Freight delivery method and device and storage medium

Abstract

The invention provides a freight delivery method and device and a storage medium. The method comprises the steps of selecting a zero-load freight order according to an order selection rule; generating a virtual order based on the zero-load freight order; constructing a virtual vehicle, and pre-distributing all virtual orders to the virtual vehicle; according to the distribution optimization objective function and the constraint condition of the distribution optimization objective function, distributing the virtual order to the real vehicle; and according to the distribution result of the virtual order, obtaining a distribution scheme for the zero-load freight order. According to the invention, distribution processing can be automatically carried out on the zero-load freight order, the optimized distribution scheme is obtained, the freight efficiency of zero-load logistics is effectively improved, the freight cost of a user can be reduced, and the use experience of the user is improved; and the utilization rate of vehicle resources can be increased, the transportation cost is reduced, and the transportation efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of freight logistics, and in particular to a freight distribution method, device and storage medium. Background Art

[0002] At present, there is a large demand for LTL freight transportation in the logistics and freight industry. LTL freight means that users can entrust the goods to the carrier in the form of LTL, determine the weight and volume of the LTL freight, and designate the carrier to transport to the corresponding destination. The transportation cost of LTL freight orders is usually charged by volume or weight. Since the cost of full truckload settlement is lower than that of LTL settlement, multiple LTL freight orders arriving at the same or adjacent areas can be combined for delivery, assigned to one transport vehicle for delivery and full truckload settlement, or, for a LTL freight order with a large freight volume entrusted by the user, it can be split and assigned to multiple transport vehicles for full truckload delivery, which can save transportation costs and transportation resources. At present, LTL freight orders rely on manual allocation. It is difficult to split, merge and distribute LTL freight orders through manual allocation, which is difficult to reduce users' freight costs, and the user experience is not high. In addition, there is a situation where the transport vehicle is not fully loaded, the transportation cost is high, the transportation efficiency is low, and resources are wasted. Summary of the invention

[0003] In view of this, a technical problem to be solved by the present invention is to provide a freight delivery method, device and storage medium.

[0004] According to a first aspect of the present disclosure, a freight delivery method is provided, comprising: selecting an LTL freight order according to an order selection rule; generating a virtual order based on the LTL freight order; wherein the virtual order comprises: at least one of an LTL freight order that is not allowed to be split and a split sub-order of an LTL freight order that is allowed to be split; constructing a virtual vehicle, and pre-assigning all of the virtual orders to the virtual vehicle; assigning the virtual orders to real vehicles according to a delivery optimization objective function and constraints of the delivery optimization objective function; and obtaining a delivery plan for the LTL freight order according to the allocation result of the virtual order.

[0005] Optionally, allocating the virtual order to the real vehicle according to the delivery optimization objective function and the constraints of the delivery optimization objective function includes: determining the value of the delivery optimization objective function; allocating the virtual order to the real vehicle according to the constraints and using a delivery optimization algorithm with the goal of minimizing the value of the delivery optimization objective function.

[0006] Optionally, determining the value of the distribution optimization objective function includes: determining the total transportation cost of the virtual order, the total fixed cost of using the real vehicle, and the penalty cost of allocating items in the LTL freight order to different vehicles; and determining the value of the distribution optimization objective function based on the total transportation cost, the total fixed cost and the penalty cost.

[0007] Optionally, determining the total transportation cost of the virtual order includes: calculating the transportation cost of the virtual order according to the billing rules and attribute information of the virtual order; wherein, the full vehicle billing rule is adopted for the virtual order assigned to the real vehicle, and the less-than-truckload billing rule is adopted for the virtual order assigned to the virtual vehicle; the attribute information includes: at least one of the transportation address, merchant, and vehicle model; and determining the total transportation cost based on the transportation costs of all the virtual orders.

[0008] Optionally, the distribution optimization algorithm includes: a metaheuristic algorithm; taking minimizing the value of the distribution optimization objective function as the goal, allocating the virtual order to the real vehicle according to the constraints and using the distribution optimization algorithm includes: performing iterative allocation processing on the virtual order according to the constraints and using the metaheuristic algorithm to allocate the virtual order to the real vehicle; wherein, if the value of the distribution optimization objective function after an iterative allocation processing is less than or equal to the value of the distribution optimization objective function before this iterative allocation processing, the result of this iterative allocation processing is accepted; when the number of iterative allocation processing reaches a number threshold or the execution time of the iterative allocation processing exceeds a time threshold, the iterative allocation processing of the virtual order is terminated.

[0009] Optionally, the constraints include: the virtual order is assigned to only one vehicle, a billing method is assigned to each of the virtual vehicle and the real vehicle, a volume limit of the real vehicle, and a weight limit of the real vehicle.

