Scheduling method and scheduling device for logistics distribution vehicles

Abstract

The invention discloses a logistics distribution vehicle scheduling method and scheduling device, and relates to the technical field of warehouse logistics. A specific embodiment of the method comprises the following steps: receiving a delivery order of each project and obtaining delivery information of each delivery order; based on the delivery information of the delivery order, acquiring a co-distribution line combination of co-distribution items capable of sharing delivery vehicles in the items; for each co-matching item, calculating a target vehicle use scheme which is most matched with a preset target; and vehicle scheduling is carried out according to the target vehicle usage scheme, and vehicle usage information of distribution vehicles of all the co-distribution items is output. According to the embodiment, the optimal balance between the cost and the timeliness can be realized, and multi-project single-line common delivery is realized by mining orders capable of being delivered together and performing optimal matching on carriers of a plurality of projects, so that the average cost of line parties is reduced, low cost and high timeliness are realized, resources are fully utilized, and the overall transportation efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of warehousing and logistics technology, and in particular to a method and device for scheduling logistics delivery vehicles. Background Technology

[0002] In logistics and distribution, deliveries from a merchant's warehouse to their store are mostly project-based point-to-point transportation contracts. The same carrier handles all transportation from the pickup point to all destinations nationwide for the same merchant. Therefore, when deciding on a vehicle dispatch plan, there are two options: Option 1: From a cost-optimal perspective, a carrier will only accept goods from one merchant on a single trunk route, maximizing cargo volume and waiting until all orders for delivery that day are completed before selecting vehicle type and frequency; Option 2: From a time-optimal perspective, only the timeliness of orders should be considered, taking into account the number of daily dispatches.

[0003] In the process of realizing this invention, the inventors discovered that the prior art has at least the following problems: all routes of the same project are operated by the same carrier. On some popular routes, vehicles may be fully loaded or even overloaded, while on less popular routes, vehicles may be empty or underloaded. This makes it impossible to balance cost and efficiency, resulting in uneven resource utilization and reduced overall transportation efficiency. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a scheduling method and scheduling device for logistics delivery vehicles, which can achieve an optimal balance between cost and timeliness. By mining orders that can be jointly delivered, carriers for multiple projects are optimally matched to achieve joint delivery of multiple projects on a single route, thereby reducing the average cost per route. This achieves a cost advantage over scheme 2, but better timeliness than scheme 1, making full use of resources and improving overall transportation efficiency.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for scheduling logistics delivery vehicles is provided, comprising:

[0006] Receive delivery orders for each project and obtain delivery information for each order.

[0007] Based on the delivery information of the delivery orders, obtain the shared delivery route combinations for shared delivery projects that can share delivery vehicles among the projects.

[0008] For each shared allocation project, calculate the target vehicle usage plan that best matches the preset target, and

[0009] Based on the target vehicle usage plan, vehicle scheduling is performed, and vehicle usage information for delivery vehicles of each shared distribution project is output.

[0010] According to the scheduling method for logistics delivery vehicles with the above configuration, when scheduling delivery vehicles for delivery orders, vehicle sharing can be achieved for delivery items that meet the requirements, and delivery can be carried out using the vehicle usage plan that best matches the preset goals. Therefore, optimizing vehicle scheduling can make vehicle travel routes more reasonable, effectively improve vehicle utilization, reduce delivery costs, and reasonably plan scheduled time periods, such as daily vehicle delivery shifts, ensuring delivery timeliness, achieving a balance between cost and efficiency, improving resource utilization, and enhancing overall transportation efficiency. The scheduling method for logistics delivery vehicles with the above configuration significantly improves customer satisfaction.

[0011] Optionally, in the scheduling method according to the first aspect of the present invention, obtaining a shared delivery route combination for shared delivery projects capable of sharing delivery vehicles includes: obtaining a shared delivery route combination for shared delivery projects with matching forward and reverse routes, wherein the matching of forward and reverse routes includes: the departure city of the forward delivery route matching the destination city of the reverse delivery route, and the destination city of the forward delivery route matching the departure city of the reverse delivery route; the overlap rate of the delivery frequency of the forward route and the reverse route within a predetermined time period exceeds a specific value, and the overlap rate of the vehicle type exceeds a specific value; the end-of-line delivery scenario of the forward route matching the end-of-line delivery scenario of the reverse route; the product categories of the forward route and the product categories of the reverse route not being mutually exclusive; and the end-of-line delivery requirements of the forward route matching the end-of-line delivery requirements of the reverse route.

[0012] Based on the scheduling method for logistics delivery vehicles with the above configuration, shared delivery can be achieved from both forward and reverse delivery perspectives. When scheduling delivery vehicles for delivery orders, the system can effectively identify projects that can share delivery vehicles by comprehensively evaluating at least five dimensions: route matching overlap rate (forward and reverse city matching), shipping demand (shipping frequency matching), last-mile scenario (warehouse, store, supermarket, etc. matching), multi-item shipping categories (industry attributes), and customized KPIs (last-mile delivery requirement matching). This enables shared delivery of forward and reverse routes for nationwide delivery, and real-time recommendations for delivery vehicles for pre-ordered waves of orders, improving vehicle utilization and ensuring timeliness.

