Express transportation planning data processing method and device, medium and electronic equipment

Abstract

The embodiment of the invention provides an express transportation planning data processing method and device, a medium and electronic equipment. The method comprises the following steps: acquiring node information of each express delivery node in an express delivery service area, transport side information between any two express delivery nodes, and order information of an express delivery order between any two express delivery nodes in a plan period; according to the node information, the transportation side information and the order information, constructing an express transportation planning model including model constraints and a model target based on a mixed integer programming algorithm; and solving decision variables in the express transportation planning model to obtain first express transportation planning data. According to the technical scheme of the embodiment of the invention, the search space and optimization capability of the model can be increased, so that the accuracy of express transportation planning data processing is improved.

Description

Technical Field

[0001] The present application relates to the field of computer and data processing technology, and in particular, to a method, device, medium and electronic device for processing express transportation planning data. Background Art

[0002] In the express logistics transportation scenario, achieving the spatial transfer of express delivery within the specified time limit is the embodiment of the core competitiveness of logistics companies. Compared with traditional transportation issues, the scale of express transportation, transportation time requirements and transportation express types are more complicated, and the express nodes in the express transportation network have corresponding levels.

[0003] In the prior art, a patent with publication number CN118446395A discloses a data processing method, device, medium and electronic device for express transportation route planning, which constructs a feasible path for orders based on a certain specific rule and selects a fixed path by distance. Afterwards, a model is built using the selected path as a potential feasible path, with the goal of minimum transportation cost, minimum timeout probability and minimum total timeout duration, to calculate the specific paths used for transportation needs between nodes and the amount of goods transported by each path. However, when optimizing feasible routes, this solution selects the routes, and each point can only be connected to the nearest points of different levels. There are only 28 connection situations, and there are only 6 possible transportation routes at most each time, which reduces the search space and optimization ability of the model. Based on this, how to increase the search space and optimization ability of the model and improve the accuracy of express transportation planning data processing is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The embodiments of the present application provide a method, device, computer program product or computer program, computer-readable medium and electronic device for processing express transportation planning data, which can increase the search space and optimization capability of the model to at least a certain extent, thereby improving the accuracy of express transportation planning data processing.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0006] According to one aspect of an embodiment of the present application, a method for processing express transportation planning data is provided, the method comprising: obtaining node information of each express node in an express service area, transportation edge information between any two express nodes, and order information of express orders between any two express nodes in a planning period; constructing an express transportation planning model including model constraints and model objectives based on a mixed integer programming algorithm according to the node information, the transportation edge information, and the order information; solving the decision variables in the express transportation planning model to obtain first express transportation planning data, wherein the express transportation planning model includes the following model objectives:

[0007]

[0008] Among them, TC ij W represents the cost of transporting a unit weight of express from express node i to express node j per kilometer, which is a quantitative value; ij represents the average weight of the express items transported from express node i to express node j, which is a quantitative value; x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; D ij represents the transportation distance from express node i to express node j, which is a quantitative value; OC i represents the average transit cost of each express delivery at express node i, which is a quantitative value; δ k represents the length of time that express order k exceeds the scheduled delivery time, which is a continuous decision variable; b k Indicates whether the express order k exceeds the scheduled delivery time, which is a 0-1 decision variable; overQ i represents the number of express deliveries that exceed the express delivery capacity limit of express delivery node i, which is a continuous decision variable; α represents the express delivery transportation cost coefficient, which is a quantitative value; β represents the express delivery transit cost coefficient, which is a quantitative value; γ represents the penalty cost for express delivery overtime, which is a quantitative value; η represents the penalty cost for express delivery default, which is a quantitative value; It represents the penalty cost per unit of express delivery that exceeds the express delivery capacity limit of the express delivery node, which is a quantitative value; E represents the set of express delivery edges; K represents the set of express delivery orders, where the express deliveries in the same express delivery order have the same starting node and ending node; N represents the set of express delivery nodes.

[0009] In some embodiments of the present application, based on the above scheme, the express transportation planning model includes the following model constraints:

[0010]

[0011] Among them, x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; p jkrepresents the shipment volume of express node j for express order k; x jik represents the number of express deliveries from express node j to express node i in express order k, which is an integer decision variable; d jk represents the number of parcels received by courier node j for courier order k; N represents the set of courier nodes; K represents the set of courier orders, where the couriers in the same courier order have the same starting node and ending node.

[0012] In some embodiments of the present application, based on the above scheme, the express transportation planning model includes the following model constraints:

[0013]

[0014] Among them, x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; Q j represents the upper limit of express delivery processing volume of express delivery node j, which is a fixed quantity; overQ i represents the number of couriers that exceed the express capacity limit of express node i, which is a continuous decision variable; N represents the set of express nodes; K represents the set of express orders, where the couriers in the same express order have the same starting node and ending node.