[0010] Optionally, obtaining a distribution plan for the LTL freight order according to the allocation result of the virtual order includes: obtaining a splitting result of the LTL freight order based on the allocation result of the virtual order; generating a distribution plan for the LTL freight order according to the allocation result and the splitting result.

[0011] Optionally, obtaining the splitting result of the LTL freight order based on the allocation result of the virtual order includes: aggregating the split sub-orders assigned to the virtual vehicle or the same real vehicle and belonging to the same LTL freight order to generate an aggregated order; comparing the aggregated order with the order data of the same LTL freight order, and determining whether the LTL freight order is split based on the comparison result; wherein the order data includes: at least one of the quantity, volume, and weight of packages.

[0012] Optionally, generating a delivery plan for the LTL freight order based on the allocation result and the order splitting result includes: generating an LTL delivery plan based on the aggregated orders assigned to the virtual vehicle and the information on whether they are split, and / or the LTL freight orders assigned to the virtual vehicle that are not allowed to be split; generating a full truckload delivery plan based on the aggregated orders assigned to the real vehicle and the information on whether they are split, and / or the LTL freight orders assigned to the real vehicle that are not allowed to be split.

[0013] Optionally, generating a virtual order based on the LTL freight order includes: splitting the LTL freight order that is allowed to be split based on the order splitting granularity information to obtain the split sub-order; and using the split sub-order and the LTL freight order that is not allowed to be split as the virtual order.

[0014] Optionally, the order selection rules include: the order time of the LTL freight order is within the same time period, and the address of the LTL freight order belongs to the same area.

[0015] According to a second aspect of the present disclosure, there is provided a freight distribution device, comprising: a selection module for selecting a LTL freight order according to an order selection rule; a splitting module for generating a virtual order based on the LTL freight order; wherein the virtual order comprises: at least one of a LTL freight order that is not allowed to be split and a split sub-order of a LTL freight order that is allowed to be split; a pre-allocation module for constructing a virtual vehicle and pre-allocating all the virtual orders to the virtual vehicle; an optimization allocation module for allocating the virtual orders to real vehicles according to a distribution optimization objective function and constraints of the distribution optimization objective function; and a plan generation module for obtaining a distribution plan for the LTL freight order according to the distribution result of the virtual order.

[0016] According to a third aspect of the present disclosure, there is provided a freight delivery device, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute the method as described above based on instructions stored in the memory.

[0017] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the instructions are executed by a processor as described above.

[0018] The freight delivery method, device and storage medium disclosed in the present invention select LTL freight orders and generate virtual orders, pre-assign all virtual orders to virtual vehicles, and assign virtual orders to real vehicles according to the delivery optimization objective function and constraints, so as to obtain delivery plans for LTL freight orders; it can automatically perform delivery processing on LTL freight orders, obtain optimized delivery plans, effectively improve the freight efficiency of LTL logistics, reduce users' freight costs, and improve users' usage experience; and it can improve the utilization rate of vehicle resources, reduce transportation costs, and improve transportation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0020] Figure 1 is a flow chart of an embodiment of a freight delivery method according to the present disclosure;

[0021] Figure 2 A schematic diagram of a process of allocating a virtual order to a real vehicle in one embodiment of a freight delivery method according to the present disclosure;

[0022] Figure 3 It is a schematic diagram of a process of determining whether a LTL freight order is a split order in one embodiment of the freight delivery method disclosed herein;

[0023] Figure 4 is a module schematic diagram of an embodiment of a freight delivery device according to the present disclosure;

[0024] Figure 5 It is a module schematic diagram of an optimization allocation module in one embodiment of a freight distribution device according to the present disclosure;

[0025] Figure 6 A schematic diagram of a solution generation module in an embodiment of a freight delivery device according to the present disclosure;

[0026] Figure 7 It is a module schematic diagram of another embodiment of a freight delivery device according to the present disclosure. DETAILED DESCRIPTION

[0027] Exemplary embodiments of the present disclosure will be described below in conjunction with the accompanying drawings. For the sake of clarity and conciseness, not all features of the embodiments are described in the specification. However, it should be understood that many implementation-specific settings must be made in the process of implementing the embodiments in order to achieve the specific goals of the developer, for example, to meet those restrictions related to the device and the service, and these restrictions may vary depending on the implementation. In addition, it should be understood that although the development work may be very complex and time-consuming, it is only a routine task for those skilled in the art who benefit from the content of this disclosure.

[0028] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

[0029] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0030] It should also be understood that in the embodiments of the present disclosure, “plurality” may refer to two or more than two, and “at least one” may refer to one, two, or more than two.

