Logistics sorting method, device, equipment and storage medium

Abstract

Embodiments of the present application provide a logistics sorting method, device, equipment and storage medium, and relate to the technical field of logistics. The method generates a sorting target function, a flow direction set and a sorting constraint set, then obtains dual variables, generates a constraint master problem and a constraint sub-problem based on the dual variables, the sorting target function and the sorting constraint set, divides flow directions in a split flow direction candidate set into multi-bin flow directions and single-bin flow directions based on a minimum number of occupied bins, allocates flow directions in a merge flow direction candidate set to remaining bins to obtain an initial solution, calculates an optimal solution of the constraint sub-problem based on the initial solution, updates the constraint master problem, solves the constraint master problem based on a heuristic rule, and iterates multiple times until a flow direction bin allocation result is obtained. Considering the cargo volume and flow direction, the flow direction is merged and split, the rationality of bin allocation is optimized, the reuse rate of the trolley can be fully improved, and thus the sorting capacity and efficiency of the sorting machine are improved.

Description

Technical Field

[0001] This application relates to the field of logistics technology, and in particular to logistics sorting methods, apparatus, equipment and storage media. Background Technology

[0002] As a transit hub in the logistics network, the sorting center's operation mainly includes three stages: unloading, sorting, and loading. In the unloading stage, transport trucks from upstream receiving warehouses, sorting centers, or service outlets arrive at the sorting center (referred to as "inbound"). This stage primarily involves unloading packages from the trucks and allowing them to flow into the sorting area. In the sorting stage, packages are categorized and aggregated based on their destination (referred to as "flow direction"), downstream nodes, and other information. In the subsequent loading stage, packages destined for the same destination within the area are loaded onto trucks before a given deadline and sent to downstream areas (referred to as "outbound"). Automated sorting equipment is introduced into the sorting stage.

[0003] In related technologies, the sorting capacity of automated sorting equipment is related to the inherent attributes of the equipment and the sorting plan. The inherent attributes of the equipment are basically determined after the equipment is put into use. The sorting plan refers to the flow direction and the allocation relationship of the sorting slots. Currently, sorting plans are mostly formulated manually and adjusted based on changes in data such as the flow quantity and the number of packages in the flow direction. This method is not very efficient. Summary of the Invention

[0004] The main objective of this application is to propose a logistics sorting method, apparatus, equipment, and storage medium to improve sorting efficiency and sorting capacity.

[0005] To achieve the above objectives, a first aspect of this application proposes a logistics sorting method, comprising:

[0006] Generate a sorting objective function, a flow direction set, and a sorting constraint set. The sorting constraint set includes multiple sorting constraints, and the flow direction set includes a merged flow direction candidate set and a split flow direction candidate set.

[0007] Obtain the dual variables of the sorting constraints that contain continuous variables in the sorting constraint set; generate the main constraint problem based on the dual variables and the sorting objective function; and generate the sub-constraint problems based on the remaining sorting constraints.

[0008] Calculate the minimum number of grid slots occupied for each flow direction. Based on the minimum number of grid slots occupied, divide the flow directions in the candidate set of split flow directions into multi-grid flow directions and single-grid flow directions. Assign the flow directions in the candidate set of merged flow directions to the remaining grid slots to obtain the initial solution.

[0009] The optimal solution to the constrained subproblem is calculated based on the initial solution. The constrained master problem is updated according to the optimal solution. The constrained master problem is solved based on heuristic rules. The process is repeated multiple times until the iteration termination condition is met, and the flow direction grid allocation result is obtained.

[0010] In some embodiments, the sorting machine includes two logical areas that generate a sorting objective function, including:

[0011] Obtain the volume of goods flowing into each direction;

[0012] Based on whether the flow direction is independently allocated a grid in the logical area, determine the grid variable for each flow direction in different logical areas;

[0013] Based on the flow direction and the grid variables, calculate the first dropping parameter for each flow direction in different logical areas, summarize the first dropping parameters for each flow direction to obtain the second dropping parameter, and minimize the second dropping parameter to obtain the sorting objective function.

[0014] In some embodiments, generating a flow set includes:

[0015] Compare the magnitudes of each flow volume and the split threshold;

[0016] Based on the comparison results, flows greater than the splitting threshold are assigned to the splitting flow candidate set, and flows less than or equal to the splitting threshold are assigned to the merging flow candidate set.

[0017] In some embodiments, generating a set of sorting constraints includes:

[0018] In the candidate set of merged flow directions, a first merged flow direction constraint condition is generated according to a first constraint rule, and a second merged flow direction constraint condition is generated according to a second constraint rule. The first constraint rule is that a flow direction in the candidate set of merged flow directions can only be assigned to one grid. The second constraint rule is that when at least one of the flow directions is assigned to the same grid, the total processing capacity of the grid is not greater than the expected capacity of the grid.

[0019] In the candidate set of split flow directions, a split flow direction constraint condition is generated according to a third constraint rule, wherein the third constraint rule is that the total expected cargo volume of at least one grid allocated to the flow direction in the candidate set of split flow directions is greater than or equal to the cargo volume of the flow direction corresponding to the flow direction.

[0020] A subset of flow direction constraints is generated based on the fourth constraint rule, wherein the fourth constraint rule is that the merged flow direction candidate set and the split flow direction candidate set are not assigned to the same type of grid at the same time, and the merged flow direction candidate set and the split flow direction candidate set are not assigned to the same grid.

[0021] Generate a subset of grid constraints related to the number of flow directions assigned to the grid;

[0022] Generate a subset of flow direction constraints related to whether the flow direction occupies an independent grid;

[0023] Multiple sorting constraints are obtained based on the first merge flow constraint, the second merge flow constraint, the split flow constraint, the subset of flow constraints, the subset of grid constraints, and the subset of flow constraints, and the sorting constraint set is generated.

[0024] In some embodiments, the grid includes merged type grids and split type grids, and the step of generating a subset of flow direction constraints according to the fourth constraint rule includes:

[0025] When the grid is assigned to the split flow candidate set and is a split type grid, the grid variable of the grid is one; otherwise, it is zero. When the grid is assigned to the merge flow candidate set and is a merge type grid, the grid variable of the grid is one; otherwise, it is zero. The first flow constraint condition is generated based on the grid variable.

[0026] When the flow direction is assigned to the grid, a flow direction variable is generated to determine the relationship between the flow direction and the grid. A second flow direction constraint condition is generated based on the flow direction variable and the grid variable. A third flow direction constraint condition is generated based on the flow direction variable, the first preset large number, and the grid variable.

[0027] The flow direction constraint subset is generated based on the first flow direction constraint, the second flow direction constraint, and the third flow direction constraint.

[0028] In some embodiments, generating a subset of grid constraints related to the number of flow directions assigned to the grid includes:

[0029] When the number of flow directions assigned to the grid is one, the number variable of the grid is one; otherwise, it is zero. The first grid constraint condition about the first variable is generated based on the number variable, the second preset large number, and the preset small number.

[0030] When the flow direction is assigned to the grid, a flow direction variable is generated to represent the relationship between the flow direction and the grid. Based on the flow direction variable and the first variable, a second grid constraint condition is generated.

