider the cost element, the vehicle capacity resource maximum utilization, the path executability and the like, and the vehicle scheduling result can be poor in the scene that the transportation demand is unbalanced and the vehicle resource composition is complex are overcome, and the technical effect of efficiently determining a vehicle scheduling method with good service cost is achieved.

[0059] Figure 1b A complete flowchart of a vehicle scheduling method provided by the first embodiment of the present application.

[0060] Step 1, generating a target transportation task: determining stable transportation demand according to historical transportation tasks, and determining the target transportation task according to the stable transportation demand;

[0061] Step 2, generating a transportation path: searching a transportation path according to the target transportation task;

[0062] Step 3, calculating vehicle cost on the transportation path: calculating transportation cost of different types of schedulable vehicles (self-operated or third-party vehicles) on the transportation path;

[0063] Step 4, realizing vehicle resource allocation (scheduling) on the transportation path: obtaining an allocation result (scheduling scheme) by constructing a mathematical programming model.

[0064] Figure 2a Another flowchart of a vehicle scheduling method provided for an embodiment of the present application, the embodiment is based on the above-mentioned embodiments, and the S120 operation is refined, as shown in the following figure: Figure 2a The method specifically includes the following steps:

[0065] S210, determining a target transportation task, the target transportation task including target freight volume between a logistics sorting center and multiple sites.

[0066] In the target transportation task, the departure demand time and the arrival demand time of each site can also be included. The departure demand time can refer to the departure time of the sub-transportation task corresponding to the site, and the departure demand time can include the departure time of the sub-transportation task arriving at the site and the departure time of the sub-transportation task leaving the site; the arrival demand time can refer to the arrival time of the sub-transportation task arriving at the site. For example, the sub-transportation task corresponding to site 1 includes sub-transportation task 1 arriving at site 1 and sub-transportation task 2 leaving site 1, and the departure demand time of site 1 can include the departure time of sub-transportation task 1 and sub-transportation task 2, and the arrival demand time of site 1 can refer to the arrival time of sub-transportation task 1.

[0067] S220, determining a current site from the multiple sites, the current site being the site with the earliest departure demand time among the multiple sites.

[0068] In the embodiment, the site corresponding to the sub-transportation task with the earliest departure time among the sub-transportation tasks arriving at the multiple sites can be determined as the current site.

[0069] Taking an example for illustration, assuming that sub-transportation task 1 is to transport 5 tons of goods from the logistics sorting center at 8 o'clock in the morning, arrive at site 1 at 10 o'clock in the morning, and sub-transportation task 1 is determined as the sub-transportation task with the earliest departure time according to 8 o'clock in the morning, then the arrival site (site 1) of sub-transportation task 1 can be determined as the current site.

[0070] S230, determining a reachable next station for the current station according to the first constraint condition, the second constraint condition and the third constraint condition.

[0071] The first constraint condition is a time limit condition between adjacent stations, the second constraint condition is a distance limit condition of empty vehicle driving between adjacent sub-transportation tasks in the execution order, and the third constraint condition is a regional limit condition of the transportation path between adjacent stations.

[0072] Optionally, the determining of the reachable next station for the current station according to the first constraint condition, the second constraint condition and the third constraint condition can include: determining a candidate station from the remaining stations, the remaining stations being the stations except the current station in the plurality of stations, and the candidate station being the station whose departure demand time is equal to or later than the arrival demand time of the current station; and determining the station satisfying the first constraint condition, the second constraint condition and the third constraint condition from the candidate stations as the reachable next station.

[0073] For example, the current station is station 1, and the remaining stations can be station 2, station 3, station 4 and station 5. Further, the station whose departure demand time (which can refer to the departure time of the sub-transportation task leaving the station) is equal to or later than the arrival demand time (i.e. 10:00 am) of the current station can be selected as the candidate station (assuming station 2, station 3 and station 5), so that the station (assuming station 3) satisfying the first constraint condition, the second constraint condition and the third constraint condition among the candidate stations can be determined as the reachable next station of station 1.

[0074] S240, generating the transportation path according to the current station and the reachable next station.