[0015] In some embodiments of the present application, based on the above scheme, the express transportation planning model includes the following model constraints:

[0016]

[0017] Among them, x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; ijk It indicates whether there is a courier transported from courier node i to courier node j in courier order k, which is a 0-1 decision variable; B represents the auxiliary decision variable, which is a maximum value; E represents the set of courier transportation edges; K represents the set of courier orders, among which the couriers in the same courier order have the same starting node and ending node.

[0018] In some embodiments of the present application, based on the above scheme, the express transportation planning model includes the following model constraints:

[0019]

[0020] Among them, x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; L ijIndicates the hierarchical change relationship from express node i to express node j, where 1 indicates upstream transfer, 0 indicates same-level transfer, and 2 indicates downstream transfer. It is quantitative; U ij represents the minimum express delivery volume threshold from express node i to express node j, which is a quantitative value; q ij Indicates whether there is a courier transported from courier node i to courier node j, which is a 0-1 decision variable; q ijk Indicates whether there is a courier transported from courier node i to courier node j in courier order k, which is a 0-1 decision variable; E represents the set of courier transport edges; K represents the set of courier orders, where the couriers in the same courier order have the same starting node and ending node; N max Represents a collection of top-level express nodes.

[0021] In some embodiments of the present application, based on the above scheme, the express transportation planning model includes the following model constraints:

[0022]

[0023] Among them, δ k It represents the time that the express order k exceeds the scheduled delivery time, which is a continuous decision variable; Indicates that express order k is at express node D k The dispatch time is a continuous decision variable; Indicates that express order k is at express node O k The dispatch time is a continuous decision variable; Indicates that express order k starts from the starting node O k To the terminal node D k The longest transportation time is quantitative; b k Indicates whether the express order k exceeds the scheduled delivery time, which is a 0-1 decision variable; B represents the auxiliary decision variable, which is a maximum value; T jk represents the dispatch time of express order k at express node j, which is a continuous decision variable; T ik represents the dispatch time of express order k at express node i, which is a continuous decision variable; D ij represents the transportation distance from express node i to express node j, which is a fixed quantity; S represents the rated speed of the express transportation vehicle, which is a fixed quantity; H jk represents the transit time of express order k at express node j, which is a fixed quantity; q ijk It indicates whether there is a courier transported from courier node i to courier node j in courier order k, which is a 0-1 decision variable; K represents the set of courier orders, where the couriers in the same courier order have the same starting node and ending node; O represents the set of starting nodes of the courier; D represents the set of ending nodes of the courier; E represents the set of courier transportation edges.

[0024] In some embodiments of the present application, based on the above scheme, the express transportation planning model includes the following model constraints:

[0025]

[0026] Among them, y ijk represents the distribution status of the express in express order k from express node i to express node j, where 1 represents the collection stage, 2 represents the bulk stage, and 0 represents the same-level circulation, which is an integer decision variable; L ij represents the hierarchical change relationship from express node i to express node j, where 1 represents upstream transfer, 0 represents transfer at the same level, and 2 represents downstream transfer, which is quantitative; q ijk Indicates whether there is a courier transported from courier node i to courier node j in courier order k, which is a 0-1 decision variable; y jlk represents the distribution status of the express in express order k from express node j to express node l, where 1 represents the collection stage, 2 represents the bulk stage, and 0 represents the same-level circulation. It is an integer decision variable; B represents the auxiliary decision variable, which is the maximum value; q jlk It indicates whether there is a courier transported from courier node j to courier node l in courier order k, which is a 0-1 decision variable; K represents the set of courier orders, where the couriers in the same courier order have the same starting node and ending node; E represents the set of courier transportation edges.

[0027] In some embodiments of the present application, based on the aforementioned scheme, after obtaining the first express transportation planning data, the method further includes: determining the departure time of each departure shift in each express node according to the node information, determining the upper limit of the transportation time of each express order according to the order information, and determining the transportation distance of the transportation route for transporting each express order; based on the departure time, the upper limit of the transportation time, the transportation distance, and the first express transportation planning data, with the goal of maximizing the time limit compliance rate of each express order, solving the second express transportation planning data based on a heuristic strategy algorithm, the second express transportation planning data at least includes the target transportation route for executing each express order within the planning period, the number of express deliveries transported by each express order on the corresponding target transportation route, and the target departure shift for each express order at the corresponding express node.

