Picking path optimization method and apparatus, and electronic device

Abstract

A picking path optimization method and apparatus, and an electronic device. The method comprises: acquiring storage location layout information corresponding to a plurality of orders to be planned, wherein each of said orders comprises one or more products to be picked, and the storage location layout information comprises storage zone positions corresponding to said products, passageway positions in a storage zone and storage location positions in passageways; on the basis of the storage location layout information, generating at least one order batch and a picking task corresponding to each order batch, wherein each of said orders is comprised in one order batch; and on the basis of a picking strategy, performing picking path optimization on the picking task. Since it is possible to automatically perform automatic batching, determination of unpacking and picking tasks and picking path optimization on the basis of storage location layout information, the efficiency and accuracy of picking path planning can be improved.

Description

Picking path optimization method, device and electronic equipment

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese patent application CN202410332947.6, filed on March 22, 2024, entitled “A picking path optimization method, device and electronic device”, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present application belongs to the field of logistics technology, and in particular relates to a picking path optimization method, device and electronic equipment. Background Art

[0004] Picking route planning in a warehouse is a core step in outbound delivery. It refers to the process of removing goods from the warehouse's storage area (also known as a storage location) and organizing, sorting, and packing them according to the order. During the picking process, pickers can follow the planned picking route to remove the items required by the order one by one, inspect, count, mark, and then place them in the corresponding packing locations. Typically, the picking stage of warehousing operations is one of the most time-consuming and labor-intensive parts of the entire process. However, traditional picking route planning methods often rely on manual experience and rules, resulting in low efficiency and accuracy. Summary of the Invention

[0005] The present application provides a picking path optimization method, device and electronic equipment for solving the technical problem of low efficiency and accuracy of picking path planning methods in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a picking path optimization method, comprising: obtaining storage location layout information corresponding to multiple orders to be planned, wherein the orders to be planned include one or more goods to be picked, and the storage location layout information includes the warehouse area location corresponding to the goods to be picked, the channel location within the warehouse area, and the storage location location within the channel; generating at least one order batch and picking tasks corresponding to each order batch according to the storage location layout information, wherein each order to be planned is included in an order batch; and optimizing the picking path for the picking task according to the picking strategy.

[0007] In the above scheme, after obtaining the warehouse layout information corresponding to the order to be planned, multiple picking tasks can be generated for each order batch based on the warehouse layout information, and the picking path of the picking tasks can be optimized according to the picking strategy to obtain the optimal picking path. Among them, the picking path optimization method provided in the embodiment of the present application can automatically optimize the picking path based on the warehouse layout information. Therefore, compared with the manual determination of the picking path in the prior art, the efficiency and accuracy of picking path planning can be improved.

[0008] In an optional embodiment, the picking path optimization of the picking task according to the picking strategy includes: optimizing the path within the aisle of the picking task according to the picking strategy to obtain the aisle picking order within the aisle; optimizing the inter-aisle path of the picking task according to the aisle picking order and the shortest path rule to obtain the intra-area picking order within the warehouse area. In the above scheme, in the process of optimizing the picking path of the picking task, the path within the aisle can be optimized first, and then the inter-aisle path can be further optimized based on the optimized aisle path. This ensures that the picking path between aisles is optimal while ensuring the accuracy of the aisle picking path planning.

[0009] In an optional embodiment, the picking strategy includes at least one of the following: selecting the picking path with the shortest distance within the aisle, selecting the picking path with the least number of ladder climbs within the aisle, selecting the picking path with the least number of squats within the aisle, or selecting the picking path with the least number of turns within the aisle. In the above schemes, a picking strategy for optimizing inter-aisle paths can be developed based on the picker's picking habits, thereby obtaining a picking path with higher picking efficiency.

[0010] In an optional embodiment, the picking task is optimized between channels according to the picking sequence within the channel and the shortest path rule to obtain the picking sequence within the storage area, including: constructing a passive shortest path problem according to the picking sequence within the channel and the shortest path rule; and determining the picking sequence within the storage area by solving the passive shortest path problem. In the above scheme, the picking sequence within the channel is abstracted into two nodes, and the distance between the nodes is calculated to find the optimal path between channels. Since there is no need to configure other parameters, the optimal solution for the access sequence of each channel can be directly found, thereby improving the efficiency and accuracy of picking path planning.

[0011] In an optional embodiment, the passive shortest path problem is constructed based on the picking order in the channel and the shortest path rule, including: determining two nodes corresponding to the channel according to the picking order in the channel corresponding to the channel, wherein the nodes represent the shortest paths in the channel covering the storage locations corresponding to the picking order in the channel, and the picking orders corresponding to the two nodes are opposite; determining the picking order in the storage area by solving the passive shortest path problem includes: calculating the adjacent distances between the nodes between two adjacent channels; determining multiple target nodes that have the shortest distance from the first channel to the last channel according to the adjacent distances, wherein each channel corresponds to a target node; and determining the picking order in the storage area according to the multiple target nodes. In the above scheme, the picking order in the channel is abstracted into two nodes, and the distance between the nodes is calculated to find the optimal path between the channels. Since there is no need to configure other parameters, the optimal solution for the access order of each channel can be directly found, thereby improving the efficiency and accuracy of picking path planning.