[0013] Optionally, in the scheduling method according to the first aspect of the present invention, obtaining the shared delivery route combination of shared delivery projects that can share delivery vehicles further includes: mining interest surfaces in historical delivery order data, combining the distribution information of goods volume and geographical data information of historical delivery orders, using a hierarchical clustering method to divide each administrative region in the city into multiple interest surfaces, and configuring the city of origin as a specific interest surface of the city, and configuring the city of destination as a specific interest surface of the city.

[0014] The scheduling method for logistics delivery vehicles with the above configuration can take into account two important factors: geographically separated delivery routes and controllable cargo volume within a category, making route matching more accurate and efficient. By mining AOI (Area of ​​Interest) information from historical order data, we can better understand the distribution of goods and transportation needs, thus providing a more accurate reference for forward and reverse route matching.

[0015] Optionally, in the scheduling method according to the first aspect of the present invention, obtaining a shared delivery route combination of shared delivery projects capable of sharing delivery vehicles includes: obtaining a shared delivery route combination of shared delivery projects with matching same-city routes, wherein the same-city route matching includes: the pickup stations of the same-city routes of each shared delivery project are compatible; the delivery stations of the same-city routes of each shared delivery project are compatible; the categories of goods shipped on the same-city routes of each shared delivery project are not mutually exclusive; and the end-of-line delivery requirements of the same-city routes of each shared delivery project are matched.

[0016] Based on the scheduling method for logistics delivery vehicles with the above configuration, shared delivery can be achieved from the perspective of same-city delivery. By considering four dimensions—matching pick-up points on the same-city routes of each shared delivery project, matching delivery points on the same-city routes of each shared delivery project, ensuring non-exclusive categories of shipped goods on the same-city routes of each shared delivery project, and matching last-mile delivery requirements on the same-city routes of each shared delivery project—projects that can share delivery vehicles can be effectively identified. This enables shared delivery of same-city routes during same-city delivery, and real-time recommendations of delivery vehicles for pre-ordered delivery waves can improve vehicle utilization and ensure timeliness.

[0017] In addition, the same-city delivery of the present invention includes same-city delivery where the departure city and destination city of each item's shipping route are matched, and same-city delivery where the departure station and destination station are the same in the same city.

[0018] Optionally, in the scheduling method according to the first aspect of the present invention, obtaining a shared delivery route combination for shared delivery projects that can share delivery vehicles includes: obtaining a shared delivery route combination for shared delivery projects that is matched with dedicated line routes, wherein the matching of dedicated line routes includes: the minimum aggregated business of dedicated line routes for each shared delivery project is greater than a predetermined threshold; the overlap of dispatch frequency of dedicated line routes for each project within a predetermined time period is greater than a predetermined value; the capacity of dedicated line routes for each shared delivery project meets predetermined requirements; and the dispatched commodity categories of dedicated line routes for each shared delivery project are not mutually exclusive.

[0019] Based on the scheduling method for logistics delivery vehicles with the above configuration, shared delivery can be achieved from the perspective of customized dedicated lines. This is achieved by effectively identifying projects that can share delivery vehicles based on three dimensions: the aggregated business volume of dedicated lines for each shared delivery project exceeds a predetermined threshold; the overlap in dispatch frequency of dedicated lines for each project within the predetermined time period exceeds a predetermined value (business scale); the capacity of dedicated lines for each shared delivery project meets predetermined requirements (dedicated line capacity satisfaction); and the categories of goods shipped on dedicated lines for each shared delivery project are not mutually exclusive (project shipment categories). This enables shared delivery of routes during dedicated line delivery, and real-time recommendations of delivery vehicles for pre-determined wave delivery orders improve vehicle utilization and ensure timeliness.

[0020] Optionally, in the scheduling method according to the first aspect of the present invention, for each shared allocation project, calculating the target vehicle use plan that best matches the preset target includes: obtaining the number of shared allocation route combinations for each shared allocation project; when the number of shared allocation route combinations is 1, calculating the matching degree between the original vehicle use plan and the vehicle use plan implemented by the shared allocation route combinations for the shared allocation project and the preset target, and taking the one that best matches the preset target as the target vehicle use plan; and when the number of shared allocation route combinations is greater than 1, calculating the matching degree between the original vehicle use plan and the vehicle use plan implemented by each shared allocation route combination for the shared allocation project and the preset target, and taking the one that best matches the preset target as the target vehicle use plan.

[0021] Based on the scheduling method of logistics delivery vehicles with the above configuration, it is possible to calculate various common route combinations for each delivery project, thereby selecting the optimal target vehicle plan and optimizing the scheduling.