[0031] The grid constraint subset is obtained based on the first grid constraint condition and the second grid constraint condition.

[0032] In some embodiments, generating a subset of flow direction constraints related to whether the flow direction occupies an independent cell includes:

[0033] The second variable is obtained based on the quantity variable and the flow direction variable;

[0034] The first constraint condition is obtained based on the second variable and the flow direction variable;

[0035] The second constraint condition is obtained based on the second variable, the third preset large number, and the flow direction variable;

[0036] The flow direction constraint subset is obtained based on the first constraint and the second constraint.

[0037] To achieve the above objectives, a second aspect of this application provides a logistics sorting device, comprising:

[0038] Constraint construction module: used to generate sorting objective function, flow direction set and sorting constraint set, wherein the sorting constraint set includes multiple sorting constraint conditions and the flow direction set includes merged flow direction candidate set and split flow direction candidate set;

[0039] Problem decomposition module: used to obtain the dual variables of the sorting constraints that contain continuous variables in the sorting constraint set, generate the main constraint problem based on the dual variables and the sorting objective function, and generate the sub-constraint problems based on the remaining sorting constraints;

[0040] Flow direction allocation module: used to calculate the minimum number of grid slots occupied for each flow direction, divide the flow directions in the split flow direction candidate set into multi-grid flow directions and single-grid flow directions based on the minimum number of grid slots occupied, and allocate the flow directions in the merge flow direction candidate set to the remaining grid slots to obtain the initial solution;

[0041] Iterative solution module: used to calculate the optimal solution of the constrained subproblem based on the initial solution, update the constrained main problem according to the optimal solution, solve the constrained main problem based on heuristic rules, iterate multiple times until the iteration termination condition is met, and obtain the flow direction grid allocation result.

[0042] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0043] To achieve the above objectives, a fourth aspect of the present application provides a storage medium that stores a computer program, which, when executed by a processor, implements the method described in the first aspect.

[0044] The logistics sorting method, apparatus, equipment, and storage medium proposed in this application generate a sorting objective function, a set of flow directions, and a set of sorting constraints. Then, it obtains the dual variables of the sorting constraints containing continuous variables in the sorting constraint set. Based on the dual variables and the sorting objective function, it generates a main constraint problem. Based on the remaining sorting constraints, it generates sub-constraint problems. It then calculates the minimum number of occupied grids for each flow direction. Based on the minimum number of occupied grids, it divides the flow directions in the split flow direction candidate set into multi-grid flow directions and single-grid flow directions. It allocates the flow directions in the merge flow direction candidate set to the remaining grids to obtain an initial solution. Finally, it calculates the optimal solution to the sub-constraint problems based on the initial solution, updates the main constraint problem based on the optimal solution, solves the main constraint problem based on heuristic rules, iterates multiple times until the iteration termination condition is met, and obtains the flow direction grid allocation result. This application's embodiments construct a sorting optimization model. When designing the sorting objective function and sorting constraint set, it considers both cargo volume and flow direction, merging and splitting flow directions. Flow directions are divided into a merged flow direction candidate set and a split flow direction candidate set, and sorting optimization is performed based on the partitioning results. Merging flow directions avoids wasting slots and balances the parcel processing volume between slots. By merging and splitting flow directions, the rationality of slot allocation is optimized, significantly improving the reuse rate of carts, thereby enhancing the sorting capacity and efficiency of the sorting machine. Attached Figure Description

[0045] Figure 1 This is a flowchart of the logistics sorting method provided in the embodiments of this application.

[0046] Figure 2 This is an optional flowchart of the logistics sorting method provided in the embodiments of this application.

[0047] Figure 3 This is a flowchart illustrating the generation of the sorting objective function in an embodiment of this application.

[0048] Figure 4 This is a flowchart of generating a sorting constraint set provided in an embodiment of this application.

[0049] Figure 5 This is a subset of flow direction constraints generated according to the fourth constraint rule, provided in the embodiments of this application.

[0050] Figure 6 This is a flowchart illustrating the generation of a subset of grid constraints related to the number of flow directions allocated by grids, as provided in an embodiment of this application.

[0051] Figure 7 This is a schematic diagram illustrating the generation of a subset of flow direction constraints related to whether the flow direction occupies an independent grid, as provided in an embodiment of this application.

[0052] Figure 8This is a flowchart of solving the sorting optimization model based on the heuristic Benders decomposition algorithm provided in the embodiments of this application.

[0053] Figure 9 This is a schematic diagram showing the specific allocation of grid slots and flow direction under the two schemes.

[0054] Figure 10 This is a schematic diagram illustrating the solution efficiency of the logistics sorting method provided in the embodiments of this application.

[0055] Figure 11 This is a structural block diagram of a logistics sorting device provided in another embodiment of this application.

[0056] Figure 12 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] It should be noted that although functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0060] In recent years, with the rise of e-commerce and online retail, the express delivery and logistics industry has experienced rapid development. The explosive growth in parcel volume and the need for high standards of customer service have placed higher demands on the operational efficiency of each link in logistics fulfillment. Among these, sorting centers, as transit hubs in the logistics network, face challenges in improving their efficiency. The operation process of a sorting center mainly includes three stages: unloading, sorting, and loading. In the unloading stage, transport trucks from upstream receiving warehouses, sorting centers, or service outlets arrive at the sorting center (referred to as "inbound"). This stage primarily involves unloading parcels from the trucks and allowing them to flow into the sorting area. In the sorting stage, parcels are classified and aggregated according to their destination (referred to as "flow direction"), downstream nodes, and other information. In the subsequent loading stage, parcels destined for the same destination within the area are loaded onto trucks before a given deadline and sent to downstream locations (referred to as "outbound"). Based on attributes such as weight and size, parcels can be categorized into large and small / medium-sized parcels. Typically, in the sorting process, large packages are sorted manually, while small and medium-sized packages are mainly handled by automated sorting equipment.

[0061] In related technologies, the sorting capacity of automated sorting equipment is related to the inherent attributes of the sorting equipment and the sorting plan. The inherent attributes of the sorting equipment include the number of inlets, cart length, number of compartments, and operating speed, which are basically determined after the equipment is put into operation. The sorting plan refers to the allocation relationship between flow direction and compartments. Currently, sorting plans are mostly formulated manually, and adjusted based on changes in data such as the flow volume and parcel volume of the site operations. This method is difficult to balance sorting capacity and sorting demand, and the following drawbacks occur: for example, allocating a compartment to a high-volume flow direction may cause sorting machine congestion, or allocating a separate compartment to a low-volume flow direction may result in low reuse rates and reduced sorting machine capacity. It is evident that the method of specifying a sorting plan in related technologies cannot fully utilize the sorting machine's sorting capacity, leading to low sorting efficiency and low sorting capacity.

[0062] Based on this, embodiments of this application provide a logistics sorting method, apparatus, equipment, and storage medium. By constructing a sorting optimization model, when designing the sorting objective function and sorting constraint set, the volume and flow direction are considered. The flow direction is merged and split, dividing the flow direction into a merged flow direction candidate set and a split flow direction candidate set. Sorting optimization is performed based on the division results. Merging flow directions can avoid wasting slots and balance the parcel processing volume between slots. By merging and splitting flow directions, the rationality of slot allocation is optimized, which can significantly improve the reuse rate of carts, thereby increasing the sorting capacity and efficiency of the sorting machine.