[0031] It should also be understood that any component, data or structure mentioned in the embodiments of the present disclosure can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0032] In addition, the term "and / or" in the present disclosure is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present disclosure generally indicates that the associated objects before and after are in an "or" relationship.

[0033] It should also be understood that the description of the various embodiments in the present disclosure focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0034] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0035] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0036] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0037] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0038] In addition, in order to avoid obscuring the present disclosure due to unnecessary details, only the processing steps and / or device structures closely related to at least the scheme according to the present disclosure are shown in the drawings, and other details that are not closely related to the present disclosure are omitted. It should also be noted that similar reference numerals and letters in the drawings indicate similar items, and therefore once an item is defined in one drawing, it does not need to be discussed again for subsequent drawings.

[0039] In the related technologies known to the inventors, when splitting orders, there are many packages in the LTL freight order, and the amount of calculation is very large. In addition, when splitting, merging and allocating the LTL freight orders, two types of optimization problems need to be dealt with: 1. LTL transportation does not need to consider loading constraints, and is only charged by volume weight; 2. Full truckload transportation needs to consider loading constraints. At present, the allocation of LTL freight orders relies on manual labor, which makes it difficult to perform more reasonable distribution processing of LTL freight orders through manual allocation.

[0040] For example, the transportation cost of LTL settlement is higher than that of FTL settlement. If the volume of a single LTL freight order is too large or multiple LTL freight orders to the same destination are to be sent out on the same day, it is difficult to obtain the optimal settlement method for the LTL freight order through manual allocation. Usually, the LTL settlement method is used for settlement, which is difficult to reduce the user's freight cost. In addition, there is a situation where the transport vehicle is not fully loaded, resulting in high transportation costs and low transportation efficiency. Therefore, a new freight distribution technology solution is needed.

[0041] Figure 1 FIG. 1 is a flow chart of an embodiment of a freight delivery method according to the present disclosure, as shown in FIG. Figure 1 As shown:

[0042] Step 101, select an LTL freight order according to order selection rules.

[0043] In one embodiment, the order selection rule may be a plurality of rules, for example, the order selection rule includes rules such as the order time of the LTL freight order is within the same time period, the address of the LTL freight order belongs to the same region, etc. The address of the LTL freight order includes the address of the delivery address, etc., and 6 pm may be set as the order cut-off time, and the LTL freight orders whose order time is within one day (i.e., the same time period is between 6 pm of the previous day and 6 pm of the current day) and whose delivery address belongs to the same region are selected as a scheduling wave for freight distribution processing.

[0044] Step 102, generating a virtual order based on the LTL freight order.

[0045] In one embodiment, the virtual order includes at least one of a LTL freight order that is not allowed to be split and a split sub-order of an LTL freight order that is allowed to be split. Before splitting the LTL freight order, it is necessary to determine whether the LTL freight order is allowed to be split.

[0046] The rules for determining whether splitting an order is allowed include: whether the business requires that the LTL freight order can be split, for example, warehouse distribution orders are not allowed to be split; if the LTL freight order is a single package, splitting is not allowed; if the package information is not given on the LTL freight order (package information includes: package quantity, package weight, package volume, etc.), splitting is not allowed, etc.

[0047] Based on the order splitting granularity information, the LTL freight orders that are allowed to be split can be split to obtain split sub-orders, and the split sub-orders and the LTL freight orders that are not allowed to be split can be used as virtual orders.

[0048] The order splitting granularity can be the number of packages, package volume, weight, etc. The order splitting granularity can be the total number of packages of all orders divided by the maximum number of orders that the algorithm can handle (the number of packages is used as the order splitting granularity). For example, if 50 orders are scheduled at a time, assuming that the number of packages in each order is 100, the total number of packages is 5000. Assuming that the maximum processing capacity of the algorithm is 500 packages, then 5000 / 500=10, that is, 10 packages are used as the order splitting granularity. This order splitting granularity is used to split the LTL freight orders, and 50 orders are split into 500 virtual orders (if the number of packages of the order cannot be divided by the order splitting granularity, the remainder is used as a virtual order).

[0049] When the order splitting granularity is weight or volume, the weight or volume of the goods in the LTL freight order is evenly divided by this order splitting granularity to obtain a virtual order. If the weight or volume of the goods in the LTL freight order cannot be divided by the order splitting granularity, the remainder is taken as a virtual order.

[0050] Step 103, construct a virtual vehicle and pre-assign all virtual orders to the virtual vehicle.

[0051] Step 104: Allocate the virtual order to a real vehicle according to the distribution optimization objective function and the constraints of the distribution optimization objective function. The real vehicle may be a variety of transport vans, trucks and other vehicles.