[0012] In an optional embodiment, generating multiple picking tasks based on the storage location layout information includes: for a single order batch, generating the multiple picking tasks based on the storage location layout information using a greedy algorithm. In the above solution, the greedy algorithm can be used to generate the picking tasks, and since the greedy algorithm has a lower time complexity, it can improve the efficiency and accuracy of picking path planning.

[0013] In an optional embodiment, the method of using a greedy algorithm to generate the multiple picking tasks according to the storage location layout information includes: determining a seed commodity, wherein the seed commodity is the commodity to be picked that has the most channels in common with other commodities to be picked among the commodities to be picked that have not been scheduled, and has the smallest number of channels, columns, and layers; initializing the current picking task using the seed commodity; determining an insert commodity from the commodities to be picked that have not been scheduled using a greedy rule, wherein the insert commodity is the commodity to be picked that has the smallest insertion cost when inserted into the current picking task; and inserting the insert commodity into the current picking task until the number of commodities to be picked in the current picking task is greater than a quantity threshold. In the above scheme, a greedy algorithm can be used to generate picking tasks, wherein, since the greedy algorithm has a lower time complexity, the efficiency and accuracy of picking path planning can be improved.

[0014] In an optional embodiment, the insertion cost represents the added value of the picking path after the product to be picked is inserted.

[0015] In an optional embodiment, generating at least one order batch based on the warehouse layout information includes: determining whether the order to be planned includes multiple items to be picked; if the order to be planned includes multiple items to be picked, dividing the multiple orders to be picked into multiple order batches based on the warehouse layout information and batch grouping conditions; otherwise, generating one order batch based on all the orders to be planned. In the above scheme, when the order to be planned includes multiple items to be picked, the multiple orders to be picked can be divided into order batches, so that the warehouse area covered by one order batch is minimized while ensuring that multiple items to be picked in the same order to be planned are in the same order batch, thereby obtaining a picking path with higher picking efficiency.

[0016] In an optional embodiment, the division of the multiple to-be-picked orders into multiple order batches based on the storage location layout information and batch grouping conditions includes: determining the order batch that covers the smallest number of storage areas based on constraint conditions; wherein the constraint conditions include: one to-be-picked order is assigned to one order batch, the number of to-be-picked orders in an order batch is not greater than the maximum number of to-be-picked orders that can be assigned to the order batch, and the number of to-be-picked goods for one storage area in an order batch is not greater than the total number of to-be-picked orders. In the above scheme, the order batch division method that minimizes the storage areas covered by an order batch can be solved, thereby obtaining the optimal solution for order batch division, thereby improving picking efficiency.

[0017] In an optional embodiment, the method of dividing the multiple orders to be picked into multiple order batches based on the warehouse layout information and batching conditions includes: constructing a set of commodity locations corresponding to the orders to be picked, wherein the values ​​in the commodity location set represent the warehouse area numbers where the commodities to be picked in the orders to be picked are located; sorting the values ​​in the commodity location set and concatenating them into a character string; sorting the multiple orders to be picked according to the character strings corresponding to the multiple orders to be picked; and segmenting the sorted orders to be picked according to the maximum number of orders to be picked that can be allocated by the order batch. In the above scheme, when the order to be planned includes multiple commodities to be picked, the multiple orders to be picked can be divided into order batches, so that the warehouse area covered by one order batch is minimized while ensuring that the multiple commodities to be picked in the same order to be planned are in the same order batch, thereby obtaining a picking path with higher picking efficiency.

[0018] In the second aspect, an embodiment of the present application provides a picking path optimization device, including: an acquisition module for acquiring storage location layout information corresponding to multiple orders to be planned, wherein the orders to be planned include one or more goods to be picked, and the storage location layout information includes the storage area location corresponding to the goods to be picked, the channel location within the storage area, and the storage location location within the channel; a generation module for generating at least one order batch and picking tasks corresponding to each order batch according to the storage location layout information, wherein each order to be planned is included in an order batch; an optimization module for optimizing the picking path for each picking task according to the picking strategy.

[0019] In the above scheme, after obtaining the warehouse layout information corresponding to the order to be planned, multiple picking tasks can be generated for each order batch based on the warehouse layout information, and the picking path of the picking tasks can be optimized according to the picking strategy to obtain the optimal picking path. Among them, the picking path optimization method provided in the embodiment of the present application can automatically optimize the picking path based on the warehouse layout information. Therefore, compared with the manual determination of the picking path in the prior art, the efficiency and accuracy of picking path planning can be improved.

[0020] In an optional embodiment, the optimization module is specifically configured to: optimize the path within the aisle for the picking task according to the picking strategy to obtain an aisle picking order within the aisle; and optimize the path between aisles for the picking task according to the aisle picking order and the shortest path rule to obtain an intra-storage area picking order within the storage area. In the above scheme, during the picking path optimization process for the picking task, the path within the aisle can be optimized first, and then the path between aisles can be further optimized based on the optimized aisle path. This ensures that the picking path between aisles is optimal while ensuring the accuracy of the aisle picking path planning.