[0063] This application provides a logistics sorting method, apparatus, equipment, and storage medium, which are specifically described through the following embodiments. First, the logistics sorting method in this application embodiment is described.

[0064] The logistics sorting method provided in this application relates to the field of logistics technology. This method can be applied to a terminal, a server, or a computer program running on either the terminal or the server. For example, the computer program can be a native program or software module in an operating system; it can be a native application (APP), i.e., a program that needs to be installed in the operating system to run, such as a client supporting logistics sorting; it can also be a mini-program, i.e., a program that only needs to be downloaded to a browser environment to run; or it can be a mini-program that can be embedded in any APP. In short, the above-mentioned computer program can be any form of application, module, or plugin. The terminal communicates with the server via a network. This logistics sorting method can be executed by the terminal or the server, or by the terminal and the server working together.

[0065] In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, or smartwatch, etc. The server can be a standalone server, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms; it can also be a service node in a blockchain system, where the service nodes form a peer-to-peer (P2P) network. The P2P protocol is an application layer protocol running on top of the Transmission Control Protocol (TCP). The terminal and server can connect via Bluetooth, Universal Serial Bus (USB), or a network, etc., and this embodiment does not impose any limitations.

[0066] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0067] The sorting machine in this embodiment is a circular cross sorting machine. First, the circular cross sorting machine in this embodiment will be described.

[0068] Reference Figure 1 , Figure 1 This is a schematic diagram of the principle of the circular cross sorting machine provided in the embodiments of this application.

[0069] Figure 1 The circular cross sorting machine includes , Two inlet ports and , Two logical areas. Along the transport direction of the conveyor belt, Logical area coverage import port To the inlet The area where the grids are located, Logical area coverage import port To the inlet The area where the grids are located.

[0070] For circular cross-type sorting machines, the evaluation metrics are sorting capacity and reuse rate. When packages enter from the inlet... Import, if the flow direction corresponding to the package is in If a logical area has been allocated a corresponding slot, the package will fall into that slot. The logical area completes the unloading process. Otherwise, the package is transported by cart to... Logic area, in The logically assigned slots have completed the unloading process.

[0071] The drop ratio is a key factor affecting the sorting capacity of a sorting machine. Assuming a given inlet... The imported quantity is Among them, the logic area is set. The proportion of goods unloaded is Import port The imported quantity is Set the logical area The proportion of goods unloaded is The calculated sorting capacity The representation is:

[0072]

[0073] in, A sorting time period is defined as a time interval in seconds. ; It is the length of the car. This is the speed of the car, measured in units of... ; It is the actual utilization rate of the circular cross sorter, that is, the ratio of actual sorting capacity to theoretical sorting capacity. It is affected by actual operating factors such as equipment maintenance and usage cycle, so it is set according to the actual situation. It is the reuse rate, which is calculated as follows:

[0074]

[0075] in, Indicates flow through the logic area The volume of goods, Indicates flow through the logic area The volume of goods.

[0076] At this point, assuming , Packages at the inlet are supplied from the same package pool. For each flow direction, the two inlets complete the loading process indiscriminately. Therefore, it is assumed that different flows pass through the two inlets. , The probability of each package entering the sorting machine is the same. Under this assumption, maximizing sorting capacity is equivalent to maximizing the number of packages dropped into the corresponding logical area from the inlet, meaning the following relationship exists:

[0077]

[0078] Next, based on the information about the circular cross sorting machine mentioned above, the logistics sorting method in this application embodiment is described. When performing logistics sorting in this application embodiment, the following assumptions are met based on the results of actual research: 1) It is assumed that the sorting machine operates normally, without considering special circumstances such as equipment failure or maintenance; 2) It is assumed that packages can be sorted and unloaded according to the sorting plan, without considering situations such as missorting, omissions, or damage; 3) It is assumed that when the amount of goods allocated to the grid within a unit of time does not exceed the grid's processing capacity, it will not cause congestion of the sorting machine; 4) It is assumed that different flow directions pass through two inlets. , The probability of entering the sorting machine is the same.

[0079] Figure 2 This is an optional flowchart of the logistics sorting method provided in the embodiments of this application. Figure 2 The method may include, but is not limited to, steps 110 to 140. It is also understood that this embodiment... Figure 2 The order of steps 110 to 140 is not specifically limited. The order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0080] Step 110: Generate the sorting objective function, the flow direction set, and the sorting constraint set. The sorting constraint set includes multiple sorting constraints, and the flow direction set includes the merged flow direction candidate set and the split flow direction candidate set.

[0081] In one embodiment, a sorting optimization model is constructed to automatically generate the optimal sorting plan and obtain the correspondence between flow direction and grid. The sorting optimization model includes a sorting objective function and a set of sorting constraints, and the set of sorting constraints includes multiple sorting constraints.

[0082] The following describes the construction process of the sorting optimization model in the embodiments of this application.

[0083] In one embodiment, reference is made to Figure 3 , Figure 3 This is a flowchart of generating the sorting objective function in the embodiments of this application, specifically including steps 310 to 330:

[0084] Step 310: Obtain the flow volume corresponding to each flow direction.

[0085] Prior to sorting, the volume of goods flowing in different directions per unit time is obtainable prior information. The set of flow directions is represented as follows: , , The number of flows, for each flow direction , Its flow volume is .

[0086] Step 320: Determine the grid variable for each flow direction in different logical areas based on whether the flow direction is independently allocated to a grid in the logical area.

[0087] In one embodiment, the grid variable is used to determine whether a flow direction has been allocated an independent grid in any logical area. If flow direction i has been independently allocated a grid in logical area w, then the grid variable for flow direction i in logical area w... ,otherwise, Where w is the logical region index. It is understood that in the embodiments of this application, the grid variable takes the value of 0 or 1.

[0088] Step 330: Calculate the first drop-off parameter for each flow direction in different logical areas based on the flow direction, quantity, and grid variables. Summarize the first drop-off parameters for each flow direction to obtain the second drop-off parameter. Minimize the second drop-off parameter to obtain the sorting objective function.

[0089] In one embodiment, the sorting objective function refers to the optimization objective of the sorting plan. In this embodiment, the optimization objective is to maximize sorting capacity, which is equivalent to maximizing the number of packages introduced through the inlet and unloaded in the corresponding logical area. That is, the sorting objective function can be expressed as follows:

[0090]

[0091] Due to the sorting time period T, the cart length L, and the cart's running speed Actual utilization rate of the circular cross sorter These parameters are inherent properties of the circular cross sorter; therefore, maximizing sorting capacity is equivalent to minimizing the amount of goods flowing through the logical area. Therefore, this embodiment of the application utilizes the flow direction and grid variables to indicate the amount of goods flowing through the logic area. By balancing sorting demand and sorting capacity, it avoids congestion in the sorting machine. The resulting sorting objective function is expressed as follows:

[0092]

[0093] in, Represents grid variables, If an independent grid is assigned to flow direction i in any logical region w, then the grid variable... Otherwise . This indicates the volume of goods flowing to direction i. This indicates the first delivery parameter of the flow direction i in the logical region w. This indicates the second loading parameter.