[0022] Optionally, in the scheduling method of the first aspect of the present invention, calculating the degree of matching between the vehicle use plan implemented by each shared route combination of the shared allocation project and the preset target includes: searching all vehicle use plans of the shared route combinations in the current wave, using preset parameters as constraints, calculating each vehicle use plan, and obtaining the target vehicle use plan that best matches the preset target.

[0023] Based on the scheduling method for logistics delivery vehicles with the above configuration, by searching all shared route combinations for the current wave and calculating with preset parameters as constraints, the influence of human intervention and subjective judgment can be reduced, the accuracy and objectivity of decision-making can be enhanced, and the vehicle scheduling that best meets the preset goals can be found, thereby optimizing vehicle scheduling and improving vehicle utilization.

[0024] Optionally, in the scheduling method of the first aspect of the present invention, vehicle scheduling is performed according to the target vehicle usage plan, and vehicle usage information of delivery vehicles for each shared distribution project is output, including: using a first parameter as an input parameter and a second parameter as a constraint condition, outputting at least one of the following in the description of the delivery order of the shared distribution project: the carrier of the delivery vehicle, the delivery route, the delivery vehicle type, and the delivery number.

[0025] Based on the scheduling method for logistics delivery vehicles with the above configuration, vehicle scheduling can be achieved by considering at least one of the following: carrier, delivery route, vehicle type, and delivery frequency. This allows for better planning of vehicle scheduling and transportation routes, improving logistics efficiency. Furthermore, customers can more clearly understand the delivery status and progress of their orders, track delivery details, and easily check delivery information.

[0026] To achieve the above objectives, according to another aspect of the present invention, a dispatching device for logistics delivery vehicles is provided, comprising:

[0027] Information receiving unit: Receives delivery orders for each project and obtains delivery information for each order.

[0028] Route discovery unit: Based on the delivery information of the delivery orders, it obtains the shared delivery route combinations of shared delivery projects that can share delivery vehicles among the projects.

[0029] Route calculation unit: For each shared allocation project, calculates the target vehicle usage plan that best matches the preset target, and

[0030] Output unit: Based on the target vehicle usage plan, vehicle scheduling is performed, and the vehicle usage information of delivery vehicles for each shared distribution project is output.

[0031] To achieve the above objectives, according to another aspect of the present invention, an electronic device for scheduling logistics delivery vehicles is provided, comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the scheduling method described in one aspect.

[0032] To achieve the above objectives, according to another aspect of the present invention, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the scheduling method described in one aspect.

[0033] One embodiment of the above invention has the following advantages or beneficial effects: Because it employs the technical means of receiving delivery orders for each project and obtaining delivery information for each order, obtaining shared delivery route combinations for shared delivery projects that can share delivery vehicles based on the delivery information of the orders, calculating the target vehicle usage plan that best matches the preset target for each shared delivery project, and outputting the vehicle usage information of the delivery vehicles for the target vehicle usage plan, it overcomes the technical problem of not being able to balance cost and timeliness. This achieves an optimal balance between cost and timeliness. By identifying orders that can be shared for delivery, it optimally matches carriers for multiple projects, enabling shared delivery of multiple projects on a single route, thereby reducing the average cost per route, achieving low cost and high timeliness, fully utilizing resources, and improving overall transportation efficiency.

[0034] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0035] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein:

[0036] Figure 1 This is a schematic diagram of the main flow of a logistics delivery vehicle scheduling method according to an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram illustrating the configuration of shared delivery route combinations for shared delivery projects that can share delivery vehicles in the scheduling method of logistics delivery vehicles;

[0038] Figure 3 This is a schematic diagram illustrating a specific process for scheduling logistics and delivery vehicles.

[0039] Figure 4 This is a schematic diagram of the main unit of a logistics delivery vehicle dispatching device according to an embodiment of the present invention;

[0040] Figure 5 This is an exemplary system architecture diagram in which embodiments of the present invention can be applied;

[0041] Figure 6 This is a schematic diagram of the structure of a computer system suitable for implementing terminal devices or servers of the present invention. Detailed Implementation

[0042] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0043] Figure 1 This is a schematic diagram of the main flow of a logistics delivery vehicle scheduling method according to an embodiment of the present invention, as shown below. Figure 1 As shown, it includes steps S101 to S104.

[0044] In step S101, delivery orders for each project are received and delivery information for each delivery order is obtained.

[0045] More specifically, in step S101, delivery orders from various projects are received, and key delivery information is obtained from them, such as the distance to delivery stations for each project, last-mile delivery requirements, and business attributes. Business attributes include, but are not limited to, last-mile scenarios, customer types, shipping needs, and product categories. This step effectively acquires key data for subsequent scheduling optimization.

[0046] In step S102, based on the delivery information of the delivery orders, the shared delivery route combinations of the shared delivery projects that can share delivery vehicles are obtained from each project. According to step S102, the vehicle scheduling of delivery orders for each project can be optimized, and shared projects that can share vehicles for shared delivery can be identified. This allows the delivery routes of shared projects to be configured as shared delivery route combinations, thereby balancing vehicle usage costs and timeliness, making full use of resources, and improving overall transportation efficiency.