[0052] Step 105, obtaining a distribution plan for the LTL freight order according to the allocation result of the virtual order.

[0053] In one embodiment, the splitting result of the LTL freight order is obtained based on the allocation result of the virtual order, and a delivery plan for the LTL freight order is generated according to the allocation result and the splitting result.

[0054] There can be many distribution optimization objective functions. For example, the distribution optimization objective function includes: the total transportation cost of virtual orders, the total fixed cost of real vehicle use, and the penalty cost of items in LTL freight orders being distributed among different vehicles.

[0055] The total transportation cost of the virtual order, the total fixed cost of the real vehicle usage, and the penalty cost of allocating items in the LTL freight order to different vehicles can be determined. Based on the total transportation cost, total fixed cost, and penalty cost, the value of the distribution optimization objective function is determined.

[0056] The constraints may include that the virtual order is only assigned to one vehicle, that a billing method is assigned to each of the virtual vehicle and the real vehicle, that the volume of the real vehicle is limited, and that the weight of the real vehicle is limited.

[0057] For example, define O = {1...n} to represent the set of virtual orders, V = {1...m} to represent the set of real vehicles, O i ={1...l} is the package set of the ith virtual order, C ={1...h} is the set of billing methods, represents the volume limit of the kth real vehicle, represents the volume limit of the kth real vehicle, represents the usage cost of the kth real vehicle, ucost(O i ) represents the penalty cost of whether the packages in the i-th virtual order belong to the same vehicle.

[0058] Define the decision variable y k Indicates whether the kth real vehicle is used, x i，j，k Characterizes that the jth package in the i-th virtual order is assigned to the k-th real vehicle, Characterize the k-th real vehicle using Z types of charging functions.

[0059] Determine the distribution optimization objective function and optimization goal as follows:

[0060]

[0061] Identify constraints and decision conditions:

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] in, Represents the total transportation cost of the virtual order and the total fixed cost of the real vehicle use; ucost(O i ) represents the penalty cost of allocating items in an LTL freight order to different vehicles.

[0069] Formula (1-2) indicates that a virtual order is assigned to only one vehicle, that is, each package of a virtual order is assigned to a unique vehicle. Formula (1-3) indicates that a billing method is assigned to each virtual vehicle and real vehicle, that is, each vehicle is assigned a billing method. Formula (1-4) indicates the volume limit of a real vehicle. Formula (1-5) indicates the volume limit of a real vehicle.

[0070] There are many ways to calculate the penalty cost. For example, to count the number of virtual orders belonging to the same LTL freight order assigned to different vehicles, multiple calculation rules (penalty cost functions) can be used to calculate the penalty cost based on the statistical value, wherein the higher the statistical value, the higher the penalty cost.

[0071] In one embodiment, the value of the distribution optimization objective function is determined, and the distribution optimization objective function is minimized as the goal, and the virtual orders are assigned to the real vehicles according to the constraints and using the distribution optimization algorithm. There can be many distribution optimization algorithms, such as meta-heuristic algorithms.

[0072] Iterative allocation processing can be performed on virtual orders according to constraints and using a metaheuristic algorithm to allocate virtual orders to real vehicles; if the value of the distribution optimization objective function after one iterative allocation processing is less than or equal to the value of the distribution optimization objective function before this iterative allocation processing, the result of this iterative allocation processing is accepted; if the value of the distribution optimization objective function after each iterative allocation processing is greater than the value of the distribution optimization objective function before this iterative allocation processing, the result of this iterative allocation processing is not accepted. When the number of iterative allocation processing reaches a number threshold or the execution time of the iterative allocation processing exceeds a time threshold, the iterative allocation processing of the virtual orders ends.

[0073] Metaheuristic algorithm is a method for solving the optimal solution or satisfactory solution of complex optimization problems based on the mechanism of computational intelligence. Metaheuristic algorithm is an algorithm based on intuition or experience. It can give a feasible solution to the problem within acceptable computing time and space, and the degree of deviation between the feasible solution and the optimal solution cannot necessarily be predicted in advance. A variety of existing metaheuristic algorithms can be used, for example, metaheuristic algorithms such as variable neighborhood search VNS (Variable Neighborhood Search) algorithm. VNS algorithm is a local search algorithm, which changes the neighborhood structure to achieve the process of searching for a near-optimal solution.

[0074] In one embodiment, a virtual vehicle with infinite capacity (i.e., a maximum value is set for the volume, weight, and other restrictions of the vehicle) is constructed, and it is stipulated that this virtual vehicle is settled according to the LTL billing rules. By adding a virtual vehicle with infinite capacity to the vehicle pool to place the LTL modeling technique, two types of optimization problems (1. LTL transportation does not need to consider loading constraints and is only charged by volume and weight; 2. Full truckload transportation needs to consider loading constraints) are converted into a single modeling problem.