[0094] In one embodiment, the sorting objective function may also disregard the merged flow portion, primarily because goods in merged flows require secondary sorting.

[0095] After obtaining the sorting optimization target, the following describes the process of dividing the flow direction into a flow direction set based on the flow direction and the volume of goods in the flow direction.

[0096] In this embodiment of the application, the flow direction set includes a merged flow direction candidate set and a split flow direction candidate set. The purpose of dividing the flow direction set is to maximize the sorting capacity by allocating split flow directions and merged flow directions, thereby balancing the flow direction sorting demand and the sorting machine sorting capacity.

[0097] Considering the limited processing capacity and number of grids, the allocation relationship between grids and flow directions is mainly satisfied in three situations: when the flow volume is large enough, one flow direction occupies multiple grids; when the flow volume is moderate, one flow direction occupies one grid independently; when the flow volume is small, one flow direction shares one grid with other flow directions.

[0098] In one embodiment, the process of generating a flow direction set specifically includes: comparing the magnitude of the cargo volume of each flow direction with the splitting threshold, and according to the comparison result, assigning flows greater than the splitting threshold to the splitting flow direction candidate set, and assigning flows less than or equal to the splitting threshold to the merging flow direction candidate set.

[0099] Specifically, this means: the volume of goods flowing to a location less than or equal to the splitting threshold. The flow direction is taken as the merge flow direction, and all merge flow directions constitute the candidate set of merge flow directions. ,in, Includes all flows of goods with a volume less than or equal to The direction of flow, that is The flow of goods will exceed the splitting threshold. The direction of the flow is taken as the splitting flow direction, and all splitting flow directions constitute the candidate set of splitting flow directions. ,in, Includes all destinations with a volume greater than The direction of flow, that is ,and .

[0100] It is understandable that, considering the number of grids, grid processing capacity, and flow requirements, the candidate set of flow directions should be merged. A flow direction can be merged with other flows and assigned to a single cell, or it can be assigned a separate cell. Split flow direction candidate set. A flow direction can be assigned to multiple cells or only one cell. When a flow direction is assigned to only one cell, that cell is called an independent cell; when the flow direction is in... and Each logical area is allocated a grid, and the corresponding grid is called a multiplexed grid.

[0101] The process of generating the sorting constraint set is described below.

[0102] In one embodiment, reference is made to Figure 4 , Figure 4This is a flowchart of generating a sorting constraint set provided in an embodiment of this application, specifically including steps 410 to 460:

[0103] Step 410: In the candidate set of merged flows, generate the first merged flow constraint condition according to the first constraint rule, and generate the second merged flow constraint condition according to the second constraint rule.

[0104] In one embodiment, the first constraint rule is that a flow direction in the candidate set of merged flow directions can only be assigned one grid cell. Therefore, the first merged flow direction constraint condition is expressed as:

[0105]

[0106] in, This represents the flow direction variable, which takes the value 0 or 1. Specifically, , If the merged flow is in the candidate set, the flow direction will be... Assigned to grid ,but ;otherwise, As can be seen, the first merging constraint is used to restrict the flow direction in the candidate set of merging flows to be assigned only one grid.

[0107] In one embodiment, the second constraint rule is that when at least one flow direction is assigned to the same grid, the total throughput of the grid does not exceed the grid's expected throughput. Therefore, the second merged flow direction constraint is expressed as:

[0108]

[0109] Where C represents the expected quantity of goods in the grid, and J represents the set of grids. , The number of grid points. If grid point j is assigned to multiple flow directions from the candidate set for merging flows, then if... This indicates that flow direction i is allocated to grid j. This represents the processing volume of goods flowing from grid j to grid i. The sum of all flows allocated to grid j is then obtained. , represents the total volume of goods processed by grid j. For grid j, the total volume of goods processed cannot exceed its processing capacity, which is the expected volume of goods.

[0110] It is understood that the expected quantity of goods in different compartments may be the same or different, and this embodiment does not limit this.

[0111] Step 420: Generate split flow direction constraints in the candidate set of split flow directions according to the third constraint rule.

[0112] In one embodiment, the third constraint rule is that the total expected cargo volume of at least one cell in the candidate set of split flow directions is greater than or equal to the cargo volume of the corresponding flow direction. Therefore, the split flow direction constraint condition is expressed as:

[0113]

[0114] The purpose of the split flow direction constraint is to ensure that the number of slots allocated to flow direction i in the candidate set of split flow directions can cover its flow direction cargo volume. In other words, if a flow direction is allocated multiple slots, the sum of the expected cargo volumes of these slots must be at least greater than the flow direction cargo volume corresponding to that flow direction.

[0115] Step 430: Generate a subset of flow direction constraints based on the fourth constraint rule.

[0116] In one embodiment, the fourth constraint rule is that the merged flow candidate set and the split flow candidate set are not assigned the same type of grid at the same time, and the merged flow candidate set and the split flow candidate set are not assigned the same grid. The overall constraint representation of the flow constraint condition subset is as follows:

[0117]

[0118] The purpose of this subset of flow direction constraints is to ensure that flows from the split flow direction candidate set and flows from the merge flow direction candidate set do not share the same grid.

[0119] To reduce the computational workload of the overall constraints mentioned above, grid variables are introduced for simplification. (Refer to...) Figure 5 , Figure 5 The present application provides a method for generating a subset of flow direction constraints based on the fourth constraint rule, including steps 510 to 530:

[0120] Step 510: When a grid is assigned to the split flow candidate set and is a split type grid, the grid variable of the grid is one; otherwise, it is zero. When a grid is assigned to the merge flow candidate set and is a merge type grid, the grid variable of the grid is one; otherwise, it is zero. Generate the first flow constraint condition based on the grid variable.

[0121] In one embodiment, the grid type is either a merge type grid or a split type grid. If grid j is assigned to the split flow candidate set... And in the split flow to the candidate set In this case, the grid cell corresponds to the same flow direction as other grid cells, and is a split-type grid cell. At this time, the grid cell variable... ,otherwise, If grid j is assigned to the merge flow candidate set And in merging the flow to the candidate set In this case, the grid corresponds to multiple flow directions and is a merged type grid. At this time, the grid variable... ,otherwise, .

[0122] The first flow constraint condition generated based on the grid variables is expressed as follows:

[0123]

[0124] in, Represents grid variables, The purpose is to indicate that a grid can only be either a merge type grid or a split type grid, that the merge flow candidate set and the split flow candidate set are not assigned to the same type of grid at the same time, and that the merge flow candidate set and the split flow candidate set are not assigned to the same grid.

[0125] Step 520: When the flow direction is assigned to the grid, generate the flow direction variable that determines the relationship between the flow direction and the grid. Generate the second flow direction constraint condition based on the flow direction variable and the grid variable, and generate the third flow direction constraint condition based on the flow direction variable, the first preset large number, and the grid variable.