[0028] According to one aspect of an embodiment of the present application, a device for processing express transportation planning data is provided, the device comprising: an acquisition unit, used to acquire node information of each express node in an express service area, transportation edge information between any two express nodes, and order information of express orders between any two express nodes in a planning period; a construction unit, used to construct an express transportation planning model including model constraints and model objectives based on a mixed integer programming algorithm according to the node information, the transportation edge information, and the order information; a solution unit, used to solve the decision variables in the express transportation planning model to obtain first express transportation planning data, wherein the express transportation planning model includes the following model objectives:

[0029]

[0030] Among them, TC ij W represents the cost of transporting a unit weight of express from express node i to express node j per kilometer, which is a quantitative value; ij represents the average weight of the express items transported from express node i to express node j, which is a quantitative value; x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; D ij represents the transportation distance from express node i to express node j, which is a quantitative value; OC i represents the average transit cost of each express delivery at express node i, which is a quantitative value; δ k represents the length of time that express order k exceeds the scheduled delivery time, which is a continuous decision variable; b k Indicates whether the express order k exceeds the scheduled delivery time, which is a 0-1 decision variable; overQ i represents the number of express deliveries that exceed the express delivery capacity limit of express delivery node i, which is a continuous decision variable; α represents the express delivery transportation cost coefficient, which is a quantitative value; β represents the express delivery transit cost coefficient, which is a quantitative value; γ represents the penalty cost for express delivery overtime, which is a quantitative value; η represents the penalty cost for express delivery default, which is a quantitative value; It represents the penalty cost per unit of express delivery that exceeds the express delivery capacity limit of the express delivery node, which is a quantitative value; E represents the set of express delivery edges; K represents the set of express delivery orders, where the express deliveries in the same express delivery order have the same starting node and ending node; N represents the set of express delivery nodes.

[0031] According to one aspect of the embodiments of the present application, a computer program product or a computer program is provided, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method described in the above embodiments.

[0032] According to one aspect of an embodiment of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method described in the above embodiment is implemented.

[0033] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; 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 methods described in the above embodiments.

[0034] In the present application, according to the node information of each express node in the express business area, the transportation edge information between any two express nodes, and the order information of express orders between any two express nodes within the planning period, an express transportation planning model including model constraints and model objectives is constructed based on a mixed integer programming algorithm. The decision variables in the express transportation planning model can be solved to obtain the first express transportation planning data. The present application abandons the practice of screening feasible transportation paths to build a model in the prior art, which can increase the search space and optimization capabilities of the model and improve the accuracy of express transportation planning data processing.

[0035] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0037] Figure 1 A schematic diagram of an express delivery network according to an embodiment of the present application is shown;

[0038] Figure 2 A flowchart of a method for processing express transportation planning data according to an embodiment of the present application is shown;

[0039] Figure 3 A block diagram of an express transportation planning data processing device according to an embodiment of the present application is shown;

[0040] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0041] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0042] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0043] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0044] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0045] It should be noted that the "multiple" mentioned in this article refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0046] Before elaborating on this solution in detail, Figure 1 This paper briefly describes the concepts and production application scenarios involved in this solution.

[0047] See also Figure 1 , showing a schematic diagram of an express delivery network according to an embodiment of the present application.

[0048] In actual production scenarios, for example, express delivery services usually provide a given transportation network (i.e., express business area), in which there are multiple express delivery nodes at different levels (e.g. Figure 1The transportation network shown is divided into three layers), and there is a transportation demand from the collection point to the delivery point between the express nodes. In the current network scenario, the connection status between different express nodes is known, forming a non-fully connected network structure. Furthermore, the timeliness of the transportation demand of different express nodes varies. For example, in intra-provincial distribution, it is divided into two situations: express and express package in business logic, and there are different time limits for completing express and express packages. Express delivery requires the completion of the next morning delivery, and express package requires the next day delivery. For example, in inter-provincial distribution, according to the arrival time standard, the number of days required for delivery is uncertain, and it is required by default on the Tth day. n Arrived at 12 o'clock that day.

[0049] It should be noted that next-morning delivery means that for express delivery on the same day, it needs to be delivered before 12:00 the next day, and next-day delivery means that for express delivery on the same day, it needs to be delivered before 20:00 the next day.

[0050] Furthermore, in actual production scenarios, the departure schedule and departure time of the express delivery node can be given. After the express delivery arrives at the express delivery node to collect the goods, only the express delivery that arrives before the schedule can catch the schedule. The express delivery that arrives in advance needs to wait at the express delivery node. If there is a new route and there is no schedule, a new schedule needs to be created. For example, the default departure time can be 22:00 on the T0 day, and then there will be three schedules every day. The departure time nodes can be 2:30, 6:30, and 22:30, for example.

[0051] Combination Figure 1 It should be noted that the transportation network 100 is divided into three layers (i.e., the express nodes are divided into three levels), among which the third-level express nodes (generally the express nodes at the district and county level) can be A3, B3, C3, D3, E3, F3, G3, H3, I3, the second-level express nodes (generally the express nodes at the prefecture-level city level) can be A2, B2, C2, D2, and the first-level express nodes (generally the express nodes at the provincial capital level) can be A1, B1.