[0075] Optionally, the generating of the transportation path according to the current station and the reachable next station can include: connecting the reachable next station with the current station to generate a current transportation path; determining whether the number of regions crossed by the current transportation path reaches a preset number threshold; if not, taking the reachable next station as a new current station and returning to perform the operation of determining the reachable next station for the current station according to the first constraint condition, the second constraint condition and the third constraint condition; and if so, taking the current transportation path as the transportation path.

[0076] As shown in the preceding example, site 3 can be connected with site 1 to generate a current transportation path "site 1→site 3", and it is further determined whether the number of cross-regions of the current transportation path "site 1→site 3" reaches a preset number threshold, if yes, "site 1→site 3" can be taken as the transportation path, that is, the current transportation path ends after reaching site 3; if no, site 3 can be taken as a new current site, and candidate sites (supposedly site 2 and site 5) of site 3 are determined from the remaining sites (site 2, site 4 and site 5), and a site (supposedly site 2) meeting the first constraint condition, the second constraint condition and the third constraint condition is determined as the reachable next site of site 3.

[0077] The operations of S220-S240 extend each actual physical site based on a space-time network flow model, consider different constraints at different times, use a tree search algorithm and combine a common pruning method in an actual scene to modularize construction of full-path generation, and establish a standardized and multi-scene covering path generation mechanism.

[0078] S250, generating transportation cost data for different types of schedulable vehicles according to the transportation path and the target freight volume.

[0079] S260, generating a scheduling scheme for the schedulable vehicles based on the transportation path generation according to the transportation cost data and the vehicle scheduling constraint condition.

[0080] The technical scheme of the present application determines a target transportation task, the target transportation task including target freight volumes between a logistics sorting center and multiple sites; determines a current site from the multiple sites, the current site being a site with the earliest departure demand time among the multiple sites; determines a reachable next site for the current site according to a first constraint condition, a second constraint condition and a third constraint condition; generates a transportation path according to the current site and the reachable next site; generates transportation cost data for different types of schedulable vehicles according to the transportation path and the target freight volume; and generates a scheduling scheme for the schedulable vehicles based on the transportation path generation according to the transportation cost data and the vehicle scheduling constraint condition. Because the technical means of simultaneously considering the two coupled constraints of path planning feasibility and vehicle resource allocation effect is adopted, the technical problems of poor landability of vehicle scheduling results in the scene of unbalanced transportation demand and complex vehicle resource composition due to the problems of not comprehensively considering cost factors, maximum utilization of vehicle capacity resources and path executability in the prior art are overcome, and the technical effect of efficiently determining a vehicle scheduling method with good service cost is achieved.

[0081] In order for those skilled in the art to better understand the transportation path generation method in the second embodiment, Figure 2b A transportation path generation flowchart is provided for the second embodiment of the present application.

[0082] Find the task with the earliest departure time from the site transportation demand job (equivalent to the target transportation task) as the starting task job_i;

[0083] Generate a feasible path route with the starting task being job_i;

[0084] Get the current station C (equivalent to the arrival station of the starting task job_i), and search the job for a set of candidate stations C_jobs whose departure time is later than or equal to the arrival time of the transport task at the current station C;

[0085] If C_jobs is empty, it means that there is no station in the job whose departure time is later than or equal to the arrival time of the transportation task of the current station C, and the route ends at the current station C and does not grow any further; if C_jobs is not empty, a station c_next can be randomly selected from it;

[0086] Determine whether the first, second, and third constraints are met between c_next and C. If any of the conditions are not met, delete c_next from C_jobs to update C_jobs, and re-extract a new c_next from the updated C_jobs to check the constraints. If there are no unchecked candidate sites in the updated C_jobs, the route ends at the current site C and does not continue to grow.

[0087] If c_next and C meet the first, second, and third constraints, c_next can be added to the route, that is, the current route is planned from the current site C to c_next;

[0088] If the current route has reached the upper limit of the number of cities, the route will stop growing after reaching c_next. If the upper limit of the number of cities has not been reached, the job can be searched for a set of candidate stations whose departure time is later than or equal to the arrival time of the c_next transport task.