[0126] Among them, for the merged flow candidate set and split flow to candidate set The included flow direction, when assigned to a grid, generates a flow direction variable that establishes the relationship between the flow direction and the grid. The second flow direction constraint is then expressed as:

[0127]

[0128] The third flow direction constraint is expressed as follows:

[0129]

[0130] in, The first preset large number is a very large positive number set based on the actual situation. k can represent the candidate set to be merged. It can also represent the split flow towards the candidate set. For each grid point j, when flow direction i is assigned to grid point j, the flow direction variable... If the cell is a split-type cell assigned in the split flow set, or a merge-type cell in the merge flow set, then The two constraints mentioned above can constrain the split flow to the candidate set. The flow direction is assigned to the split-type grid, and the candidate set of the flow direction is constrained and merged. The flow direction is assigned to the merged type grid.

[0131] Step 530: Generate a subset of flow direction constraints based on the first flow direction constraint, the second flow direction constraint, and the third flow direction constraint.

[0132] Step 440: Generate a subset of grid constraints related to the number of flow directions assigned to the grid.

[0133] In one embodiment, a subset of grid constraints is used to constrain the number of flow directions assigned to each grid, and the overall constraint is expressed as:

[0134]

[0135] in, This represents the number of flow directions assigned to grid j.

[0136] In one embodiment, it is described in detail, referring to Figure 6 , Figure 6 This is a flowchart illustrating the generation of a subset of grid constraints related to the number of flow directions in grid allocation, provided in an embodiment of this application. Specifically, it includes steps 610 to 630:

[0137] Step 610: When the number of flow directions assigned to the grid is one, the number variable of the grid is one; otherwise, it is zero. Generate the first grid constraint condition about the first variable based on the number variable, the second preset large number, and the preset small number.

[0138] Each grid may be assigned to one or more flow directions; therefore, an indicator function is set as the quantity variable for each grid, expressed as:

[0139]

[0140] in, This represents the quantity variable of grid j. For indicator functions, when hour, ,otherwise .

[0141] Next, the first grid constraint condition for the first variable, generated based on the quantity variable, the second preset large number, and the preset small number, is expressed as follows:

[0142]

[0143]

[0144] in, Indicates the first variable, `m` represents the second preset large number, a very large positive number set according to the actual situation, and `m` represents the preset small number, a very small positive number set according to the actual situation. This constraint is used to ensure that the quantity variable corresponding to the grid cannot be too large or too small.

[0145] Step 620: When the flow direction is assigned to a grid, generate the flow direction variable that represents the relationship between the flow direction and the grid, and generate the second grid constraint condition based on the flow direction variable and the first variable.

[0146] In one embodiment, the constraint condition for the second grid opening is expressed as:

[0147]

[0148]

[0149] This constraint is used to ensure that the total number of flow directions assigned to grid j is not greater than the corresponding quantity variable, and takes into account the possibility of negative numbers during the calculation process.

[0150] Step 630: Obtain the grid constraint subset based on the first grid constraint condition and the second grid constraint condition.

[0151] Step 450: Generate a subset of flow direction constraints related to whether the flow direction occupies an independent cell.

[0152] In one embodiment, the flow direction constraint subset is used to reflect whether the flow direction occupies a grid space independently in the logic area, and the overall constraint is expressed as:

[0153]

[0154]

[0155] in, Representation of logical area The set of grids, Grid Located in the logic area Inside , Indicates flow direction It occupies a separate compartment. , The third preset large number is a very large positive number set according to the actual situation.

[0156] Since the aforementioned overall constraint is a nonlinear constraint, a linearization technique is used to transform it into a linear constraint. In one embodiment, referring to... Figure 7 , Figure 7 This is a schematic diagram illustrating the generation of a subset of flow direction constraints related to whether the flow direction occupies an independent grid, provided in an embodiment of this application. Specifically, it includes steps 710 to 740:

[0157] Step 710: Obtain the second variable based on the quantity variable and the flow variable.

[0158] In one embodiment, the second variable is represented as:

[0159]

[0160] Step 720: Obtain the first constraint condition based on the second variable and the flow direction variable.

[0161] In one embodiment, due to This represents the flow direction variable, which takes the value 0 or 1. Specifically, , If the merged flow is in the candidate set, the flow direction will be... Assigned to grid ,but ;otherwise, And when hour, ,otherwise The second variable satisfies the first flow direction constraint, expressed as:

[0162]

[0163]

[0164]

[0165] Step 730: Obtain the second constraint condition based on the second variable, the third preset large number, and the flow direction variable.

[0166] In one embodiment, the second flow direction constraint is expressed as:

[0167]

[0168]

[0169] Understandable, The third preset large number indicates that flow direction i has an independent cell j in the logic area w, which means that cell j is only allocated to flow direction i. However, flow direction i can include multiple cells. The second flow direction constraint relationship is used to constrain flow direction i to occupy a cell in the logic area when it occupies a cell alone, so that it will not occupy too many cells.

[0170] Step 740: Obtain the flow direction constraint subset based on the first and second constraints.

[0171] Step 460: Based on the first merge flow direction constraint, the second merge flow direction constraint, the split flow direction constraint, the flow direction constraint subset, the grid constraint subset, and the flow direction constraint subset, multiple sorting constraints are obtained, and a sorting constraint set is generated.

[0172] The sorting constraint set is represented as follows:

[0173]

[0174]

[0175]

[0176]

[0177]

[0178]

[0179]

[0180]

[0181]

[0182]

[0183]

[0184]

[0185]

[0186]

[0187]

[0188]

[0189]

[0190] In one embodiment, the sorting constraint set further includes the following parameter constraint relationships:

[0191]

[0192]

[0193]

[0194]

[0195]

[0196]

[0197] The sorting optimization model is obtained through the above steps. This model includes a sorting objective function, a flow direction set, and a sorting constraint set. The optimization solution process for the sorting optimization model is described below.

[0198] Step 120: Obtain the dual variables of the sorting constraints that contain continuous variables in the sorting constraint set, generate the main constraint problem based on the dual variables and the sorting objective function, and generate the sub-constraint problems based on the remaining sorting constraints.

[0199] In one embodiment, the sorting optimization model contains a large number of variables and has low solution efficiency. Therefore, heuristic rules are formulated based on the characteristics of the model, and the sorting optimization model is solved based on the heuristic Benders decomposition algorithm.

[0200] The heuristic Benders decomposition algorithm, when solving the problem, first divides the set of sorting constraints according to whether the variables are continuous. If a variable takes any value within a certain range, then this variable can be considered a continuous variable, such as the one mentioned above. If a variable takes the value of an integer, then that variable can be treated as an integer variable, as mentioned above. The sorting constraints in the sorting constraint set are classified according to variables, resulting in at least one sorting constraint containing continuous variables and at least one sorting constraint containing integer variables.

[0201] Next, for sorting constraints containing continuous variables, dual variables are introduced to transform them into an equivalent but more easily solvable form. The method for generating dual variables follows the basic steps of the heuristic Benders decomposition algorithm. For example, based on the sorting objective function and the corresponding sorting constraints, a new optimization problem is constructed as the main constraint problem. This problem contains only dual variables and a dual objective function, where the dual objective function is a linear combination of sorting objective functions, and the dual constraints are linear combinations of their corresponding sorting constraints. After generating the main constraint problem (MP) based on the dual variables and the sorting objective function, the remaining sorting constraints are used as the constraint subproblems (SP).