[0052] In an actual production scenario, for example, in an express order with the secondary express node A2 as the starting node and the tertiary express node D3 as the terminating node, multiple expresses are included, and their transportation routes are generally composed of multiple express nodes and transportation edges between express nodes, which may include but are not limited to A2-C3-D3, A2-A1-D3, A2-A1-D2-D3, A2-A1-B1-D2-D3, and one or more target transportation routes need to be determined from these transportation routes to execute the express order, and. In the entire express business area, in each planning cycle (such as every day), there are a large number of express orders that need to plan target transportation routes, and the number of expresses that need to be carried by each transportation edge in the transportation route. Since the express transportation scale, transportation timeliness requirements, etc. are very complex, and there are also many factors that affect the accuracy of express transportation planning data processing, how to improve the accuracy of express transportation planning data processing while ensuring the search space and optimization capabilities of the model has become a technical problem that needs to be solved urgently. In this case, the present application proposes a express transportation planning data processing solution.

[0053] The implementation details of the technical solution of the embodiment of the present application are described in detail below:

[0054] Figure 2 A flowchart of a method for processing express transportation planning data according to an embodiment of the present application is shown. The method for processing express transportation planning data can be executed by a device having a computing and processing function. Figure 2 As shown, the express transportation planning data processing method includes at least steps 210 to 230, which are described in detail as follows:

[0055] In step 210, node information of each express node in the express service area, transport edge information between any two express nodes, and order information of express orders between any two express nodes within the planning period are obtained.

[0056] In the present application, the node information of each express node in the express business area may include the location, level, dispatch schedule, upper limit of express capacity that can be distributed, average distribution cost of express delivery, etc. of the express node.

[0057] In this application, the transport side information may include the express quantity threshold that must be met for shipment, the transport distance, etc.

[0058] In this application, order information may include the number of express deliveries, average express weight, upper limit of shipping time, etc.

[0059] It should be noted that in the present application, express deliveries of the same express delivery order can be transported separately. For example, express deliveries between two cities can be transported using different transportation routes.

[0060] In step 220, according to the node information, the transport edge information, and the order information, an express transport planning model including model constraints and model objectives is constructed based on a mixed integer programming algorithm.

[0061] In this application, the express transportation planning model constrains the logistics balance constraints of express orders at express nodes, that is, the express transportation planning model includes the model constraints shown in the following formula (1):

[0062]

[0063] Among them, x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; p jk represents the shipment volume of express node j for express order k; x jik represents the number of express deliveries from express node j to express node i in express order k, which is an integer decision variable; d jk represents the number of parcels received by courier node j for courier order k; N represents the set of courier nodes; K represents the set of courier orders, where the couriers in the same courier order have the same starting node and ending node.

[0064] In this application, the express transportation planning model constrains the upper limit of the express capacity that the express node can collect and distribute, that is, the express transportation planning model includes the model constraint shown in the following formula (2):

[0065]

[0066] Among them, x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; Q j represents the upper limit of express delivery processing volume of express delivery node j, which is a fixed quantity; overQ i represents the number of couriers that exceed the express capacity limit of express node i, which is a continuous decision variable; N represents the set of express nodes; K represents the set of express orders, where the couriers in the same express order have the same starting node and ending node.

[0067] In this application, the express transportation planning model constrains the use conditions of the transportation edge, that is, the express transportation planning model includes the model constraints shown in the following formula (3):

[0068]

[0069] Among them, x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; ijkIt indicates whether there is a courier transported from courier node i to courier node j in courier order k, which is a 0-1 decision variable; B represents the auxiliary decision variable, which is a maximum value; E represents the set of courier transportation edges; K represents the set of courier orders, among which the couriers in the same courier order have the same starting node and ending node.

[0070] In this application, the express transportation planning model constrains that the transfer between express nodes at the same level should meet the minimum shipment threshold, that is, the express transportation planning model includes the model constraint shown in the following formula (4):

[0071]

[0072] Among them, x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; L ij Indicates the hierarchical change relationship from express node i to express node j, where 1 indicates upstream transfer, 0 indicates same-level transfer, and 2 indicates downstream transfer. It is quantitative; U ij represents the minimum express delivery volume threshold from express node i to express node j, which is a quantitative value; q ij Indicates whether there is a courier transported from courier node i to courier node j, which is a 0-1 decision variable; q ijk Indicates whether there is a courier transported from courier node i to courier node j in courier order k, which is a 0-1 decision variable; E represents the set of courier transport edges; K represents the set of courier orders, where the couriers in the same courier order have the same starting node and ending node; N max Represents a collection of top-level express nodes.