[0089] The following is an embodiment of a vehicle dispatching device provided by an embodiment of the present invention. The device and the vehicle dispatching method of the above embodiment 1 belong to the same inventive concept. For details not fully described in the embodiment of the vehicle dispatching device, reference can be made to the contents of the above embodiments.

[0090] Figure 3 A schematic diagram of the structure of a vehicle dispatching device provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the device includes: a transport task determination module 310, a transport path generation module 320, a transport cost generation module 330 and a scheduling solution generation module 340.

[0091] The transportation task determination module 310 is configured to determine a target transportation task, wherein the target transportation task comprises target freight volumes between a logistics sorting center and a plurality of stations.

[0092] The transportation path generation module 320 is configured to generate transportation paths between the logistics sorting center and the plurality of stations according to the target transportation task and path generation constraints.

[0093] The transportation cost generation module 330 is configured to generate transportation cost data for different types of schedulable vehicles respectively according to the transportation paths and the target freight volumes.

[0094] The scheduling scheme generation module 340 is configured to generate a scheduling scheme for the schedulable vehicles based on the transportation paths according to the transportation cost data and vehicle scheduling constraints.

[0095] The technical scheme of the embodiment of the present application determines a target transportation task, wherein the target transportation task comprises target freight volumes between a logistics sorting center and a plurality of stations; generates transportation paths between the logistics sorting center and the plurality of stations according to the target transportation task and path generation constraints; generates transportation cost data for different types of schedulable vehicles respectively according to the transportation paths and the target freight volumes; and generates a scheduling scheme for the schedulable vehicles based on the transportation paths according to the transportation cost data and vehicle scheduling constraints. Because the technical scheme simultaneously considers the two coupled constraints of path planning feasibility and best vehicle resource allocation effect, it overcomes the technical problems of the prior art that do not comprehensively consider cost factors, maximum utilization of vehicle capacity resources, and path executability, which results in poor feasibility of vehicle scheduling results in scenarios where transportation demand is uneven and vehicle resources are complex, thereby achieving the technical effect of efficiently determining a vehicle scheduling method with good service cost.

[0096] In the above device, optionally, the transportation task determination module 310 can comprise:

[0097] The historical freight volume acquisition unit is configured to acquire historical freight volumes between the logistics sorting center and the plurality of stations.

[0098] The transportation task determination unit is configured to statistically analyze the historical freight volumes to obtain the target transportation task.

[0099] In the above device, optionally, the transportation task determination unit can be specifically configured to:

[0100] determine stable freight volumes between the logistics sorting center and each of the plurality of stations according to historical freight volumes between the logistics sorting center and each of the plurality of stations.

[0101] The target transportation task is determined according to a stable freight volume between the logistics distribution center and each of the stations.

[0102] Optionally, in the device, the path generation constraint condition comprises a first constraint condition, a second constraint condition and a third constraint condition; the first constraint condition is a time limit connection limit condition between adjacent stations, the second constraint condition is a distance limit condition for empty vehicle driving of sub-transportation tasks that are adjacent in execution order in the target transportation task, and the third constraint condition is a region limit condition for a transportation path between adjacent stations; the target transportation task further comprises a departure demand time of each station.

[0103] Correspondingly, the transportation path generation module 320 comprises:

[0104] A current station determination unit is configured to determine a current station from the plurality of stations, the current station being a station with the earliest departure demand time among the plurality of stations;

[0105] A reachable next station determination unit is configured to determine a reachable next station for the current station according to the first constraint condition, the second constraint condition and the third constraint condition.

[0106] A transportation path generation unit is configured to generate the transportation path according to the current station and the reachable next station.

[0107] Optionally, in the device, the target transportation task further comprises an arrival demand time of each station.

[0108] Correspondingly, the reachable next station determination unit can be specifically configured to:

[0109] determine a candidate station from remaining stations, the remaining stations being stations other than the current station among the plurality of stations, and the candidate station being a station with a departure demand time equal to or later than an arrival demand time of the current station among the remaining stations;

[0110] determine a station that satisfies the first constraint condition, the second constraint condition and the third constraint condition from the candidate station as the reachable next station.