[0202] Step 130: Calculate the minimum number of grid cells occupied for each flow direction. Based on the minimum number of grid cells occupied, divide the flow directions in the split flow direction candidate set into multi-grid flow directions and single-grid flow directions. Assign the flow directions in the merge flow direction candidate set to the remaining grid cells to obtain the initial solution.

[0203] Reference Figure 8 , Figure 8 This is a flowchart of solving the sorting optimization model based on the heuristic Benders decomposition algorithm provided in the embodiments of this application.

[0204] Combination Figure 8 First, initialize the output. Then, sort the flow directions from largest to smallest based on the flow direction and quantity, and then sort them according to the flow direction and quantity. Expected cargo volume of Hegekou calculate The minimum number of grid slots occupied for each flow direction is expressed as:

[0205]

[0206] in, This indicates the minimum number of grid slots required for flow direction i.

[0207] Next, the split flow will be allocated to the candidate set. The flow direction within.

[0208] Specifically, the candidate set of split flow directions is determined based on the minimum number of grid slots occupied for each flow direction. The flow direction in the middle is divided into and ,in, This includes multi-cell flow directions, which contain the minimum number of cells required for each flow direction. Each compartment It includes single-cell flow directions, and each flow direction is assigned to only one cell.

[0209] Understandably, during the allocation process, priority is given to using the grids with smaller numbers, and efforts are made to ensure a balanced distribution of goods between the two logical areas. Therefore, the distribution volume between the two logical areas is calculated based on the sorting objective function, expressed as follows:

[0210]

[0211] in, This indicates the amount of goods dropped into logical area A. This indicates the quantity of goods dropped into logic area B. This represents the grid variables in logic area A. This represents the grid variables in the B logic area.

[0212] Update grid variables during the calculation of the unloading volume in the two logical zones. Number of flow directions allocated to grids and flow direction variables Decision variables, including those mentioned above.

[0213] Next, the merge flow will be allocated to the candidate set. The flow direction in the split flow direction candidate set After the flow direction is assigned, the remaining grid cells are obtained, and the candidate flow direction set will be merged. The flow direction is allocated to the remaining cells, and during the allocation process, the flow direction allocated to each cell must satisfy the following:

[0214]

[0215] Meanwhile, the merged flow is directed to the candidate set. When using grids in the middle flow direction, the allocation result should minimize the loss value of the sorting objective function. If there are remaining grids, the candidate set for split flow direction should be adjusted further. The flow direction within.

[0216] By following the steps above, a high-quality initial solution can be obtained, which includes the correspondence between the flow direction and the grid.

[0217] Step 140: Calculate the optimal solution of the constraint subproblem based on the initial solution, update the constraint master problem according to the optimal solution, solve the constraint master problem based on heuristic rules, iterate multiple times until the iteration termination condition is met, and obtain the flow direction grid allocation result.

[0218] In one embodiment, after obtaining the initial solution, an iterative process is required based on the initial solution. First, the convergence threshold is initialized, and the initial solution generated by the above process is input into the model after heuristic Benders decomposition algorithm to calculate the optimal solution of the constrained subproblem SP, the decision variables, and the objective function value corresponding to the sorting objective function. Next, based on the optimal solution of the constrained subproblem SP, the optimal cut is generated and added to the constrained main problem. In the middle, the constraint master problem MP is updated. Then, the solution grid variables are adjusted according to heuristic rules. Number of flow directions allocated to grids and flow direction variables Solve the constrained master problem MP and calculate the current objective function value. .

[0219] The heuristic rule here is specifically described as follows: update the convergence threshold, for The flow direction can be further optimized if the candidate flow direction set can be merged further. The flow directions in the middle are merged to form If the flow direction allocation of the grid increases the value of the sorting objective function, then the grid is reallocated, and the optimal solution of the constraint subproblem SP is updated; otherwise, the algorithm terminates and outputs the flow direction and grid allocation scheme.

[0220] If during the calculation process, , If the set loss value is very small, the iteration termination condition is considered met, and the Benders decomposition algorithm is stopped; otherwise, the iteration continues.

[0221] After the iteration terminates, the current flow direction grid allocation result is obtained. The flow direction grid allocation result includes the grid corresponding to each flow direction, and logistics sorting can be performed according to the flow direction grid allocation result.

[0222] In one specific embodiment, a numerical experiment was conducted based on real data from a large sorting center. The sorting center's circular cross-sorter contains two logical zones, A and B. Logical zone A contains 100 slots, and logical zone B contains 100 slots. Data from a specific month was selected, representing 146 flow directions. Based on actual operational conditions, the expected throughput C = 150 (pieces / hour), the splitting threshold δ = 80 (pieces / hour), and the equipment utilization rate were set as follows: =85%, the length of the trolley is L=0.6m, and the speed of the trolley is V=2m / s.

[0223] Compared to manual methods in related technologies, the sorting capacity of the logistics sorting method provided in this application embodiment is increased from 14,895 (pieces / hour) to 18,607 (pieces / hour), and the reuse rate is increased from 1.460 to 1.824.

[0224] Reference Figure 9 , Figure 9 This is a schematic diagram showing the specific allocation of grid openings and flow directions under two different schemes. (Combined with...) Figure 9 As can be seen from the data, compared to manual sorting plans, the number of merged and split flow directions in the logistics sorting method of this application embodiment is significantly increased. In related technologies, manual sorting schemes do not merge flow directions primarily to avoid secondary sorting; in actual operation, merged flow directions are adjusted as needed. However, because flow directions with small cargo volumes independently occupy slots, and the number of slots is limited, flow directions with large cargo volumes cannot be allocated enough slots, potentially leading to slot congestion in actual operation. This application embodiment, when optimizing the sorting scheme, allows for flow direction merging, providing greater decision-making optimization space and balancing the parcel processing volume between slots. Furthermore, it allows for reasonable flow splitting, occupying multiple slots, and optimizes the rationality of slot allocation in two logical areas, which can significantly improve the reuse rate of the cart during one cycle, thereby increasing the sorting capacity of the sorting machine.

[0225] In one embodiment, reference is made to Figure 10 , Figure 10 This is a schematic diagram illustrating the solution efficiency of the logistics sorting method provided in this application embodiment. Based on actual sorting scenarios, For the number of flows, To determine the number of compartments, a splitting threshold δ=100 was set, the expected cargo volume C=150, the number of flow directions was selected from [50, 200], and the number of compartments was selected from [50, 250]. Ten test cases were generated. The comparison results are as follows: Figure 10 Among them, indicators ,in, The objective function value obtained by the heuristic decomposition algorithm in the embodiments of this application is... This refers to the objective function value obtained directly using Gurobi. (Indicator) This reflects the efficiency improvement of the heuristic Benders decomposition algorithm in this application compared to the direct solution using Gurobi, where... The solution time required for Gurobi. This figure represents the solution time required by the heuristic Benders decomposition algorithm in this embodiment. It is evident that, in terms of solution quality, the heuristic Benders decomposition algorithm in this embodiment produces higher quality solutions; compared to the Gurobi solution, the gap in the 10 tested cases is close to 0. In terms of solution efficiency, the heuristic Benders decomposition algorithm in this embodiment is highly efficient. For flow directions with a scale of less than 100, results are obtained within 1 second; for larger flow directions, a high-quality solution is obtained within a few seconds. Compared to the direct Gurobi solution, the solution speed is improved by tens or even thousands of times. Therefore, compared to the Gurobi solution, the heuristic Benders decomposition algorithm in this embodiment is superior.