[0073] In this application, the express transportation planning model constrains the express transportation time not to exceed the maximum time limit, that is, the express transportation planning model includes the model constraint shown in the following formula (5):

[0074]

[0075] Among them, δ k It represents the time that the express order k exceeds the scheduled delivery time, which is a continuous decision variable; Indicates that express order k is at express node D k The dispatch time is a continuous decision variable; Indicates that express order k is at express node O k The dispatch time is a continuous decision variable; Indicates that express order k starts from the starting node O k To the terminal node D k The longest transportation time is quantitative; b kIndicates whether the express order k exceeds the scheduled delivery time, which is a 0-1 decision variable; B represents the auxiliary decision variable, which is a maximum value; T jk represents the dispatch time of express order k at express node j, which is a continuous decision variable; T ik represents the dispatch time of express order k at express node i, which is a continuous decision variable; D ij represents the transportation distance from express node i to express node j, which is a fixed quantity; S represents the rated speed of the express transportation vehicle, which is a fixed quantity; H jk represents the transit time of express order k at express node j, which is a fixed quantity; q ijk It indicates whether there is a courier transported from courier node i to courier node j in courier order k, which is a 0-1 decision variable; K represents the set of courier orders, where the couriers in the same courier order have the same starting node and ending node; O represents the set of starting nodes of the courier; D represents the set of ending nodes of the courier; E represents the set of courier transportation edges.

[0076] In this application, the express transportation planning model constrains the hierarchical relationship between express nodes (it is not possible to first disperse the goods and then collect the goods), that is, the express transportation planning model includes the model constraint shown in the following formula (6):

[0077]

[0078] Among them, y ijk represents the distribution status of the express in express order k from express node i to express node j, where 1 represents the collection stage, 2 represents the bulk stage, and 0 represents the same-level circulation, which is an integer decision variable; L ij represents the hierarchical change relationship from express node i to express node j, where 1 represents upstream transfer, 0 represents transfer at the same level, and 2 represents downstream transfer, which is quantitative; q ijk Indicates whether there is a courier transported from courier node i to courier node j in courier order k, which is a 0-1 decision variable; y jlk represents the distribution status of the express in express order k from express node j to express node l, where 1 represents the collection stage, 2 represents the bulk stage, and 0 represents the same-level circulation. It is an integer decision variable; B represents the auxiliary decision variable, which is the maximum value; q jlk It indicates whether there is a courier transported from courier node j to courier node l in courier order k, which is a 0-1 decision variable; K represents the set of courier orders, where the couriers in the same courier order have the same starting node and ending node; E represents the set of courier transportation edges.

[0079] In this application, the express transportation planning model includes the model objective shown in the following formula (7):

[0080]

[0081] Among them, TC ij W represents the cost of transporting a unit weight of express from express node i to express node j per kilometer, which is a quantitative value; ij represents the average weight of the express items transported from express node i to express node j, which is a quantitative value; x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; D ij represents the transportation distance from express node i to express node j, which is a quantitative value; OC i represents the average transit cost of each express delivery at express node i, which is a quantitative value; δ k represents the length of time that express order k exceeds the scheduled delivery time, which is a continuous decision variable; b k Indicates whether the express order k exceeds the scheduled delivery time, which is a 0-1 decision variable; overQ i represents the number of express deliveries that exceed the express delivery capacity limit of express delivery node i, which is a continuous decision variable; α represents the express delivery transportation cost coefficient, which is a quantitative value; β represents the express delivery transit cost coefficient, which is a quantitative value; γ represents the penalty cost for express delivery overtime, which is a quantitative value; η represents the penalty cost for express delivery default, which is a quantitative value; It represents the penalty cost per unit of express delivery that exceeds the express delivery capacity limit of the express delivery node, which is a quantitative value; E represents the set of express delivery edges; K represents the set of express delivery orders, where the express deliveries in the same express delivery order have the same starting node and ending node; N represents the set of express delivery nodes.

[0082] In this application, the model objectives mainly include five parts. The first part is the express transportation cost, the second part is the express transit cost, the third part is the minimum number of express order defaults, the fourth part is the shortest express order default time, and the fifth part is the minimum number of express deliveries that exceed the express node i's express processing capacity limit.

[0083] In step 230, the decision variables in the express transportation planning model are solved to obtain the first express transportation planning data.

[0084] In the present application, after constructing the express transportation planning model, the various decision variables (i.e., decision variables) in the express transportation planning model can be solved based on a mathematical solver to obtain express transportation planning data, i.e., data such as the quantity of express deliveries in each express order transported from one express node to another.