[0111] Optionally, in the device, the transportation path generation unit can be specifically configured to:

[0112] connect the reachable next station and the current station to generate a current transportation path;

[0113] determine whether a cross-region quantity of the current transportation path reaches a preset quantity threshold;

[0114] If not, the reachable next station is taken as a new current station, and the operation of determining a reachable next station for the current station according to the first constraint condition, the second constraint condition and the third constraint condition is performed again.

[0115] If yes, the current transportation path is taken as the transportation path.

[0116] In the device, optionally, the transportation cost generation module 330 can be specifically configured to:

[0117] A current transportation sub-path is obtained from the transportation path, and a current target freight volume corresponding to the current transportation sub-path is obtained.

[0118] Transportation cost data is generated for different types of schedulable vehicles corresponding to a starting station in the current transportation sub-path according to the current target freight volume.

[0119] In the device, optionally, the vehicle scheduling constraint condition comprises a fourth constraint condition, a fifth constraint condition and a sixth constraint condition; the fourth constraint condition is a quantity limitation condition of the schedulable vehicles of a specified category, the fifth constraint condition is a limitation condition of the number of times of execution of the target transportation task, and the sixth constraint condition is a category limitation condition of the schedulable vehicles for executing the target transportation task.

[0120] Correspondingly, the scheduling scheme generation module 340 can be specifically configured to:

[0121] The preset vehicle and path scheduling basic model is solved to obtain the scheduling scheme.

[0122] The preset vehicle and path scheduling basic model takes the minimum transportation cost data of the schedulable vehicles based on the transportation path as a solving target, and is established with the fourth constraint condition, the fifth constraint condition and the sixth constraint condition as constraints.

[0123] The vehicle scheduling device provided in the embodiment of the application can execute the vehicle scheduling method provided in the embodiment one of the application, and has the corresponding function modules and beneficial effects of executing the vehicle scheduling method.

[0124] Figure 4 A structural schematic diagram of an electronic device provided in the embodiment of the application is provided. Figure 4 A block diagram of an exemplary server 12 suitable for use in implementing embodiments of the application is shown. Figure 4 The server 12 shown is merely an example and should not be taken as limiting the functionality or the scope of use of embodiments of the application.

[0125] As Figure 4As shown, the server 12 is in the form of a general-purpose computing device. The components of server 12 can include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including system memory 28 to processor 16.

[0126] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics bus (e.g., AGP, PCI-Express bus), a processor or local bus using any of a variety of bus architectures.

[0127] Server 12 typically includes a variety of computer system readable media. Such media can be any available media that is locally and / or remotely accessible by server 12, including volatile and non-volatile media, removable and non-removable media.

[0128] System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Server 12 can further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (e.g., a "hard drive"). Figure 4 not shown, a magnetic hard disk drive for reading from and writing to non-removable, non-volatile magnetic media (e.g., a "hard drive"). Although not specifically shown, such Figure 4 In alternative embodiments, a magnetic hard disk drive, a solid state drive (SSD) which is a non- volatile computer storage media, a floppy disk drive for reading from and / or writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk"), and / or an optical disk drive for reading from or writing to a removable, non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, etc.) can be provided. In such instances, each can be connected to bus 18 by one or more data media interfaces. The drives and their associated computer system storage media, described above and below, can also be connected to server 12 by a storage area network (SAN), or other

[0129] Program / utility 40, having a set (at least one) of program modules 42, can be stored in, for example, system memory 28 by way of example, and can include an operating system, one or more application programs, other program modules, and program data, each of which or a combination thereof, can include implementation of a network environment as described herein. Program modules 42 generally carry out the functions and / or methodologies of embodiments of the present application as described herein.