[0021] In an optional embodiment, the picking strategy includes at least one of the following: selecting the picking path with the shortest distance within the aisle, selecting the picking path with the least number of ladder climbs within the aisle, selecting the picking path with the least number of squats within the aisle, or selecting the picking path with the least number of turns within the aisle. In the above schemes, a picking strategy for optimizing inter-aisle paths can be developed based on the picker's picking habits, thereby obtaining a picking path with higher picking efficiency.

[0022] In an optional embodiment, the optimization module is specifically configured to construct a passive shortest path problem based on the picking order within the aisle and the shortest path rule; and to determine the picking order within the storage area by solving the passive shortest path problem. In this solution, the picking order within the aisle is abstracted into two nodes, and the distance between the nodes is calculated to find the optimal path between the aisles. Since no additional parameters need to be configured, the optimal solution for the order in which each aisle is visited can be directly found, thereby improving the efficiency and accuracy of picking path planning.

[0023] In an optional embodiment, the optimization module is also used to: determine the two nodes corresponding to the channel according to the picking order in the channel corresponding to the channel, wherein the nodes represent the shortest path in the channel covering the storage locations corresponding to the picking order in the channel, and the picking orders corresponding to the two nodes are opposite; calculate the adjacent distance between the nodes between two adjacent channels; determine multiple target nodes that make the distance from the first channel to the last channel the shortest according to the adjacent distance, wherein each channel corresponds to a target node; determine the picking order in the storage area according to the multiple target nodes. In the above scheme, the picking order in the channel is abstracted into two nodes, and the distance between the nodes is calculated to find the optimal path between the channels. Since there is no need to configure other parameters, the optimal solution for the access order of each channel can be directly found, thereby improving the efficiency and accuracy of picking path planning.

[0024] In an optional embodiment, the generation module is specifically configured to generate the plurality of picking tasks for an order batch based on the storage location layout information using a greedy algorithm. In the above solution, the greedy algorithm can be used to generate the picking tasks, and since the greedy algorithm has a lower time complexity, it can improve the efficiency and accuracy of picking path planning.

[0025] In an optional embodiment, the generation module is further used to: determine a seed commodity, wherein the seed commodity is the commodity to be picked that has the most channels in common with other commodities to be picked among the unscheduled commodities to be picked, and has the smallest number of channels, columns, and layers; initialize the current picking task using the seed commodity; determine an insert commodity from the unscheduled commodities to be picked using a greedy rule, wherein the insert commodity is the commodity to be picked that has the smallest insertion cost when inserted into the current picking task; and insert the insert commodity into the current picking task until the number of commodities to be picked in the current picking task is greater than a quantity threshold. In the above scheme, a greedy algorithm can be used to generate picking tasks, wherein, since the greedy algorithm has a lower time complexity, it can improve the efficiency and accuracy of picking path planning.

[0026] In an optional embodiment, the insertion cost represents the added value of the picking path after the product to be picked is inserted.

[0027] In an optional embodiment, the generation module is specifically configured to: determine whether the order to be planned includes multiple items to be picked; if so, divide the multiple orders to be picked into multiple order batches based on the warehouse layout information and batch grouping conditions; otherwise, generate one order batch based on all the orders to be planned. In the above scheme, when the order to be planned includes multiple items to be picked, the multiple orders to be picked can be divided into order batches, thereby ensuring that multiple items to be picked in the same order to be planned are in the same order batch, while minimizing the warehouse area covered by one order batch, thereby obtaining a picking path with higher picking efficiency.

[0028] In an optional embodiment, the generation module is specifically configured to determine, based on constraints, the order batch that covers the minimum number of warehouse areas; wherein the constraints include: each pending order is assigned to an order batch, the number of pending orders in an order batch is no greater than the maximum number of pending orders that can be assigned to the order batch, and the number of pending items in an order batch for a warehouse area is no greater than the total number of pending orders. In this solution, the order batch division method that minimizes the number of warehouse areas covered by an order batch can be determined, thereby obtaining the optimal solution for order batch division and improving picking efficiency.

[0029] In an optional embodiment, the generation module is specifically used to: construct a set of commodity locations corresponding to the order to be picked, wherein the values ​​in the commodity location set represent the warehouse area numbers where the commodities to be picked in the order to be picked are located; sort the values ​​in the commodity location set and concatenate them into a character string; sort the multiple orders to be picked according to the character strings corresponding to the multiple orders to be picked; and segment the sorted orders to be picked according to the maximum number of orders to be picked that can be allocated to the order batch. In the above scheme, when the order to be planned includes multiple commodities to be picked, the multiple orders to be picked can be divided into order batches, so that the warehouse area covered by one order batch is minimized while ensuring that multiple commodities to be picked in the same order to be planned are in the same order batch, thereby obtaining a picking path with higher picking efficiency.