[0085] In the present application, according to the node information of each express node in the express business area, the transportation edge information between any two express nodes, and the order information of express orders between any two express nodes within the planning period, an express transportation planning model including model constraints and model objectives is constructed based on a mixed integer programming algorithm. The decision variables in the express transportation planning model can be solved to obtain the first express transportation planning data. The present application abandons the practice of screening feasible transportation paths to build a model in the prior art, which can increase the search space and optimization capabilities of the model and improve the accuracy of express transportation planning data processing.

[0086] In the present application, after solving the first express transportation planning data based on the express transportation planning model, the following steps 241 to 242 may also be performed:

[0087] Step 241, determining the departure time of each departure shift in each express node according to the node information, determining the upper limit of the transportation time of each express order according to the order information, and determining the transportation distance of the transportation route for transporting each express order;

[0088] Step 242, according to the departure time, the upper limit of the transportation time, the transportation distance, and the first express transportation planning data, with the goal of maximizing the time limit achievement rate of each express order, solve the second express transportation planning data based on the heuristic strategy algorithm, the second express transportation planning data at least includes the target transportation route for executing each express order within the planning period, the number of express deliveries transported by each express order on the corresponding target transportation route, and the target departure schedule of each express order at the corresponding express node.

[0089] It should be noted that in the present application, the rated transport speed of transport vehicles within the express business area is quantitative.

[0090] In this application, the scheme is mainly divided into two decoupled stages. In the first stage, the dispatch schedule constraints of each express node are not considered for the time being, and the practice of selecting feasible transportation paths to build models in the prior art is abandoned. The focus is on network resource constraints and transportation time constraints to obtain the express flow direction in the transportation network. In the second stage, based on the solved express flow direction, a heuristic strategy is used to adjust the specific departure time of each express node in the network according to the departure schedule of each express node, and the departure time of each express order at the express node is adjusted forward and backward until the departure time meets the schedule, thereby updating the delivery time and commodity time limit compliance rate of the planning results. By decoupling the express logistics flow direction planning and the departure time constraints, the complexity of express transportation planning can be reduced and the efficiency of express transportation planning data processing can be improved.

[0091] In practical applications, this application focuses on actual business scenarios, taking into account the transportation network in the entire express business area, express time limits, express node departure schedules and other information, and meeting the express node flow balance, minimum shipment threshold, cargo handling capacity limits, express logistics time constraints and hierarchical relationship constraints between express nodes. With the goal of minimizing transportation costs and transportation timeouts, this application provides a transportation flow direction plan for the entire network, globally optimizes the decision-making space, realizes intelligent optimization decisions for express transportation business, improves decision-making efficiency, facilitates manual reference execution, and significantly enhances usability.

[0092] The following describes an apparatus embodiment of the present application, which can be used to execute the express transportation planning data processing method in the above-mentioned embodiment of the present application. For details not disclosed in the apparatus embodiment of the present application, please refer to the above-mentioned embodiment of the express transportation planning data processing method of the present application.

[0093] Figure 3 A block diagram of an express transportation planning data processing device according to an embodiment of the present application is shown.

[0094] Reference Figure 3 As shown, according to an embodiment of the present application, an express transportation planning data processing device 300 includes: an acquisition unit 301, a construction unit 302 and a solution unit 303.

[0095] Among them, the acquisition unit 301 is used to obtain the node information of each express node in the express business area, the transportation edge information between any two express nodes, and the order information of the express order between any two express nodes in the planning period; the construction unit 302 is used to construct an express transportation planning model including model constraints and model objectives based on the mixed integer programming algorithm according to the node information, the transportation edge information, and the order information; the solution unit 303 is used to solve the decision variables in the express transportation planning model to obtain the first express transportation planning data, wherein the express transportation planning model includes the following model objectives:

[0096]

[0097] Among them, TC ij W represents the cost of transporting a unit weight of express from express node i to express node j per kilometer, which is a quantitative value; ij represents the average weight of the express items transported from express node i to express node j, which is a quantitative value; x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; D ij represents the transportation distance from express node i to express node j, which is a quantitative value; OC i represents the average transit cost of each express delivery at express node i, which is a quantitative value; δk represents the length of time that express order k exceeds the scheduled delivery time, which is a continuous decision variable; b k Indicates whether the express order k exceeds the scheduled delivery time, which is a 0-1 decision variable; overQ i represents the number of express deliveries that exceed the express delivery capacity limit of express delivery node i, which is a continuous decision variable; α represents the express delivery transportation cost coefficient, which is a quantitative value; β represents the express delivery transit cost coefficient, which is a quantitative value; γ represents the penalty cost for express delivery overtime, which is a quantitative value; η represents the penalty cost for express delivery default, which is a quantitative value; It represents the penalty cost per unit of express delivery that exceeds the express delivery capacity limit of the express delivery node, which is a quantitative value; E represents the set of express delivery edges; K represents the set of express delivery orders, where the express deliveries in the same express delivery order have the same starting node and ending node; N represents the set of express delivery nodes.