[0047] More specifically, see Figure 2 This illustrates a schematic diagram of the configuration for obtaining shared delivery route combinations of shared delivery projects that can share delivery vehicles in a logistics delivery vehicle scheduling method. For example... Figure 2 As shown, in this embodiment, the mining of shared distribution route combinations can be carried out from three independent aspects: forward and reverse route mining, same-city shared distribution route mining, and dedicated route mining.

[0048] In forward and reverse route mining, it is possible to obtain common route combinations of common matching projects that match forward and reverse routes. Forward and reverse route matching is as follows: Figure 2As shown, shared delivery can be achieved from both forward and reverse delivery perspectives. When scheduling delivery vehicles for delivery orders, the system can effectively identify projects that can share delivery vehicles by comprehensively evaluating at least five dimensions: route matching overlap rate (forward and reverse city matching), shipping demand (shipping frequency, vehicle type matching), last-mile delivery scenario (warehouse, store, supermarket, etc. matching), multi-item shipping categories (industry attributes), and customized KPIs (last-mile delivery requirement matching). This enables shared delivery of forward and reverse routes for nationwide delivery, and real-time recommendations of delivery vehicles for pre-ordered waves of orders, improving vehicle utilization and ensuring timeliness.

[0049] More specifically, the forward and reverse route matching includes route matching overlap rate, that is, the originating city of the forward shipping route matches the destination city of the reverse shipping route, and the destination city of the forward shipping route matches the originating city of the reverse shipping route. In this embodiment, "matching" refers not only to a perfect match but also to a basic match. For example, the originating city A-destination city B of the forward shipment is matched one by one with the originating city B*-destination city A* of the reverse shipment; a perfect match indicates a compatible forward and reverse route. Furthermore, for the destination city B, matching the originating city B* of the reverse shipment can expand the sourcing radius to 100KM (this is merely a non-limiting example and can be selected according to actual conditions), meaning that city clusters such as B*, C*, and D* can be picked up and shipped from points within a 100km radius. Similarly, the destination city of the reverse shipment can also expand the sourcing radius.

[0050] Furthermore, in this embodiment, when performing forward and reverse route matching, the AOI (Area of ​​Interest) in historical delivery order data can be mined. Combining the distribution information of goods volume and geographical data from historical delivery orders, a hierarchical clustering method, such as Meanshift clustering, is used to divide each administrative region within a city into multiple AOIs. Additionally, the shipping city and destination city are configured as specific AOIs for each city. Therefore, both the geographical division of areas and the controllable volume of goods within each category can be considered, making route matching more accurate and efficient. By mining the AOI information in historical order data, it is possible to identify which areas are of interest to users—that is, areas with a large number of orders—providing a better understanding of the distribution of goods and transportation needs, thus offering a more accurate reference for forward and reverse route matching.

[0051] Matching forward and reverse routes also includes matching shipping demand, meaning that the overlap rate of shipping frequencies within a predetermined time period for both forward and reverse routes exceeds a specific value, and the overlap rate of shipping vehicle types also exceeds a specific value. For example, for shipping demand, in matched forward and reverse routes, the overlap rate of monthly shipping frequencies (in unrestricted instances) for multiple projects must be greater than 90% (in unrestricted instances), and the overlap rate of shipping vehicle types must also be greater than 90% (in unrestricted instances, and this overlap rate can be different from the frequency overlap rate). Using this dimension, if the shipping frequencies of multiple projects are very similar, the shipping vehicle types should also be very similar. This allows for reducing transportation costs and improving transportation efficiency through vehicle sharing and increased vehicle utilization. For example, for forward shipping from Shanghai to Beijing, Projects 1 through 3 make 50 shipments per month, all using large trucks. Similarly, for reverse shipping from Beijing to Shanghai, Projects 1* through 3* make 45-50 shipments per month, with 90% of the vehicles being large trucks, thus meeting the requirements.

[0052] Forward and reverse route matching also includes shipping demand matching, that is, matching the last-mile delivery scenarios of forward routes with those of reverse routes. For example, in matched forward and reverse routes, multiple projects' last-mile fulfillment scenarios are matched separately according to delivery to Beijing warehouses, non-Beijing warehouses, supermarkets, and stores, etc. Point-to-point full-truckload transportation in the same scenario is prioritized for integration. For forward and reverse point-to-point pickup and delivery, the destination city must be within 100 kilometers for point-to-point delivery fulfillment. Accordingly, the last-mile fulfillment needs of different projects can be better met, improving the efficiency and accuracy of logistics transportation. In terms of distance, there are many routes between Beijing and Shanghai, and between Shanghai and Beijing, that meet the route matching overlap rate and shipping demand. Therefore, goods on routes such as Beijing-Shanghai-supermarket delivery and Shanghai-Beijing-supermarket delivery will be prioritized for transportation on a single vehicle.