[0030] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor, a memory, and a bus; the processor and the memory communicate with each other through the bus; the memory stores computer program instructions that can be executed by the processor, and the processor calls the computer program instructions to execute the picking path optimization method described in the first aspect.

[0031] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores computer program instructions. When the computer program instructions are executed by a computer, the computer executes the picking path optimization method described in the first aspect.

[0032] Other features and advantages of the present application will be described in the following description and, in part, will become apparent from the description or be understood by practicing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0034] FIG1 is a flow chart of a picking path optimization method provided by an embodiment of the present application;

[0035] FIG2 is a schematic diagram of a high-rise shelf-type warehouse provided in an embodiment of the present application;

[0036] FIG3 is a schematic diagram of storage locations corresponding to commodities to be picked according to an embodiment of the present application;

[0037] FIG4 is a schematic diagram of a picking order of commodities to be picked provided by an embodiment of the present application;

[0038] FIG5 is a schematic diagram of an inter-channel path optimization method provided in an embodiment of the present application;

[0039] FIG6 is a schematic diagram of constructing an active shortest circuit problem provided by an embodiment of the present application;

[0040] FIG7 is a flowchart of a greedy algorithm provided in an embodiment of the present application;

[0041] FIG8 is a structural block diagram of a picking path optimization system provided by an embodiment of the present application;

[0042] FIG9 is a structural block diagram of a picking path optimization device provided in an embodiment of the present application;

[0043] FIG10 is a structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present application.

[0045] It should be noted that the term "plurality" in the embodiments of the present invention refers to two or more. Therefore, in the embodiments of the present invention, "plurality" can also be understood as "at least two." "And / or" describes the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / ," unless otherwise specified, generally indicates an "or" relationship between the associated objects.

[0046] The picking path planning problem can be defined as follows: Given a set of picking locations, determine the order and path of the locations to be visited to achieve the shortest picking path distance. Therefore, an embodiment of the present application provides a picking path optimization method to automatically achieve the above purpose. Please refer to Figure 1, which is a flowchart of a picking path optimization method provided by an embodiment of the present application. The picking path optimization method may include the following steps:

[0047] Step S101: Obtaining storage location layout information corresponding to multiple orders to be planned, wherein the order to be planned includes one or more goods to be picked, and the storage location layout information includes the storage area location corresponding to the goods to be picked, the channel location within the storage area, and the storage location location within the channel.

[0048] Step S102: generating at least one order batch and a picking task corresponding to each order batch according to the storage location layout information, wherein each order to be planned is included in one order batch.

[0049] Step S103: Optimize the picking path for the picking task according to the picking strategy.

[0050] Specifically, in step S101, the multiple pending orders are orders that currently need to be picked together. As an implementation, these multiple pending orders can be referred to as a wave. A wave is a group of pending orders generated according to certain rules by combining multiple orders to improve warehouse efficiency and reduce missed shipments. Orders in the same wave share the same attributes and processing rules.

[0051] It should be noted that the embodiments of the present application do not impose any specific limitations on the specific implementation methods for determining a wave (i.e., multiple orders to be planned), and those skilled in the art can make appropriate adjustments based on actual conditions; for example, orders within a certain period of time can be determined as a wave in chronological order; or, orders with a fixed number of orders can be determined as a wave based on the order quantity; or, a wave can be determined based on user needs.

[0052] For example, a wave generation request involves determining a wave based on user needs. This request involves an on-site wave generator or the system filtering a set of orders and creating a wave based on preset wave generation conditions. Specifically, when a user wishes to create an order wave combination, they input (or set) the wave generation conditions into a computer device. The computer device then responds to the wave generation request, obtaining order information from the current order pool and real-time inventory information from the warehouse.

[0053] Each pending order may include one or more items to be picked. In one embodiment, in a consolidated shipment scenario, a pending order may include multiple items to be picked, each of which may be referred to as a detail. In another embodiment, in a transaction shipment scenario, each pending order may include only one detail (i.e., only one item to be picked).

[0054] The storage location layout information refers to the location of the goods to be picked in the planned order in the warehouse, wherein the storage location layout information may include the storage area location corresponding to the goods to be picked, the aisle location within the storage area, and the storage location location within the aisle.

[0055] Please refer to Figure 2, which is a schematic diagram of a high-rise rack-type three-dimensional warehouse provided in an embodiment of the present application. Figure 2 shows aisles 09, 10, 11, and 12 in the AD storage area. Each of aisles 09, 10, 11, and 12 includes two sides, A and B. Side A represents odd-numbered columns, and side B represents even-numbered columns. Each column includes seven layers. It is understood that a storage location is a storage location in a certain storage area, a certain aisle, a certain column, and a certain layer in the warehouse. For example, in Figure 2, the AD090503 storage location corresponds to the AD storage area, aisle 09, column 5, and layer 3.