[0075] After pre-splitting the selected LTL freight orders, a virtual order is obtained, and all virtual orders are pre-allocated to this virtual vehicle as the initial solution, that is, the initial solution is that all virtual orders are billed according to the LTL rules; among them, the virtual vehicles are billed according to the LTL billing rules, and the real vehicles are billed according to the vehicleload billing rules.

[0076] The volume limit and load limit of real vehicles are used as hard constraints, and the problem of over-splitting orders can be avoided by adding a soft constraint strategy that virtual orders belonging to the same LTL freight order are assigned to one vehicle as much as possible. For example, LTL freight order A is split into virtual orders A1 and A2, and LTL freight order B is split into virtual orders B1 and B2. Virtual orders A1 and A2 are assigned to the same vehicle as much as possible, and virtual orders B1 and B2 are assigned to the same vehicle as much as possible.

[0077] Meta-heuristic algorithms such as the VNS algorithm can be used to optimize and solve the distribution optimization objective function. According to the constraints and using meta-heuristic algorithms such as the VNS algorithm, the virtual orders are iteratively allocated to real vehicles. If the value of the distribution optimization objective function after an iterative allocation process is less than or equal to the value of the distribution optimization objective function before this iterative allocation process, the result of this iterative allocation process is accepted. When the number of iterative allocation processes reaches the number threshold or the execution time of the iterative allocation process exceeds the time threshold, the iterative allocation process for the virtual orders ends.

[0078] By using meta-heuristic algorithms such as the VNS algorithm for iterative allocation processing, the domain operations of the vehicles to which some virtual orders belong are continuously adjusted. The domain operations can be operations such as model exchange, exchange of multiple orders for one vehicle with multiple orders for another vehicle, and merging of orders for two vehicles. Due to the different billing methods involved, the domain operations add the model exchange of virtual vehicles and complete vehicles, and the operator weight is set higher, in order to better judge the pros and cons of cost calculation.

[0079] In each iterative allocation process, after each adjustment of the domain operation of the vehicle to which part of the virtual order belongs, it is necessary to evaluate the overall cost after this adjustment, that is, to calculate the value of the distribution optimization objective function; if the value of the distribution optimization objective function is smaller, then the solution is accepted, so that the cost of this iterative allocation process is slightly lower than the cost of the previous iterative allocation process, and then the solution is accepted with a certain probability. Based on the same method, the iterative allocation process is looped until the specified solution time or iterative allocation process threshold is reached, or the quality of the solution result obtained by using a metaheuristic algorithm such as the VNS algorithm is not better within the preset time, then the loop is exited. The optimal solution found in the iterative allocation process loop is output as the final result, that is, as the result of the virtual order being allocated to the real vehicle.

[0080] In one embodiment, the total transportation cost of the virtual order can be determined in a variety of ways. The transportation cost of the virtual order can be calculated based on the billing rules and attribute information of the virtual order, and the total transportation cost can be determined based on the transportation costs of all virtual orders. The full truckload billing rule is used for the virtual order assigned to the real vehicle, and the less-than-truckload billing rule is used for the virtual order assigned to the virtual vehicle; the attribute information includes at least one of the transportation address, the merchant, and the vehicle type.

[0081] For example, the transportation address includes the origin city, destination city and other addresses. Based on the origin city, destination city, merchant, and vehicle type (the LTL billing rules do not require matching vehicle types, while the vehicle type needs to be matched under the vehicle billing rules), the price of the corresponding route is matched. The origin city and destination city addresses can be multi-level addresses, and the multi-level addresses can be addresses at the province, city, district, county and other levels. The lower-level addresses are used for matching first. If the lower-level address cannot be matched, the higher-level origin and destination city addresses are matched in turn to obtain the price of the route, that is, the quotation of the route. The quotation can be a vehicle billing quotation, weight quotation, volume quotation, minimum charge, etc.

[0082] The full-truckload billing rules are used for virtual orders assigned to real vehicles, and the less-than-truckload billing rules are used for virtual orders assigned to virtual vehicles. The full-truckload billing rules determine the full-truckload billing quotation based on the route, vehicle model and other dimensions, and perform fixed billing. Among them, multiple virtual orders assigned to a real vehicle can share the cost according to the proportion of freight volume.