[0226] Compared to the manual sorting plan formulation of related technologies' circular cross-belt sorting machines, the embodiments of this application propose a flow allocation strategy of splitting and merging flow directions. Flow directions with large cargo volumes can be allocated to multiple slots, while flow directions with small cargo volumes can be merged and allocated to a single slot. By balancing the allocation of splitting and merging flow directions, sorting capacity is maximized, balancing the direct contradiction between flow direction sorting demand and sorting machine capacity. When solving the sorting optimization model, the characteristics of sorting operations are fully explored, and heuristic rules are used in conjunction with the Benders decomposition algorithm to improve the solution speed.

[0227] The technical solution provided in this application generates a sorting objective function, a set of flow directions, and a set of sorting constraints. Then, it obtains the dual variables of the sorting constraints containing continuous variables in the sorting constraint set. Based on the dual variables and the sorting objective function, it generates a main constraint problem. Based on the remaining sorting constraints, it generates sub-constraint problems. Next, it calculates the minimum number of grid slots required for each flow direction. Based on the minimum number of grid slots required, it divides the flow directions in the split flow direction candidate set into multi-grid flow directions and single-grid flow directions. It then allocates the flow directions in the merge flow direction candidate set to the remaining grid slots, obtaining an initial solution. Finally, it calculates the optimal solution to the sub-constraint problems based on the initial solution, updates the main constraint problem based on the optimal solution, and solves the main constraint problem based on heuristic rules. This process is repeated multiple times until the iteration termination condition is met, resulting in the flow direction grid allocation result. This application's embodiment constructs a sorting optimization model. When designing the sorting objective function and the set of sorting constraints, it considers both cargo volume and flow direction, merging and splitting flow directions, dividing them into merged flow direction candidate sets and split flow direction candidate sets, and performing sorting optimization based on the division results. Merging flow directions can avoid wasting slots and balance the parcel processing volume between slots. By merging and splitting flow directions, the allocation of slots can be optimized, significantly improving the reuse rate of carts and thus increasing the sorting capacity and efficiency of the sorting machine.

[0228] This application also provides a logistics sorting device that can implement the above-described logistics sorting method, referring to... Figure 11 The device includes:

[0229] Constraint Construction Module 1110: Used to generate the sorting objective function, flow direction set, and sorting constraint set. The sorting constraint set includes multiple sorting constraints, and the flow direction set includes the merged flow direction candidate set and the split flow direction candidate set.

[0230] Problem decomposition module 1120: Used to obtain the dual variables of sorting constraints containing continuous variables in the sorting constraint set, generate the main constraint problem based on the dual variables and the sorting objective function, and generate the sub-constraint problems based on the remaining sorting constraints.

[0231] Flow direction allocation module 1130: used to calculate the minimum number of grid slots occupied for each flow direction, divide the flow directions in the split flow direction candidate set into multi-grid flow directions and single-grid flow directions based on the minimum number of grid slots occupied, and allocate the flow directions in the merge flow direction candidate set to the remaining grid slots to obtain the initial solution.

[0232] Iterative solution module 1140: It is used to calculate the optimal solution of the constraint subproblem based on the initial solution, update the constraint main problem according to the optimal solution, solve the constraint main problem based on heuristic rules, iterate multiple times until the iteration termination condition is met, and obtain the flow direction grid allocation result.

[0233] The specific implementation of the logistics sorting device in this embodiment is basically the same as the specific implementation of the logistics sorting method described above, and will not be repeated here.

[0234] This application also provides an electronic device, including:

[0235] At least one memory;

[0236] At least one processor;

[0237] At least one program;

[0238] The program is stored in a memory, and the processor executes the at least one program to implement the logistics sorting method described above. The electronic device can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), in-vehicle computers, etc.

[0239] Please see Figure 12 , Figure 12 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0240] The processor 1201 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0241] The memory 1202 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1202 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1202 and is called and executed by the processor 1201 to execute the logistics sorting method of the embodiments of this application.

[0242] The input / output interface 1203 is used to implement information input and output;

[0243] Communication interface 1204 is used to enable communication and interaction between this device and other devices. Communication can be achieved via wired means (e.g., USB, Ethernet cable) or wireless means (e.g., mobile network, Wi-Fi, Bluetooth).

[0244] Bus 1205 transmits information between various components of the device (e.g., processor 1201, memory 1202, input / output interface 1203, and communication interface 1204);

[0245] The processor 1201, memory 1202, input / output interface 1203 and communication interface 1204 are connected to each other within the device via bus 1205.

[0246] This application embodiment also provides a storage medium that stores a computer program, which, when executed by a processor, implements the above-described logistics sorting method.

[0247] Memory, as a non-transitory storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0248] The logistics sorting method, apparatus, equipment, and storage medium proposed in this application generate a sorting objective function, a set of flow directions, and a set of sorting constraints. Then, it obtains the dual variables of the sorting constraints containing continuous variables in the sorting constraint set. Based on the dual variables and the sorting objective function, it generates a main constraint problem. Based on the remaining sorting constraints, it generates sub-constraint problems. It then calculates the minimum number of occupied grids for each flow direction. Based on the minimum number of occupied grids, it divides the flow directions in the split flow direction candidate set into multi-grid flow directions and single-grid flow directions. It allocates the flow directions in the merge flow direction candidate set to the remaining grids to obtain an initial solution. Finally, it calculates the optimal solution to the sub-constraint problems based on the initial solution, updates the main constraint problem based on the optimal solution, solves the main constraint problem based on heuristic rules, iterates multiple times until the iteration termination condition is met, and obtains the flow direction grid allocation result. This application's embodiments construct a sorting optimization model. When designing the sorting objective function and sorting constraint set, it considers both cargo volume and flow direction, merging and splitting flow directions. Flow directions are divided into a merged flow direction candidate set and a split flow direction candidate set, and sorting optimization is performed based on the partitioning results. Merging flow directions avoids wasting slots and balances the parcel processing volume between slots. By merging and splitting flow directions, the rationality of slot allocation is optimized, significantly improving the reuse rate of carts, thereby enhancing the sorting capacity and efficiency of the sorting machine.