[0056] In the warehouse shown in Figure 2, if you pick the first column first and then the second column, you need to turn around once; the first and third columns can generally be picked at the same time without turning around or walking horizontally; the third and fourth floors are the most efficient for picking, and the first and second floors require squatting, while the fifth floor and above are high-rise storage locations and require the use of ladders; after picking in aisle 09 and entering aisle 10, you can "walk forward" to bypass the largest column (column 16) or "turn back" to bypass the smallest column (column 1), which needs to be determined based on the cargo locations to be picked in the two aisles.

[0057] It should be noted that the picking path optimization method provided in the embodiment of the present application is not limited to application in the warehouse shown in Figure 2. Figure 2 is only an example provided in the embodiment of the present application. Those skilled in the art can make appropriate adjustments to the specific implementation of the warehouse according to actual conditions.

[0058] In addition, the embodiments of the present application do not impose specific limitations on the specific implementation methods for obtaining warehouse layout information corresponding to multiple orders to be planned. Those skilled in the art can make appropriate adjustments based on actual conditions; for example, warehouse layout information sent by an external device can be received; or, warehouse layout information stored locally or in the cloud can be queried, etc.

[0059] In the above step S102, the order batch is used to define a collection of combined shipping orders that need to be sorted together. One wave will generate at least one order batch, and one order batch will have at least one order to be planned. Among them, the maximum number of orders to be planned generally corresponds to the capacity of the subsequent sorting vehicle in the warehouse.

[0060] In the embodiment of the present application, each order to be planned is included in only one order batch.

[0061] It should be noted that the embodiments of the present application do not impose any specific limitations on the specific implementation methods for generating at least one order batch based on the warehouse layout information, and those skilled in the art may make appropriate adjustments based on actual conditions; for example, the orders to be planned corresponding to the goods to be picked within a range in the warehouse may be divided into one order batch; or, the order batches may be divided based on minimizing the warehouse area covered by one order batch, etc.

[0062] Picking tasks refer to the work tasks that require picking goods in the warehouse logistics system, which are generated based on customer orders or outbound instructions and other related information. They usually include the operation of taking goods off the shelves in order to complete the outbound delivery of goods in the shortest time and at the lowest cost.

[0063] In an embodiment of the present application, for each order batch, a picking task corresponding to the order batch can be generated based on the storage location layout information obtained in the above step S101; wherein the above picking task is used to pick the goods to be picked in the order batch.

[0064] It should be noted that the embodiments of the present application do not impose any specific limitations on the specific implementation methods for generating picking tasks based on storage location layout information, and those skilled in the art may make appropriate adjustments based on actual conditions; for example, picking tasks may be generated based on the picking habits of pickers; or, a greedy algorithm may be used to generate picking tasks, etc.

[0065] In step S103 above, the picking path refers to the order and path taken within the warehouse when completing the picking task corresponding to an order batch. In the embodiment of the present application, the picking path can be optimized for the picking task based on a pre-determined picking strategy, thereby obtaining a picking path corresponding to the picking task.

[0066] It should be noted that the embodiments of this application do not impose any specific limitations on the specific implementation of the picking strategy. The objectives of path optimization may include minimizing the picking path, minimizing the number of times a picker must climb a ladder, or minimizing the number of times a picker must squat. Therefore, the specific implementation of the picking strategy can be determined based on actual conditions and the specific objectives of path optimization.

[0067] As an implementation method, by configuring parameters, two modes can be realized: high-low mixed picking and high-low separated picking. The high-level refers to the storage location that needs to be picked with the help of a ladder, while the low-level refers to the storage location that does not need to be picked with the help of a ladder. In the high-low separated picking mode, the low-level storage locations of all channels can be picked first, and then the high-level storage locations of all channels can be picked; in the high-low mixed picking mode, the high-level storage locations and low-level storage locations of each channel (or even each column) are picked together, which may require frequent climbing up and down the ladder.

[0068] It can be understood that in the embodiment of the present application, the optimization of the picking path is to optimize the order of the picking path of the goods to be picked in the same warehouse area; therefore, if a picking task spans multiple warehouse areas (a scenario of combined shipment and outbound delivery), then they can be grouped according to the warehouse area, and the path optimization can be performed separately within each warehouse area, and then they can be combined at the end.

[0069] In the above scheme, after obtaining the warehouse layout information corresponding to the order to be planned, multiple picking tasks can be generated for each order batch based on the warehouse layout information, and the picking path of the picking tasks can be optimized according to the picking strategy to obtain the optimal picking path. Among them, the picking path optimization method provided in the embodiment of the present application can automatically optimize the picking path based on the warehouse layout information. Therefore, compared with the manual determination of the picking path in the prior art, the efficiency and accuracy of picking path planning can be improved.

[0070] Furthermore, based on the above embodiment, the above step S103 may specifically include the following steps:

[0071] Step 1) Optimize the path within the aisle for the picking task according to the picking strategy to obtain the aisle picking sequence within the aisle.

[0072] Step 2) Optimize the path between channels of the picking task according to the picking order within the channel and the shortest path rule to obtain the picking order within each storage area.