[0083] The LTL billing rules are based on route, volume, weight and other dimensions. The LTL billing methods can be: max (weight quotation * total order weight or volume quotation * volume, minimum charge); max (total order weight, minimum enabled quantity of weight) * weight quotation or max (total order volume, minimum enabled quantity of volume) * volume quotation; max (weight quotation * total order weight or volume quotation * volume, minimum charge), bulk weight ratio = volume (cubic centimeters) / weight (KG), goods with a ratio greater than the bulk weight are bulk goods and the volume price is taken, goods with a ratio less than the bulk weight are heavy goods and the weight price is taken.

[0084] In one embodiment, there may be multiple methods for obtaining the splitting result of the LTL freight order based on the allocation result of the virtual order. For example, the split sub-orders that are allocated to the virtual vehicle or the same real vehicle and belong to the same LTL freight order are aggregated to generate an aggregated order. The order data of the aggregated order and the same LTL freight order are compared, and based on the comparison result, it is determined whether the LTL freight order is split, and the order data includes: at least one of the package quantity, volume, and weight. If the comparison result is that the order data of the aggregated order and the same LTL freight order are different, it is determined that the order is split. If the comparison result is that the order data of the aggregated order and the same LTL freight order are the same, it is determined that the order is not split.

[0085] There are many aggregation strategies that can be used to generate aggregated orders. Aggregate the split sub-orders that are assigned to virtual vehicles or the same real vehicle and belong to the same LTL freight order. The aggregation dimension is the virtual orders in the same vehicle with the same original actual order number.

[0086] For example, the three virtual orders 958679220-1, 958679220-2, and 958679220-3, the corresponding original order number (the order number of the LTL freight order before the split) is 958679220; the other three virtual orders 958865719-1, 958865719-2, and 958865719-3, the corresponding original order number (the order number of the LTL freight order before the split) is 958865719.

[0087] The aggregation process is: aggregate the original orders 958679220 and 958865719. The six virtual orders are allocated to two vehicles: the virtual orders allocated to vehicle 1 are: virtual orders 958679220-1, 958679220-2, 958679220-3 and 958865719-1; the virtual orders allocated to vehicle 2 are: virtual orders 958865719-2, 958865719-3; the final aggregation action is: vehicle 1 is aggregated into 958679220 and 958865719-1, and vehicle 2 is aggregated into 958865719-2; that is, order 958865719 is split, while order 958679220 is not split.

[0088] According to the allocation results and order splitting results, a variety of methods can be used to generate a distribution plan for the LTL freight order. For example, according to the aggregated orders assigned to the virtual vehicle and the information on whether the orders are split, and / or the LTL freight orders assigned to the virtual vehicle that are not allowed to be split, a LTL distribution plan is generated; according to the aggregated orders assigned to the real vehicle and the information on whether the orders are split, and / or the LTL freight orders assigned to the real vehicle that are not allowed to be split, a full truckload distribution plan is generated; the LTL distribution plan and / or the full truckload distribution plan are sent to the corresponding dispatching system for dispatching.

[0089] For LTL delivery solutions, the aggregated orders assigned to virtual vehicles and LTL freight orders that are not allowed to be split are charged by weight or volume; for full truckload delivery solutions, the aggregated orders assigned to real vehicles and LTL freight orders that are not allowed to be split are settled by the full truckload "fixed price", and the user shares the freight according to the weight or volume of the aggregated orders and LTL freight orders that are not allowed to be split. The user can be notified whether the order has been split based on the information of whether the order has been split.

[0090] In one embodiment, Figure 4As shown, the present disclosure provides a freight distribution device 40, including: a selection module 41, a splitting module 42, a pre-allocation module 43, an optimization allocation module 44 and a solution generation module 45. The selection module 41 selects a LTL freight order according to an order selection rule. The splitting module 42 generates a virtual order based on the LTL freight order. For example, the splitting module 42 splits the LTL freight order that is allowed to be split based on the splitting granularity information, obtains the split sub-orders, and uses the split sub-orders and the LTL freight order that is not allowed to be split as virtual orders.

[0091] The pre-allocation module 43 constructs a virtual vehicle and pre-allocates all virtual orders to the virtual vehicle. The optimization allocation module 44 allocates virtual orders to real vehicles according to the distribution optimization objective function and the constraints of the distribution optimization objective function. The solution generation module 45 allocates virtual orders to real vehicles according to the distribution optimization objective function and the constraints of the distribution optimization objective function.

[0092] like Figure 5 As shown, the optimization allocation module 44 includes a target determination unit 441 and an iterative optimization unit 442. The target determination unit 441 determines the value of the distribution optimization objective function. The iterative optimization unit 442 takes minimizing the value of the distribution optimization objective function as the goal, and allocates virtual orders to real vehicles according to constraints and using a distribution optimization algorithm.