[0053] Forward and reverse route matching also includes matching of multiple shipping categories (industry attributes), meaning that the shipping categories of the forward route and the shipping categories of the reverse route are not mutually exclusive. For example, if the forward route involves shipping clothing and the reverse route involves shipping shoes, these two categories are not mutually exclusive and can therefore be loaded onto the same vehicle for transportation. In contrast, if the reverse route involves shipping fresh produce, these two categories are mutually exclusive and cannot share a vehicle for transportation. Based on this dimension, vehicle utilization can be improved, empty-running rates and warehouse storage space occupancy can be reduced, while also lowering logistics and distribution costs and improving efficiency.

[0054] Matching forward and reverse routes also includes matching customized KPIs (Key Performance Indicators), that is, matching the end-of-line delivery requirements of the forward route with the end-of-line delivery requirements of the reverse route. Delivery requirements may include whether delivery to the upper floor is required, whether the customer has signed for and returned the order, whether on-site installation is required, etc. Based on this dimension, customized needs can be effectively met.

[0055] The following explains how to discover shared distribution lines within the same city.

[0056] In the process of mining shared distribution routes within the same city, it is possible to obtain shared distribution route combinations for shared distribution projects that match within the same city. Same-city route matching includes... Figure 2 As shown, shared delivery can be achieved from the perspective of same-city delivery. When scheduling delivery vehicles for delivery orders, it can effectively identify projects that can share delivery vehicles by comprehensively evaluating at least four dimensions: pickup, delivery, multiple shipment categories (industry attributes), and customized KPIs (matching last-mile delivery requirements). This enables shared delivery of same-city routes during same-city delivery, and real-time recommendations of delivery vehicles for pre-ordered delivery waves, improving vehicle utilization and ensuring timeliness.

[0057] More specifically, the same-city route matching includes pickup matching, that is, the pickup stations of the same-city routes for each co-distribution project are compatible. In this embodiment, pickup station compatibility refers to the compatibility of pickup behaviors occurring at the pickup stations, such as: unified pickup from warehouses within the same industrial park / random pickup from locations within 10 kilometers across industrial parks (a non-limiting example); similar or overlapping pickup times; and non-conflicting loading and protection requirements, etc. The same-city route matching also includes delivery matching, that is, the delivery stations of the same-city routes for each co-distribution project are compatible. Delivery station compatibility refers to the compatibility of delivery behaviors occurring at the delivery stations, such as: largely overlapping fulfillment addresses, consistent delivery times, and similar value-added service requirements at the last mile, etc. The same-city route matching also includes project shipment category matching and customer fulfillment KPI matching. These two dimensions are similar to the previous forward and reverse route matching and will not be elaborated here.

[0058] The following explains how to excavate a special line.

[0059] In dedicated line mining, it is possible to obtain shared line combinations for shared allocation projects that match dedicated lines. Dedicated line matching is as follows: Figure 2 As shown, shared delivery can be achieved from the perspective of customized dedicated lines. When scheduling delivery vehicles for delivery orders, the system can effectively identify projects that can share delivery vehicles by comprehensively evaluating at least three dimensions: the scale of the route business, the availability of dedicated line capacity, and the types of goods shipped (industry attributes). This enables shared delivery of dedicated lines and allows for real-time recommendations of delivery vehicles for pre-ordered delivery waves, improving vehicle utilization and ensuring timeliness.

[0060] More specifically, the dedicated route matching includes business scale matching, meaning that the aggregated business volume of dedicated routes for each shared distribution project is greater than a predetermined threshold, and the overlap of shipping frequencies for dedicated routes within a predetermined time period for each project is greater than a predetermined value. For example, for any forward route, in terms of flow scale, the aggregated business volume of multiple projects split by route is at least 30 cubic meters per day; in terms of shipping frequency, the monthly shipping frequency overlap of multiple projects in the split routes is greater than 90% (the values ​​here are non-limiting examples). For instance, the Beijing-Shanghai route might be split into Beijing-Shanghai-IKEA-30 cubic meters and Beijing-Shanghai-IKEA-60 cubic meters, i.e., split according to project and order volume.

[0061] The dedicated route matching also includes dedicated line capacity fulfillment matching, meaning that the capacity of the dedicated lines for each shared distribution project meets the predetermined requirements. For example, for all routes split into multiple projects, there are dedicated lines available in the logistics park's dedicated line market, meaning there cannot be any routes where dedicated line capacity cannot be guaranteed; the standard delivery time of the dedicated lines basically meets the requirements of the project merchants, mainly considering the matching degree between the standard delivery time and the delivery time required by the project merchants; the commonly used vehicle types of the dedicated lines basically meet the requirements of the project merchants, meaning that projects that cannot be guaranteed by dedicated vehicle types; considering delivery time and pickup costs, the distance between the dedicated line park and the customer's shipping warehouse / pickup location is generally required to be within 100km (non-restrictive example).