[0073] Specifically, as shown in Figure 2, the warehouse pickers will first pick items within an aisle. After finishing picking items in the current aisle, they can move to another aisle to pick more items. Therefore, when optimizing the picking path, it is possible to first optimize the path within the aisle, and then optimize the path between aisles.

[0074] In step 1) above, the picking path optimization within the aisle needs to take into account the operating habits of the pickers. For example, a Z-shaped picking route can be designed within the aisle; or two adjacent columns can be picked at one time without moving (this can be achieved by configuring column offsets), etc.

[0075] As an implementation method, the picking strategy may include: selecting the picking path with the shortest distance in the aisle, selecting the picking path with the least number of ladder climbs in the aisle, selecting the picking path with the least number of squats in the aisle, and selecting the picking path with the least number of turns in the aisle.

[0076] As another implementation, the picking strategy may include: picking products in the following order: mid-level storage locations, adjacent storage locations, low-level storage locations, reverse storage locations, and high-level storage locations. A mid-level storage location refers to a storage location that a picker can just reach with their hand, an adjacent storage location refers to a storage location that a picker can reach without moving, a low-level storage location refers to a storage location that a picker can reach without using a ladder, a reverse storage location refers to a storage location that a picker can reach by turning around, and a high-level storage location refers to a storage location that a picker needs to use a ladder to reach.

[0077] For example, assume that the storage locations of the goods to be picked on the AB sides of aisle 15 are as shown in Figure 3, where floors 1-4 are low-level storage locations and floors 5-7 are high-level storage locations, and ladders are required for picking; in the high-low separation picking mode, the low level (i.e., floors 4-1) is picked first, and then the high level (i.e., floors 5-7) is picked.

[0078] According to picking practices, the layer scanning order for low-level locations is from high to low (i.e., from level 4 to level 1), and the layer scanning order for high-level locations is from low to high (i.e., from level 5 to level 7). Column scanning orders can be divided into two types: small to large (i.e., from left to right) and large to small, depending on the column from which the aisle is entered. In the 15-channel shown in the figure above, assuming that the aisle is entered from the left, i.e., column 1, the column scanning order is small to large.

[0079] Based on the above picking strategy, the picking order for lower-level locations is: (A,4,1) → (A,3,3) → (B,3,4) → (B,2,2) → (B,4,6) → (B,2,6) → (A,2,5) → ..., which means picking locations (4,1) and (3,3) on side A in sequence. At this point, the 1st and 3rd columns of side A have been picked. Then, pick locations (3,4) and (2,2) in the 2nd and 4th columns of side B, and so on. Similarly, the picking order for higher-level locations is: (A,5,21) → (A,6,19) → (B,7,22) → (B,6,18) → (A,5,15) → ....

[0080] It can be understood that the above picking sequence can minimize the number of turns.

[0081] It's important to note that each storage location within an aisle can be abstracted into an adjacency matrix model. This model allows for rapid location locating, and traversing this matrix represents how the picking path is generated. The matrix can be used to represent the characteristics of business rules, such as high- and low-level picking and return patterns. These characteristics can better assist pickers in path planning. Therefore, the picking order within the aisle shown in Figure 3 can be represented in the matrix shown in Figure 4.

[0082] In step 2), each picking task can be optimized for the inter-aisle path based on the intra-aisle picking order and the shortest path rule to obtain the intra-area picking order in each warehouse area. The specific implementation of the above inter-aisle path optimization will be introduced in subsequent embodiments of the present application.

[0083] In the above scheme, in the process of optimizing the picking path for each picking task, the path within the channel can be optimized first, and then the path between channels can be further optimized based on the optimized path within the channel. In this way, the picking path between channels can be optimized while ensuring the accuracy of the picking path planning within the channel.

[0084] Furthermore, based on the above embodiment, the above step of performing inter-channel path optimization may specifically include the following steps:

[0085] Step 1), construct the passive shortest path problem according to the picking order in the channel and the shortest path rule.

[0086] Step 2) Determine the picking order within the warehouse area by solving the passive shortest path problem.

[0087] Specifically, the path optimization between channels can be performed by constructing and solving the passive shortest path problem, thereby determining the picking order within the warehouse area. It should be noted that the embodiments of the present application do not specifically limit the specific implementation methods for constructing and solving the passive shortest path problem, and those skilled in the art can make appropriate adjustments based on actual conditions.

[0088] In this solution, the picking order within an aisle is abstracted into two nodes, and the distance between the nodes is calculated to find the optimal path between the aisles. Since no additional parameters need to be configured, the optimal solution for the order in which each aisle is visited can be directly found, thereby improving the efficiency and accuracy of picking path planning.

[0089] Furthermore, based on the above embodiment, the step of constructing the passive shortest path problem according to the picking order in the channel and the shortest path rule may specifically include the following steps:

[0090] Step 1) Determine two nodes corresponding to the channel according to the picking order within the channel corresponding to the channel, wherein the nodes represent the shortest path within the channel covering the storage locations corresponding to the picking order within the channel, and the picking orders corresponding to the two nodes are opposite.