[0093] In one embodiment, the target determination unit 441 determines the total transportation cost of the virtual order, the total fixed cost of the use of the real vehicle, and the penalty cost of the items in the LTL freight order being distributed among different vehicles. For example, the target determination unit 441 calculates the transportation cost of the virtual order according to the billing rules and attribute information of the virtual order, and determines the total transportation cost based on the transportation cost of all virtual orders. The target determination unit 441 determines the value of the distribution optimization objective function according to the total transportation cost, the total fixed cost, and the penalty cost.

[0094] In one embodiment, the iterative optimization unit 442 performs iterative allocation processing on the virtual order according to the constraint conditions and uses a metaheuristic algorithm to allocate the virtual order to the real vehicle; wherein, if the value of the distribution optimization objective function after one iterative allocation processing is less than or equal to the value of the distribution optimization objective function before this iterative allocation processing, the iterative optimization unit 442 accepts the result of this iterative allocation processing. When the number of iterative allocation processing reaches the number threshold or the execution time of the iterative allocation processing exceeds the time threshold, the iterative optimization unit 442 ends the iterative allocation processing of the virtual order.

[0095] In one embodiment, Figure 6As shown, the solution generation module 45 includes an order splitting determination unit 451 and a delivery determination unit 452. The order splitting determination unit 451 obtains the order splitting result of the LTL freight order based on the allocation result of the virtual order. The delivery determination unit 452 generates a delivery solution for the LTL freight order according to the allocation result and the order splitting result.

[0096] The order splitting determination module 451 aggregates the split sub-orders assigned to the virtual vehicle or the same real vehicle and belonging to the same LTL freight order to generate an aggregated order; the order splitting determination module 451 compares the order data of the aggregated order and the same LTL freight order, and determines whether the LTL freight order is split based on the comparison result.

[0097] The delivery determination unit 452 generates an LTL delivery plan based on the aggregated orders assigned to the virtual vehicle and the information on whether the orders are split, and / or the LTL freight orders assigned to the virtual vehicle that are not allowed to be split; the delivery determination unit 452 generates a full vehicle delivery plan based on the aggregated orders assigned to the real vehicle and the information on whether the orders are split, and / or the LTL freight orders assigned to the real vehicle that are not allowed to be split.

[0098] In one embodiment, Figure 7 As shown, the present disclosure provides a freight delivery device that may include a memory 72, a processor 71, a communication interface 73, and a bus 74. The memory 72 is used to store instructions, the processor 71 is coupled to the memory 72, and the processor 71 is configured to execute the above-mentioned freight delivery method based on the instructions stored in the memory 72.

[0099] The memory 72 may be a high-speed RAM memory, a non-volatile memory, etc., or a memory array. The memory 72 may also be divided into blocks, and the blocks may be combined into virtual volumes according to certain rules. The processor 71 may be a central processing unit CPU, or an application specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the freight distribution method disclosed herein.

[0100] In one embodiment, the present disclosure provides a computer-readable storage medium storing computer instructions, which implement the method in any of the above embodiments when the instructions are executed by a processor.

[0101] The freight delivery method, device and storage medium in the above-mentioned embodiments select LTL freight orders and generate virtual orders, pre-assign all virtual orders to virtual vehicles, and assign virtual orders to real vehicles according to the delivery optimization objective function and constraints to obtain delivery plans for LTL freight orders; they can automatically process the delivery of LTL freight orders to obtain optimized delivery plans, effectively improve the freight efficiency of LTL logistics, reduce users' freight costs, and improve users' usage experience; and they can improve the utilization rate of vehicle resources, reduce transportation costs, and improve transportation efficiency.

[0102] Many ways can be used to implement the method and system of the present disclosure. For example, the method and system of the present disclosure can be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present disclosure are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure can also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0103] The description of the present disclosure is given for the purpose of illustration and description, and is not intended to be exhaustive or to limit the present disclosure to the disclosed form. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present disclosure, and to enable those of ordinary skill in the art to understand the present disclosure and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. A freight delivery method, characterized in that: include: Select LTL freight orders according to order selection rules; Generate a virtual order based on the LTL freight order; wherein the virtual order includes: at least one of a LTL freight order that is not allowed to be split and a split sub-order of the LTL freight order that is allowed to be split; Constructing a virtual vehicle, and pre-assigning all the virtual orders to the virtual vehicle; Allocating the virtual order to a real vehicle according to a distribution optimization objective function and constraints of the distribution optimization objective function; A delivery plan for the LTL freight order is obtained according to the allocation result of the virtual order.

2. The method according to claim 1, characterized in that The allocating the virtual order to the real vehicle according to the distribution optimization objective function and the constraints of the distribution optimization objective function includes: Determining a value of the distribution optimization objective function; With the goal of minimizing the value of the distribution optimization objective function, the virtual order is allocated to the real vehicle according to the constraints and using the distribution optimization algorithm.