[0062] The dedicated route matching also includes multi-item shipment category matching, meaning that the categories of shipped goods are not mutually exclusive. Similar to the two mining methods mentioned above, considering the operational capabilities of dedicated lines and industry concentration effects, it is generally required that projects from the same industry (FMCG, 3C, apparel, automobiles) be shipped in the same vehicle with multiple categories mixed together.

[0063] Having obtained the shared delivery route combinations for shared delivery projects through step S102, step S103 can be performed to calculate the target vehicle usage plan that best matches the preset target for each shared delivery project.

[0064] Using step S103, the target vehicle plan that best matches the preset goal, such as the lowest cost, can be found from all vehicle plans of each project (including the original vehicle plan and the vehicle plan of the shared route combination).

[0065] For example, calculating the target vehicle usage plan that best matches the preset target for each shared allocation project may include: obtaining the number of shared allocation route combinations for each shared allocation project; when the number of shared allocation route combinations is 1, calculating the matching degree between the original vehicle usage plan and the vehicle usage plan implemented by the shared allocation route combination for the shared allocation project and the preset target, and taking the one that best matches the preset target as the target vehicle usage plan; and when the number of shared allocation route combinations is greater than 1, calculating the matching degree between the original vehicle usage plan and the vehicle usage plan implemented by each shared allocation route combination for the shared allocation project and the preset target, and taking the one that best matches the preset target as the target vehicle usage plan.

[0066] For example, calculating the degree of matching between the vehicle usage plan implemented by each shared route combination of the shared allocation project and the preset target may include: searching for vehicle usage plans of all shared route combinations in the current wave; and using preset parameters as constraints, calculating each vehicle usage plan to obtain the target vehicle usage plan that best matches the preset target.

[0067] Based on the scheduling method for logistics delivery vehicles with the above configuration, various shared route combinations for each delivery project can be calculated, thereby selecting the optimal target vehicle usage plan and optimizing scheduling. By searching for vehicle usage plans for all shared route combinations in the current wave and calculating them with preset parameters as constraints, the influence of human intervention and subjective judgment can be reduced, enhancing the accuracy and objectivity of decision-making. The vehicle usage plan that best meets the preset goals can be found, thereby optimizing vehicle scheduling, improving vehicle utilization, fully utilizing resources, and improving overall transportation efficiency.

[0068] Figure 3 The vehicle usage plan decision section illustrates a schematic process example of step S103 above. As shown in the figure, after receiving the delivery orders for each item of the day ( Figure 3 Taking three project orders as an example, after obtaining the delivery information for each delivery order (such as shipping requirements, available vehicles for the route, payment methods for the route, etc.), the method in step S102 can be used to mine routes, thereby identifying shared delivery route combinations for shared delivery projects that can share vehicles. Figure 3In this example, assuming Project 1 and Project 3 meet the requirements for same-city route compatibility, Project 1 and Project 3 become target projects, and the route of Project 1 and the route of Project 2 can be combined as shared routes. According to step S103, if Project 1, 3 and Project 2 cannot form a shared route, then the number of shared route combinations is 1. The matching degree between the original vehicle usage plan and the vehicle usage plan implemented by the shared route combination of the shared projects 1 and 3 and the preset target, such as the cost-optimal one, is calculated, and the cost-optimal one is recommended as the target vehicle usage plan. On the other hand, if in this example, Project 1 and Project 2 also meet the requirements for dedicated line shared allocation, then the number of shared route combinations for target Project 1 is 2. These two shared route combinations are judged, and the vehicle usage plans of all shared route combinations in the current wave are searched; and with preset parameters (timeliness, vehicle volume, vehicle weight, etc.) as constraints, various vehicle usage plans of these two shared route combinations are calculated, and compared with the original vehicle usage plan, the target vehicle usage plan with the lowest cost is found and recommended. This allows for real-time recommendations of routes that need to be shipped that day.

[0069] In step S104, vehicle scheduling is performed according to the target vehicle usage plan, and the vehicle usage information of delivery vehicles for each shared delivery project is output. The vehicle scheduling based on the target vehicle usage plan, and the output of the delivery vehicle usage information for each shared delivery project, may include: using a first parameter as input and a second parameter as constraint, outputting at least one of the following: the carrier of the delivery vehicle, the delivery route, the vehicle type, and the delivery number. Therefore, based on input parameters such as order volume and vehicle pool, and constraints such as order time windows and delivery requirements, a vehicle type and delivery number recommendation scheme that balances cost and timeliness to meet the same day's shipping needs can be output.