[0091] Accordingly, the above step of determining the picking order in the warehouse area by solving the passive shortest path problem may specifically include the following steps:

[0092] Step 2), calculate the adjacent distance between nodes of two adjacent channels.

[0093] Step 3) Determine multiple target nodes that have the shortest distance from the first channel to the last channel based on the adjacent distances, wherein each channel corresponds to one target node.

[0094] Step 4) Determine the picking order within the warehouse area based on multiple target nodes.

[0095] Specifically, in the above step 1), the picking order of each channel can be abstracted into two nodes. In this way, the abstracted nodes represent the shortest path in the channel that covers the storage locations corresponding to the picking order in the channel, and the picking orders corresponding to the two nodes are opposite.

[0096] For example, please refer to the second channel in Figure 5. There are three storage locations to be picked in the channel: 3, 4, and 5. Then there are two access orders for these three storage locations: 3-4-5 or 5-4-3. 3-4-5 in Figure 5 can be abstracted as node 2F, and 5-4-3 in Figure 5 can be abstracted as node 2B.

[0097] It can be understood that the walking mode within the abstracted node is fixed, so the length of its picking path is a fixed value; and the two nodes abstracted from each channel are fully connected with the two nodes of its adjacent channel.

[0098] Therefore, as an implementation method, a passive shortest path problem can be constructed; as another implementation method, two virtual nodes O (starting node) and D (ending node) can be added to construct an active shortest path problem, thereby determining the shortest path between channels. Taking the construction of the active shortest path problem as an example, please refer to Figure 6, which is a schematic diagram of a construction of the active shortest path problem provided by an embodiment of the present application. By solving the shortest path of OD, the optimal walking method between each two adjacent channels can be automatically obtained, that is, whether to turn back.

[0099] The embodiments of this application do not specifically limit the specific implementation method for solving the shortest path problem, and those skilled in the art may make appropriate adjustments based on actual circumstances. For example, the shortest path problem can be solved using steps 2) through 4) above; alternatively, the shortest path problem can be solved using algorithms such as dynamic programming, Dijkstra's algorithm, or binary tree traversal.

[0100] In this solution, the picking order within an aisle is abstracted into two nodes, and the distance between the nodes is calculated to find the optimal path between the aisles. Since no additional parameters need to be configured, the optimal solution for the order in which each aisle is visited can be directly found, thereby improving the efficiency and accuracy of picking path planning.

[0101] Furthermore, based on the above embodiment, a specific implementation method of generating a picking task is introduced below. The above step S102 may specifically include the following steps:

[0102] For an order batch, a greedy algorithm is used to generate multiple picking tasks based on the storage location layout information.

[0103] Specifically, splitting picking tasks refers to splitting all the items to be picked within the same order batch into several picking tasks. In this embodiment of the present application, a greedy algorithm can be used to achieve the above purpose. It should be noted that this embodiment of the present application does not specifically limit the specific implementation method of using a greedy algorithm to generate multiple picking tasks, and those skilled in the art can make appropriate adjustments based on actual conditions.

[0104] The following example introduces a specific implementation of a greedy algorithm provided in an embodiment of the present application. Please refer to FIG7 , which is a flowchart of a greedy algorithm provided in an embodiment of the present application. The above-mentioned steps of using the greedy algorithm to generate multiple picking tasks according to the storage location layout information may specifically include the following steps:

[0105] Step 1) determining a seed commodity, wherein the seed commodity is a commodity to be selected that has not been arranged and has the most channels in common with other commodities to be selected, and has the smallest number of channels, columns, and layers.

[0106] Step 2): Initialize the current picking task using the seed product.

[0107] Step 3) Use the greedy rule to determine the inserted product from the unscheduled products to be picked, where the inserted product is the product to be picked that has the lowest insertion cost when inserted into the current picking task.

[0108] Step 4) Insert the inserted product into the current picking task until the number of products to be picked in the current picking task is greater than the quantity threshold.

[0109] The insertion cost in the greedy algorithm represents the added value of the picking path after inserting the product to be picked, and can estimate the cost across warehouse areas, channels, columns, and levels.

[0110] In the above solution, a greedy algorithm can be used to generate picking tasks. Since the greedy algorithm has a lower time complexity, the efficiency and accuracy of picking path planning can be improved.

[0111] Furthermore, based on the above embodiment, the specific implementation method of order batching is introduced below. The above step S102 may specifically include the following steps:

[0112] Step 1) determines whether the order to be planned includes multiple items to be picked.

[0113] Step 2): If the order to be planned includes multiple commodities to be picked, the multiple orders to be picked are divided into multiple order batches according to the warehouse layout information and the batch grouping conditions; otherwise, one order batch is generated according to all the orders to be planned.

[0114] Specifically, in step 1) above, order batching refers to the scenario of consolidated shipment and outbound delivery, requiring that multiple items to be picked for the same order to be planned be grouped into the same order batch to facilitate subsequent secondary sorting. However, in the transaction outbound delivery scenario, since there is only one order batch, order batching is not required. Therefore, it is possible to determine whether the order to be planned includes multiple items to be picked.