3. The method according to claim 2, characterized in that Determining the value of the distribution optimization objective function includes: Determining a total transportation cost of the virtual order, a total fixed cost of using the real vehicle, and a penalty cost for allocating items in the LTL freight order to different vehicles; The value of the distribution optimization objective function is determined according to the total transportation cost, the total fixed cost and the penalty cost.

4. The method according to claim 3, characterized in that Determining the total shipping cost of the virtual order includes: Calculate the transportation cost of the virtual order according to the charging rules and attribute information of the virtual order; Wherein, the virtual order assigned to the real vehicle adopts the full vehicle billing rule, and the virtual order assigned to the virtual vehicle adopts the less-than-load billing rule; the attribute information includes: at least one of the transportation address, merchant, and vehicle model; The total shipping cost is determined based on the shipping costs of all the virtual orders.

5. The method according to claim 2, characterized in that The distribution optimization algorithm includes: a meta-heuristic algorithm; the distribution of the virtual order to the real vehicle according to the constraint conditions and using the distribution optimization algorithm with the goal of minimizing the value of the distribution optimization objective function includes: According to the constraint conditions and using the meta-heuristic algorithm, the virtual orders are iteratively assigned to the real vehicles; If the value of the distribution optimization objective function after one iteration of allocation processing is less than or equal to the value of the distribution optimization objective function before this iteration of allocation processing, the result of this iteration of allocation processing is accepted; When the number of iterative allocation processing reaches a number threshold or the execution time of the iterative allocation processing exceeds a time threshold, the iterative allocation processing of the virtual order is terminated.

6. The method according to claim 1, characterized in that The constraints include: the virtual order is only assigned to one vehicle, a billing method is assigned to the virtual vehicle and the real vehicle respectively, a volume limit of the real vehicle and a weight limit of the real vehicle.

7. The method according to claim 1, characterized in that The step of obtaining a distribution plan for the LTL freight order according to the allocation result of the virtual order includes: Obtaining a splitting result of the LTL freight order based on the allocation result of the virtual order; A delivery plan for the LTL freight order is generated based on the allocation result and the order splitting result.

8. The method according to claim 7, characterized in that The step of obtaining the splitting result of the LTL freight order based on the allocation result of the virtual order includes: Aggregating the split sub-orders assigned to the virtual vehicle or the same real vehicle and belonging to the same LTL freight order to generate an aggregated order; Comparing the order data of the aggregated order with the order data of the same LTL freight order, and determining whether the LTL freight order is to be split based on the comparison result; The order data includes at least one of the following data: package quantity, volume, and weight.

9. The method according to claim 8, characterized in that Generating a distribution plan for the LTL freight order according to the allocation result and the order splitting result includes: Generate an LTL delivery plan according to the aggregated order assigned to the virtual vehicle and the information on whether the order is split, and / or the LTL freight order that is not allowed to be split and assigned to the virtual vehicle; A full vehicle distribution plan is generated based on the aggregated order assigned to the real vehicle and the information on whether the order is split, and / or the LTL freight order assigned to the real vehicle that is not allowed to be split.

10. The method according to claim 1, characterized in that Generating a virtual order based on the LTL freight order includes: Based on the order splitting granularity information, the LTL freight order that is allowed to be split is split to obtain the split sub-order; The split sub-order and the LTL freight order that is not allowed to be split are used as the virtual order.

11. The method according to any one of claims 1 to 10, characterized in that The order selection rules include: the order time of the LTL freight orders is within the same time period, and the addresses of the LTL freight orders belong to the same area.

12. A freight delivery device, characterized in that: include: A selection module is used to select LTL freight orders according to order selection rules; A splitting module, used to generate a virtual order based on the LTL freight order; wherein the virtual order includes: at least one of a split sub-order of an LTL freight order that is not allowed to be split and a split sub-order of an LTL freight order that is allowed to be split; A pre-allocation module, used for constructing a virtual vehicle and pre-allocating all the virtual orders to the virtual vehicle; An optimization allocation module, used for allocating the virtual order to a real vehicle according to a distribution optimization objective function and constraints of the distribution optimization objective function; A plan generation module is used to obtain a distribution plan for the LTL freight order according to the allocation result of the virtual order.

13. A freight delivery device, characterized in that: include: Memory; and a processor coupled to the memory, wherein the processor is configured to execute the method according to any one of claims 1 to 11 based on instructions stored in the memory.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium non-transitorily stores computer instructions, and the instructions are executed by a processor to perform the method according to any one of claims 1 to 11.

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