[0130] The server 12 can also be in communication with one or more external devices 14 such as a keyboard, a pointing device, a display 24, etc.; can also be in communication with one or more devices that enable a user to interact with the server 12; and / or can be in communication with any devices (such as a network card, a modem, etc.) that enable the server 12 to communicate with one or more other computing devices. Such communication can be facilitated by an input / output (I / O) interface 22. Still yet, the server 12 can be in communication with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or the Internet) through a network adapter 20. As an example, the network adapter 20 can be capable of communicating with the other modules of the server 12 through the bus 18. It should be appreciated that the server 12 can be capable of operating in a client-server environment, or in a peer-to-peer environment, or in an environment that is a hybrid of the two, depending on the particular implementation.

[0131] The processing unit 16 performs various function applications and data processing by running programs stored in the system memory 28, such as implementing a vehicle scheduling method provided by the embodiment one, the method comprising:

[0132] determining a target transportation task, the target transportation task comprising target freight volumes between a logistics sorting center and a plurality of sites;

[0133] generating transportation paths between the logistics sorting center and the plurality of sites according to the target transportation task and path generation constraints;

[0134] generating transportation cost data for different types of schedulable vehicles according to the transportation paths and the target freight volumes;

[0135] generating a scheduling scheme for the schedulable vehicles based on the transportation paths according to the transportation cost data and vehicle scheduling constraints.

[0136] Of course, those skilled in the art can understand that the processor can also implement the technical solutions of the vehicle scheduling method provided by any embodiment of the present application.

[0137] The embodiment provides a computer readable storage medium, which stores a computer program, the program being executed by a processor to implement a vehicle scheduling method provided by the foregoing embodiment of the present application, the method comprising:

[0138] determining a target transportation task, the target transportation task comprising target freight volumes between a logistics sorting center and a plurality of sites;

[0139] generate a transportation path between the logistics distribution center and the plurality of sites according to the target transportation task and path generation constraint condition;

[0140] generate transportation cost data of different types of schedulable vehicles respectively according to the transportation path and the target freight volume;

[0141] generate a scheduling scheme of the schedulable vehicles based on the transportation path according to the transportation cost data and vehicle scheduling constraint condition.

[0142] The computer storage medium of the embodiments of the present application can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0143] The computer readable signal medium can include a data signal propagating in a baseband or as part of a carrier wave propagating through a transmission medium, in which the computer readable program code is embodied. Such a propagating data signal can take many forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can transmit, propagate or transport a program for use by or in connection with an instruction execution system, apparatus or device.

[0144] The program code contained on the computer readable medium can be transmitted in any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination thereof.

[0145] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0146] Those skilled in the art will appreciate that the modules or steps of the present application described above can be implemented in a general purpose computer, and they can be centralized in a single computing device or distributed over a network of multiple computing devices. Alternatively, they can be implemented by computer executable program codes, which can be stored in a storage device and executed by a computing device, or they can be implemented by individual integrated circuit modules, or a plurality of modules or steps can be implemented by a single integrated circuit module. Thus, the present application is not limited to any particular combination of hardware and software.

[0147] Note that the above only describes the preferred embodiments of the present application and the principles of the applied technology. Those skilled in the art will understand that the present application is not limited to the specific embodiments described above, and that various obvious changes, re-adjustments and substitutions can be made without departing from the scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments, and can include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A vehicle dispatching method characterized by comprising: The method comprises: determining a target transportation task, the target transportation task comprising target freight volumes between a logistics sorting center and a plurality of stations; generating transportation paths between the logistics sorting center and the plurality of stations according to the target transportation task and path generation constraints; generating transportation cost data for different types of schedulable vehicles respectively according to the transportation paths and the target freight volumes; generating a scheduling scheme for the schedulable vehicles based on the transportation paths according to the transportation cost data and vehicle scheduling constraints.

2. The method of claim 1, wherein, The determination of the target transportation task comprises: obtaining historical freight volumes between the logistics sorting center and the plurality of stations; statistically analyzing the historical freight volumes to obtain the target transportation task.

3. The method of claim 2, wherein, The statistical analysis of the historical freight volumes to obtain the target transportation task comprises: determining stable freight volumes between the logistics sorting center and each station in the plurality of stations according to historical freight volumes between the logistics sorting center and each station in the plurality of stations; determining the target transportation task according to the stable freight volumes between the logistics sorting center and each station.