[0098] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0099] It should be noted that Figure 4 The computer system 400 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0100] like Figure 4 As shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, ROM 402 and RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0101] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a local area network (LAN) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0102] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 409, and / or installed from a removable medium 411. When the computer program is executed by a central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0103] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0104] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0105] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0106] As another aspect, the present application further provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the method described in the above embodiment.

[0107] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiment.

[0108] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0109] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0110] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0111] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for processing express transportation planning data, characterized in that: The method comprises: Obtain node information of each express node in the express business area, transportation edge information between any two express nodes, and order information of express orders between any two express nodes within the planning period; According to the node information, the transport edge information, and the order information, a courier transport planning model including model constraints and model objectives is constructed based on a mixed integer programming algorithm; Solve the decision variables in the express transportation planning model to obtain first express transportation planning data, wherein the express transportation planning model includes the following model objectives: Among them, TC ij W represents the cost of transporting a unit weight of express from express node i to express node j per kilometer, which is a quantitative value; ij represents the average weight of the express items transported from express node i to express node j, which is a quantitative value; x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; D ij represents the transportation distance from express node i to express node j, which is a quantitative value; OC i represents the average transit cost of each express delivery at express node i, which is a quantitative value; δ k represents the length of time that express order k exceeds the scheduled delivery time, which is a continuous decision variable; b k Indicates whether the express order k exceeds the scheduled delivery time, which is a 0-1 decision variable; overQ i represents the number of express deliveries that exceed the express delivery capacity limit of express delivery node i, which is a continuous decision variable; α represents the express delivery transportation cost coefficient, which is a quantitative value; β represents the express delivery transit cost coefficient, which is a quantitative value; γ represents the penalty cost for express delivery overtime, which is a quantitative value; η represents the penalty cost for express delivery default, which is a quantitative value; It represents the penalty cost per unit of express delivery that exceeds the express delivery capacity limit of the express delivery node, which is a quantitative value; E represents the set of express delivery edges; K represents the set of express delivery orders, where the express deliveries in the same express delivery order have the same starting node and ending node; N represents the set of express delivery nodes.

2. The method according to claim 1, characterized in that The express transportation planning model includes the following model constraints: Among them, x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; p jk represents the shipment volume of express node j for express order k; x jik represents the number of express deliveries from express node j to express node i in express order k, which is an integer decision variable; d jk represents the number of parcels received by courier node j for courier order k; N represents the set of courier nodes; K represents the set of courier orders, where the couriers in the same courier order have the same starting node and ending node.

3. The method according to claim 1, characterized in that The express transportation planning model includes the following model constraints: Among them, x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; Q j represents the upper limit of express delivery processing volume of express delivery node j, which is a fixed quantity; overQ i represents the number of couriers that exceed the express capacity limit of express node i, which is a continuous decision variable; N represents the set of express nodes; K represents the set of express orders, where the couriers in the same express order have the same starting node and ending node.

4. The method according to claim 1, characterized in that: The express transportation planning model includes the following model constraints: Among them, x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; qi j k represents whether there is a courier transported from courier node i to courier node j in courier order k, which is a 0-1 decision variable; B represents an auxiliary decision variable, which is a maximum value; E represents the set of courier transportation edges; K represents the set of courier orders, among which the couriers in the same courier order have the same starting node and ending node.

5. The method according to claim 1, characterized in that The express transportation planning model includes the following model constraints: Among them, x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; L ij Indicates the hierarchical change relationship from express node i to express node j, where 1 indicates upstream transfer, 0 indicates same-level transfer, and 2 indicates downstream transfer. It is quantitative; U ij represents the minimum express delivery volume threshold from express node i to express node j, which is a quantitative value; qi j Indicates whether there is a courier transported from courier node i to courier node j, which is a 0-1 decision variable; q ijk Indicates whether there is a courier transported from courier node i to courier node , in courier order k, which is a 0-1 decision variable; E represents the set of courier transport edges; K represents the set of courier orders, where the couriers in the same courier order have the same starting node and ending node; N max Represents a collection of top-level express nodes.