[0070] Vehicle dispatching can include, for example, obtaining basic dispatching information such as order information (order number, goods, customer address, etc.), delivery personnel information (name, contact information, etc.), billing information (freight, insurance, etc.), carrier information (company name, contact information, etc.), and routing information (route, road conditions, etc.); performing pre-validation, such as filtering, validating, and filling attributes of the input parameters of the first parameter (order volume, vehicle pool, etc.), splitting and combining orders, and copying vehicles; constructing an initial solution: processing price data (carrier procurement and billing information), scoring carriers, allocating orders, and planning routes; solving vehicle route planning problems, such as using the second parameter (volume, weight, location, time, distance, delivery area) as constraints, with the goal of optimizing global cost, and the shortest distance and time as step-wise objectives; and outputting order information for co-delivery projects, carrier vehicle information (carrier, delivery vehicle type, delivery number), delivery routes (route planning information, delivery point information), etc.

[0071] Based on the scheduling method for logistics delivery vehicles with the above configuration, vehicle scheduling can be achieved by considering at least one of the following: carrier, delivery route, vehicle type, and delivery frequency. This allows for better planning of vehicle scheduling and transportation routes, improving logistics efficiency. Furthermore, customers can more clearly understand the delivery status and progress of their orders, track delivery details, and easily check delivery information.

[0072] The above-described method for scheduling logistics delivery vehicles according to an embodiment of the present invention provides an optimal balance between preset objectives such as cost and timeliness. By identifying orders that can be jointly delivered, and optimally matching carriers for multiple items, the method enables joint delivery of multiple items on a single route, thereby reducing the average cost per route, achieving low cost and high timeliness, fully utilizing resources, and improving overall transportation efficiency.

[0073] Figure 4 This is a schematic diagram of the main units of a logistics delivery vehicle dispatching device 400 according to an embodiment of the present invention, including: an information receiving unit 401: receiving delivery orders for each project and obtaining delivery information for each delivery order; a route mining unit 402: based on the delivery information of the delivery orders, obtaining the common route combinations of common delivery projects that can share delivery vehicles among the projects; a route calculation unit 403: for each common delivery project, calculating the target vehicle usage plan that best matches the preset target; and an output unit 403: performing vehicle dispatching according to the target vehicle usage plan and outputting the vehicle usage information of delivery vehicles for each common delivery project.

[0074] The logistics delivery vehicle scheduling device 400 according to the embodiments of the present invention can achieve the same technical effect as the logistics delivery vehicle scheduling method according to the embodiments of the present invention.

[0075] Figure 5 An exemplary system architecture 500 is shown, which can be applied to the scheduling method or device for logistics delivery vehicles according to embodiments of the present invention.

[0076] like Figure 5 As shown, system architecture 500 may include terminal devices 501, 502, and 503, a network 504, and a server 505. Network 504 serves as the medium for providing communication links between terminal devices 501, 502, and 503 and server 505. Network 504 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0077] Users can use terminal devices 501, 502, and 503 to interact with server 505 via network 504 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 501, 502, and 503, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0078] Terminal devices 501, 502, and 503 can be various electronic devices with displays that support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0079] Server 505 can be a server that provides various services, such as a backend management server that supports shopping websites browsed by users using terminal devices 501, 502, and 503 (for example only). The backend management server can analyze and process data such as received product information query requests, and feed back the processing results (such as target push information, product information - for example only) to the terminal device.

[0080] It should be noted that the logistics delivery vehicle scheduling method provided in this embodiment of the invention is generally executed by server 505, and correspondingly, the logistics delivery vehicle scheduling device is generally set in server 505.

[0081] It should be understood that Figure 5 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0082] The following is for reference. Figure 6 It shows a schematic diagram of the structure of a computer system 600 suitable for implementing a terminal device of the present invention. Figure 6 The terminal device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0083] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the system 600. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0084] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0085] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs the functions defined above in the system of this invention.

[0086] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a 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, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0087] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0088] The units described in the embodiments of the present invention can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor can be described as including an information receiving unit, a line mining unit, a line calculation unit, and an output unit. The names of these units do not necessarily limit the specific unit; for example, the information receiving unit can also be described as "a unit that receives delivery orders for various items and obtains delivery information for each delivery order."

[0089] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs that, when executed by the device, cause the device to include: receiving delivery orders for each item and obtaining delivery information for each delivery order; based on the delivery information of the delivery orders, obtaining shared delivery route combinations for shared delivery items among the items that can share delivery vehicles; for each shared delivery item, calculating a target vehicle usage plan that best matches a preset target; and scheduling vehicles according to the target vehicle usage plan, outputting vehicle usage information for each shared delivery item's delivery vehicles.

[0090] According to the technical solutions of the present invention, an optimal balance can be achieved between preset goals such as cost and timeliness. By mining orders that can be jointly delivered, carriers for multiple items are optimally matched to achieve joint delivery of multiple items on a single route, thereby reducing the average cost per route, achieving low cost and high timeliness, making full use of resources, and improving overall transportation efficiency.