4. The method of claim 1, wherein, The path generation constraints comprise a first constraint condition, a second constraint condition and a third constraint condition; the first constraint condition is a time efficiency connection limit condition between adjacent stations, the second constraint condition is a limit condition of empty vehicle driving distance for sub-transportation tasks that are adjacent in execution order in the target transportation task, and the third constraint condition is a regional limit condition of transportation paths between adjacent stations; the target transportation task further comprises departure demand times of each station; The generation of the transportation paths between the logistics sorting center and the plurality of stations according to the target transportation task and path generation constraints comprises: determining a current station from the plurality of stations, the current station being a station with the earliest departure demand time among the plurality of stations; determining a reachable next station for the current station according to the first constraint condition, the second constraint condition and the third constraint condition; generating the transportation paths according to the current station and the reachable next station.

5. The method of claim 4, wherein, The target transportation task further comprises arrival demand times of each station; The determination of the reachable next station for the current station according to the first constraint condition, the second constraint condition and the third constraint condition comprises: determining a candidate station from remaining stations, the remaining stations being stations other than the current station among the plurality of stations, and the candidate station being a station with an arrival demand time equal to or later than the arrival demand time of the current station among the remaining stations; determining a station satisfying the first constraint condition, the second constraint condition and the third constraint condition from the candidate station as the reachable next station.

6. The method of claim 4, wherein, The generation of the transportation paths according to the current station and the reachable next station comprises: connecting the reachable next station and the current station to generate a current transportation path; determining whether a cross-regional quantity of the current transportation path reaches a preset quantity threshold; If not, the reachable next station is taken as a new current station, and the operation of determining a reachable next station for the current station according to the first constraint condition, the second constraint condition and the third constraint condition is performed again; If yes, the current transportation path is taken as the transportation path.

7. The method of claim 1, wherein, The transportation cost data for different types of dispatchable vehicles is generated respectively according to the transportation path and the target freight volume, including: A current transportation sub-path is obtained from the transportation path, and a current target freight volume corresponding to the current transportation sub-path is obtained; Transportation cost data for different types of dispatchable vehicles corresponding to a starting station in the current transportation sub-path is generated respectively according to the current target freight volume.

8. The method of claim 1, wherein, The vehicle scheduling constraint conditions include a fourth constraint condition, a fifth constraint condition and a sixth constraint condition; the fourth constraint condition is a quantity limit condition for the dispatchable vehicles of a specified category, the fifth constraint condition is a limit condition for the number of times of execution of the target transportation task, and the sixth constraint condition is a category limit condition for the dispatchable vehicles that execute the target transportation task; The scheduling scheme for the dispatchable vehicles is generated based on the transportation path according to the transportation cost data and the vehicle scheduling constraint conditions, including: A preset vehicle and path scheduling basic model is solved to obtain the scheduling scheme; The preset vehicle and path scheduling basic model takes the minimum transportation cost data of the dispatchable vehicles based on the transportation path as a solving target, and is established with the fourth constraint condition, the fifth constraint condition and the sixth constraint condition as constraints.

9. A vehicle dispatching device characterized by comprising: including: A transportation task determination module is configured to determine a target transportation task, the target transportation task including target freight volumes between a logistics sorting center and multiple stations; A transportation path generation module is configured to generate a transportation path between the logistics sorting center and the multiple stations according to the target transportation task and path generation constraint conditions; A transportation cost generation module is configured to generate transportation cost data for different types of dispatchable vehicles respectively according to the transportation path and the target freight volumes; A scheduling scheme generation module is configured to generate a scheduling scheme for the dispatchable vehicles based on the transportation path according to the transportation cost data and vehicle scheduling constraint conditions.

10. An electronic device, comprising: The electronic device includes: One or more processors; Memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle scheduling method according to any one of claims 1-8.

11. A computer readable storage medium having stored thereon a computer program, characterized in that The program is executed by the processor to implement the vehicle scheduling method according to any one of claims 1-8.

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