[0249] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0250] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0251] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0252] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0253] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0254] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0255] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0256] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0257] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0258] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0259] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method of sorting items, characterized by, The sorting machine comprises two logical areas, and the method comprises: obtaining a flow volume corresponding to each flow direction, determining a bin variable of each flow direction in different logical areas according to whether the flow direction is independently allocated to a bin in the logical area, calculating a first landing parameter of each flow direction in different logical areas based on the flow volume and the bin variable, obtaining a second landing parameter by summarizing the first landing parameter of each flow direction, and minimizing the second landing parameter to obtain a sorting target function; generating a flow direction set, the flow direction set comprising a merging flow direction candidate set and a splitting flow direction candidate set; in the merging flow direction candidate set, generating a first merging flow direction constraint condition according to a first constraint rule and generating a second merging flow direction constraint condition according to a second constraint rule, the first constraint rule being that a flow direction in the merging flow direction candidate set can be allocated to only one bin, and the second constraint rule being that when at least one flow direction is allocated to the same bin, the total processing volume of the bin is not greater than the expected volume of the bin; in the splitting flow direction candidate set, generating a splitting flow direction constraint condition according to a third constraint rule, the third constraint rule being that the total expected volume of at least one bin allocated to the flow direction in the splitting flow direction candidate set is greater than or equal to the flow volume corresponding to the flow direction; generating a flow direction constraint condition subset according to a fourth constraint rule, the fourth constraint rule being that the merging flow direction candidate set and the splitting flow direction candidate set do not allocate the same type of bin at the same time, and the merging flow direction candidate set and the splitting flow direction candidate set do not allocate the same bin; generating a bin constraint subset related to the number of flow directions allocated to the bin; generating a flow direction constraint subset related to whether the flow direction occupies an independent bin; obtaining a plurality of sorting constraint conditions according to the first merging flow direction constraint condition, the second merging flow direction constraint condition, the splitting flow direction constraint condition, the flow direction constraint condition subset, the bin constraint subset and the flow direction constraint subset to generate a sorting constraint set; obtaining a dual variable of the sorting constraint condition containing a continuous variable in the sorting constraint set, generating a constraint master problem based on the dual variable and the sorting target function, and generating a constraint sub-problem according to the remaining sorting constraint conditions; calculating the minimum number of bins occupied by each flow direction, dividing the flow direction in the splitting flow direction candidate set into a multi-bin flow direction and a single-bin flow direction based on the minimum number of bins, and allocating the flow direction in the merging flow direction candidate set to the remaining bins to obtain an initial solution; calculating an optimal solution of the constraint sub-problem based on the initial solution, updating the constraint master problem according to the optimal solution, solving the constraint master problem based on a heuristic rule, and iterating multiple times until an iteration termination condition is met to obtain a flow direction-bin allocation result.

2. The logistics sorting method according to claim 1, characterized in that, generating a flow direction set, comprising: comparing the size relationship between each flow volume and a splitting threshold; according to the comparison result, dividing the flow direction greater than the splitting threshold to the splitting flow direction candidate set, and dividing the flow direction less than or equal to the splitting threshold to the merging flow direction candidate set.

3. The logistics sorting method according to claim 1, characterized in that, The slots include merging type slots and splitting type slots, the generating the subset of flow direction constraints according to the fourth constraint rule includes: when the slot is assigned to the splitting flow direction candidate set and is the splitting type slot, a slot variable of the slot is one, otherwise is zero, when the slot is assigned to the merging flow direction candidate set and is the merging type slot, the slot variable of the slot is one, otherwise is zero, generating a first flow direction constraint according to the slot variable; when the flow direction is assigned to the slot, generating a flow direction variable of the relationship between the flow direction and the slot, generating a second flow direction constraint according to the flow direction variable and the slot variable, and generating a third flow direction constraint according to the flow direction variable, a first preset large number and the slot variable; generating the subset of flow direction constraints according to the first flow direction constraint, the second flow direction constraint and the third flow direction constraint.

4. The logistics sorting method according to claim 3, characterized in that, The generating the subset of slot constraints related to the number of flow directions assigned to the slot includes: when the number of flow directions assigned to the slot is one, a number variable of the slot is one, otherwise is zero, generating a first slot constraint on a first variable according to the number variable, a second preset large number and a preset small number; when the flow direction is assigned to the slot, generating a flow direction variable of the relationship between the flow direction and the slot, generating a second slot constraint based on the flow direction variable and the first variable; generating the subset of slot constraints according to the first slot constraint and the second slot constraint.

5. The logistics sorting method according to claim 4, characterized in that, The generating the subset of flow direction constraints related to whether the flow direction occupies an independent slot includes: generating a second variable according to the number variable and the flow direction variable; generating a first constraint condition according to the second variable and the flow direction variable; generating a second constraint condition according to the second variable, a third preset large number and the flow direction variable; generating the subset of flow direction constraints according to the first constraint condition and the second constraint condition.

6. A logistics sorting apparatus characterized by, The sorting machine includes two logical areas, and the device includes: The constraint construction module is configured to: acquire a flow volume corresponding to each flow direction, determine a bin variable of each flow direction in different logical zones according to whether the flow direction is independently allocated to a bin, calculate a first landing parameter of each flow direction in different logical zones based on the flow volume and the bin variable, obtain a second landing parameter by aggregating the first landing parameter of each flow direction, and minimize the second landing parameter to obtain a sorting target function; generate a flow direction set, the flow direction set including a merging flow direction candidate set and a splitting flow direction candidate set; in the merging flow direction candidate set, generate a first merging flow direction constraint condition according to a first constraint rule and generate a second merging flow direction constraint condition according to a second constraint rule, the first constraint rule being that a flow direction in the merging flow direction candidate set can be allocated to only one bin, and the second constraint rule being that when at least one flow direction is allocated to the same bin, a total processing volume of the bin is not greater than an expected volume of the bin; in the splitting flow direction candidate set, generate a splitting flow direction constraint condition according to a third constraint rule, the third constraint rule being that a total expected volume of at least one bin allocated to the flow direction in the splitting flow direction candidate set is greater than or equal to the flow volume corresponding to the flow direction; generate a flow direction constraint condition subset according to a fourth constraint rule, the fourth constraint rule being that the merging flow direction candidate set and the splitting flow direction candidate set are not allocated to the same type of bin at the same time, and the merging flow direction candidate set and the splitting flow direction candidate set are not allocated to the same bin; generate a bin constraint subset related to a number of flow directions allocated to the bin; generate a flow direction constraint subset related to whether the flow direction occupies an independent bin; obtain a plurality of sorting constraint conditions according to the first merging flow direction constraint condition, the second merging flow direction constraint condition, the splitting flow direction constraint condition, the flow direction constraint condition subset, the bin constraint subset, and the flow direction constraint subset, and generate the sorting constraint set; The problem splitting module is configured to: acquire a dual variable of the sorting constraint condition including a continuous variable in the sorting constraint set, generate a constraint main problem based on the dual variable and the sorting target function, and generate a constraint sub-problem according to the remaining sorting constraint conditions; The flow direction allocation module is configured to: calculate a minimum number of bins occupied by each flow direction, divide the flow direction in the splitting flow direction candidate set into a multi-bin flow direction and a single-bin flow direction based on the minimum number of bins, and allocate the flow direction in the merging flow direction candidate set to a remaining bin to obtain an initial solution. The iterative solving module is configured to: calculate an optimal solution of the constraint sub-problem based on the initial solution, update the constraint main problem according to the optimal solution, solve the constraint main problem based on a heuristic rule, iterate multiple times until an iteration termination condition is met, and obtain a flow direction-bin allocation result.

7. An electronic device, comprising: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the logistics sorting method in any one of claims 1 to 5 when executing the computer program.

8. A storage medium storing a computer program, characterized by The computer program, when executed by a processor, implements the logistics sorting method of any one of claims 1 to 5.

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