[0091] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for scheduling logistics delivery vehicles, characterized in that, include: Receive delivery orders for each project and obtain delivery information for each order. Based on the delivery information of the delivery orders, obtain the shared delivery route combinations for shared delivery projects that can share delivery vehicles among the projects. For each shared allocation project, calculate the target vehicle usage plan that best matches the preset target, and Based on the target vehicle usage plan, vehicle scheduling is performed, and vehicle usage information for delivery vehicles of each shared distribution project is output.

2. The scheduling method according to claim 1, characterized in that, Obtaining shared delivery route combinations for shared delivery projects that can share delivery vehicles includes: obtaining shared delivery route combinations for shared delivery projects with forward and reverse route matching, wherein the forward and reverse route matching includes: The shipping city of the forward shipping route is matched with the destination city of the reverse shipping route, and the destination city of the forward shipping route is matched with the shipping city of the reverse shipping route. The overlap rate of the shipping frequency of the forward route and the reverse route within a predetermined time period exceeds a certain value, and the overlap rate of the shipping vehicle type exceeds a certain value. The last-mile delivery scenario of the forward route is matched with the last-mile delivery scenario of the reverse route; The product categories shipped on the forward route are not mutually exclusive with those shipped on the reverse route; and The end-of-line delivery requirements of the forward line match the end-of-line delivery requirements of the reverse line.

3. The scheduling method according to claim 2, characterized in that, Acquiring shared delivery route combinations for shared delivery projects that can share delivery vehicles also includes: By mining interest surfaces from historical delivery order data and combining information on the distribution of goods and geographic data in historical delivery orders, a hierarchical clustering method is used to divide the various administrative districts within the city into multiple interest surfaces; and Configure the shipping city as a specific interest surface of that city, and configure the destination city as a specific interest surface of that city.

4. The scheduling method according to claim 1, characterized in that, Obtaining shared delivery route combinations for shared delivery projects that can share delivery vehicles includes: obtaining shared delivery route combinations for shared delivery projects with matching routes within the same city, wherein the matching of routes within the same city includes: The pickup station behavior of each shared distribution project's same-city routes is compatible; The delivery station behavior of the same-city routes of each shared distribution project is compatible; The product categories shipped on the same-city routes of the various shared delivery projects are not mutually exclusive; and The end-point delivery requirements of the same-city routes for each shared distribution project are matched.

5. The scheduling method according to claim 1, characterized in that, Obtaining shared delivery route combinations for shared delivery projects that can share delivery vehicles includes: obtaining shared delivery route combinations for shared delivery projects that match dedicated line routes, wherein the dedicated line route matching includes: The total volume of dedicated line services for each shared distribution project is greater than the predetermined threshold, and the overlap of shipping frequency for dedicated line services within the predetermined time period for each project is greater than the predetermined value. The dedicated line capacity for each shared distribution project meets the pre-determined requirements; and The categories of goods shipped on dedicated lines for each shared distribution project are not mutually exclusive.

6. The scheduling method according to any one of claims 2-5, characterized in that, For each shared allocation project, calculate the target vehicle usage plan that best matches the preset target, including: Obtain the number of shared distribution line combinations for each shared distribution project; When the number of shared route combinations is 1, calculate the matching degree between the original vehicle usage plan and the vehicle usage plan implemented by the shared route combination of the shared project and the preset target, and take the one that best matches the preset target as the target vehicle usage plan; and When the number of shared route combinations is greater than 1, the degree of matching between the original vehicle use plan and the vehicle use plan implemented by each shared route combination of the shared project and the preset target is calculated respectively, and the one that matches the preset target the most is taken as the target vehicle use plan.

7. The scheduling method according to claim 6, characterized in that, Calculate the degree of matching between the vehicle usage plan implemented by each shared route combination of the shared allocation project and the preset target, including: Search for all shared route combinations and vehicle booking options for the current wave; and Using preset parameters as constraints, each of the aforementioned vehicle usage plans is calculated to obtain the target vehicle usage plan that best matches the preset target.

8. The scheduling method according to claim 7, characterized in that, Based on the target vehicle usage plan, vehicle scheduling is performed, and the usage information of delivery vehicles for each shared distribution project is output, including: Using the first parameter as input and the second parameter as constraint, output at least one of the following in the description of the delivery order for the shared delivery project: the carrier of the delivery vehicle, the delivery route, the delivery vehicle type, and the delivery number.

9. A dispatching device for logistics delivery vehicles, characterized in that, include: Information receiving unit: Receives delivery orders for each project and obtains delivery information for each order. Route discovery unit: Based on the delivery information of the delivery orders, it obtains the shared delivery route combinations of shared delivery projects that can share delivery vehicles among the projects. Route calculation unit: For each shared allocation project, calculates the target vehicle usage plan that best matches the preset target, and Output unit: Based on the target vehicle usage plan, vehicle scheduling is performed, and the vehicle usage information of delivery vehicles for each shared distribution project is output.

10. An electronic device for dispatching logistics delivery vehicles, characterized in that, include: One or more processors; Storage device 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 method as described in any one of claims 1-8.

11. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-8.

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