6. The method according to claim 1, characterized in that The express transportation planning model includes the following model constraints: Among them, δ k It represents the time that the express order k exceeds the scheduled delivery time, which is a continuous decision variable; Indicates that express order k is at express node D k The dispatch time is a continuous decision variable; Indicates that express order k is at express node O k The dispatch time is a continuous decision variable; Indicates that express order k starts from the starting node O k To the terminal node D k The longest transportation time is quantitative; b k Indicates whether the express order k exceeds the scheduled delivery time, which is a 0-1 decision variable; B represents the auxiliary decision variable, which is a maximum value; T jk represents the dispatch time of express order k at express node j, which is a continuous decision variable; T ik represents the dispatch time of express order k at express node i, which is a continuous decision variable; D ij represents the transportation distance from express node i to express node j, which is a fixed quantity; S represents the rated speed of the express transportation vehicle, which is a fixed quantity; H jk represents the transit time of express order k at express node j, which is a fixed quantity; q ijk It indicates whether there is a courier transported from courier node i to courier node j in courier order k, which is a 0-1 decision variable; K represents the set of courier orders, where the couriers in the same courier order have the same starting node and ending node; O represents the set of starting nodes of the courier; D represents the set of ending nodes of the courier; E represents the set of courier transportation edges.

7. The method according to claim 1, characterized in that The express transportation planning model includes the following model constraints: Among them, y ijk represents the state of the collection and distribution of the express in the express order k from the express node i to the express node j, where 1 represents the collection stage, 2 represents the bulk stage, and 0 represents the same-level circulation, which is an integer decision variable; L ij represents the hierarchical change relationship from express node i to express node j, where 1 represents upstream transfer, 0 represents transfer at the same level, and 2 represents downstream transfer, which is quantitative; q ijk Indicates whether there is a courier transported from courier node i to courier node j in courier order k, which is a 0-1 decision variable; y jlk represents the distribution state of the express in express order k from express node j to express node , where 1 represents the collection stage, 2 represents the bulk stage, and 0 represents the same-level circulation. It is an integer decision variable; B represents the auxiliary decision variable, which is the maximum value; q j lk indicates whether there is a courier in courier order k that is transported from courier node j to courier node , which is a 0-1 decision variable; K represents the set of courier orders, where the couriers in the same courier order have the same starting node and ending node; E represents the set of courier transportation edges.

8. The method according to any one of claims 1 to 7, characterized in that: After obtaining the first express transportation planning data, the method further includes: Determine the departure time of each departure shift in each express node according to the node information, determine the upper limit of the transportation time of each express order according to the order information, and determine the transportation distance of the transportation route for transporting each express order; According to the departure time, the upper limit of the transportation time, the transportation distance, and the first express transportation planning data, with the goal of maximizing the time limit achievement rate of each express order, the second express transportation planning data is solved based on the heuristic strategy algorithm, and the second express transportation planning data at least includes the target transportation route for executing each express order within the planning period, the number of express deliveries transported by each express order on the corresponding target transportation route, and the target departure schedule of each express order at the corresponding express node.

9. A data processing device for express transportation planning, characterized in that: The device comprises: An acquisition unit, used to acquire node information of each express node in the express business area, transport edge information between any two express nodes, and order information of express orders between any two express nodes within a planning period; A construction unit, configured to construct an express transportation planning model including model constraints and model objectives based on a mixed integer programming algorithm according to the node information, the transportation edge information, and the order information; A solving unit is used to solve the decision variables in the express transportation planning model to obtain the first express transportation planning data, wherein the express transportation planning model includes the following model objectives: Among them, TC ij W represents the cost of transporting a unit weight of express from express node i to express node j per kilometer, which is a quantitative value; ij represents the average weight of the express items transported from express node i to express node j, which is a quantitative value; x ijk represents the number of express deliveries from express node i to express node j in express order k, which is an integer decision variable; D ij represents the transportation distance from express node i to express node j, which is a quantitative value; OC i represents the average transit cost of each express delivery at express node i, which is a quantitative value; δ k represents the length of time that express order k exceeds the scheduled delivery time, which is a continuous decision variable; b k Indicates whether the express order k exceeds the scheduled delivery time, which is a 0-1 decision variable; overQ i represents the number of express deliveries that exceed the express delivery capacity limit of express delivery node i, which is a continuous decision variable; α represents the express delivery transportation cost coefficient, which is a quantitative value; β represents the express delivery transit cost coefficient, which is a quantitative value; γ represents the penalty cost for express delivery overtime, which is a quantitative value; η represents the penalty cost for express delivery default, which is a quantitative value; It represents the penalty cost per unit of express delivery that exceeds the express delivery capacity limit of the express delivery node, which is a quantitative value; E represents the set of express delivery edges; K represents the set of express delivery orders, where the express deliveries in the same express delivery order have the same starting node and ending node; N represents the set of express delivery nodes.

10. An electronic device, characterized in that: The electronic device includes one or more processors and one or more memories, wherein at least one program code is stored in the one or more memories, and the at least one program code is loaded and executed by the one or more processors to implement the operations performed by the method according to any one of claims 1 to 8.

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