[0115] In step 2) above, the goal of order batching is to minimize the number of storage areas covered by each batch. There are two constraints: 1) Multiple items to be picked for the same order must be in the same batch; 2) There is an upper limit on the maximum number of orders in each batch.

[0116] It should be noted that the embodiments of this application do not specifically limit the specific implementation of order batching, and those skilled in the art may make appropriate adjustments based on actual circumstances. For example, an optimal method may be used to batch orders; or a heuristic method may be used to batch orders.

[0117] In the above scheme, when the order to be planned includes multiple goods to be picked, the multiple orders to be picked can be divided into order batches, so as to ensure that the multiple goods to be picked in the same order to be planned are in the same order batch, and the warehouse area covered by one order batch is minimized, thereby obtaining a picking path with higher picking efficiency.

[0118] Furthermore, based on the above embodiment, the following describes a specific implementation method for batching orders using the optimal method. The above step of dividing multiple to-be-picked orders into multiple order batches based on the storage location layout information and batching conditions may specifically include the following steps:

[0119] Determine an order batch that covers the smallest number of storage areas based on constraint conditions; wherein the constraint conditions include: one order to be picked is assigned to one order batch, the number of orders to be picked in one order batch is not greater than the maximum number of orders to be picked that can be assigned to the order batch, and the number of items to be picked for one storage area in one order batch is not greater than the total number of orders to be picked. As an embodiment, the above step of determining an order batch that covers the smallest number of storage areas based on constraint conditions may specifically include the following steps:

[0120] Use the following formula to determine multiple order batches: min∑ j ∑ a y aj ;

[0121] The constraints are:

[0122] Among them, j represents the order batch, a represents the warehouse area, and y aj =1 means that order batch j covers warehouse area a, i means the order to be picked, x ij =1 means that the order i to be picked is assigned to the order batch j, M represents the maximum number of orders to be picked that can be assigned to each order batch, s ia It means that order i has goods to be picked in warehouse area a.

[0123] In the above scheme, the order batch division method that minimizes the warehouse area covered by an order batch can be solved, so that the optimal solution for order batch division can be obtained, thereby improving picking efficiency.

[0124] Furthermore, based on the above embodiment, the following describes a specific implementation method for batching orders using a heuristic method. The above step of dividing multiple to-be-picked orders into multiple order batches based on the storage location layout information and batching conditions may specifically include the following steps:

[0125] Step 1) constructs a commodity location set corresponding to the order to be picked, wherein the value in the commodity location set represents the warehouse area number where the commodity to be picked in the order to be picked is located.

[0126] Step 2) Sort the values ​​in the product location set and concatenate them into a string. Sort the multiple orders to be picked according to the strings corresponding to the multiple orders to be picked.

[0127] Step 3) The sorted orders to be picked are divided according to the maximum number of orders to be picked that can be allocated to the order batch.

[0128] Furthermore, based on the above embodiment, in the process of order batching, the optimal method can be used to batch the orders first, and if the set solution time threshold is exceeded, the heuristic method can be used to batch the orders.

[0129] An embodiment of the present application provides a structural block diagram of a picking path optimization system, which may include: a server, a wireless local area network, and a handheld mobile terminal. Please refer to Figure 8, which is a structural block diagram of a server provided by an embodiment of the present application. The server 800 may include a mapping unit 801, a query unit 802, a computing unit 803, and a storage unit 804.

[0130] Specifically, the mapping unit 801 is used to generate a warehouse floor plan based on the placement of warehouse shelves, and then calculate information such as the floor where the warehouse area is located, the number of floors where the cargo locations in each aisle within the warehouse that need to be picked with the help of ladders, and the maximum column of each aisle; the query unit 802 is used to perform order selection and inventory pre-occupancy according to manual screening rules, and to search for the storage location layout information of the selected orders to be planned from the pre-stored data; the calculation unit 803 is used to execute order batching, unpacking and picking tasks, and picking path planning (i.e., for executing the picking path optimization method provided in the embodiment of the present application) for the selected orders to be planned in sequence; the storage unit 804 is used to store the planned picking tasks for subsequent push to the picking staff.

[0131] In the embodiment of the present application, the optimal order combination and picking path under the current wave can be efficiently calculated to reduce operating costs; the optimal solution for batching plans and inter-channel return plans can be calculated; no additional configuration parameters are required, and the picking personnel do not need to have strong business qualities and system learning capabilities; taking into account picking habits, such as the number of squats and turns, etc., it is more in line with user needs and improves the efficiency of warehouse sorting operations; there is no need to calculate data that is difficult to collect, such as the spatial position relationship of the warehouse area and the walking distance between any two warehouse locations, and it has high feasibility and promotion value.

[0132] Please refer to Figure 9, which is a structural block diagram of a picking path optimization device provided in an embodiment of the present application. The picking path optimization device 900 includes: an acquisition module 901, used to obtain warehouse layout information corresponding to multiple orders to be planned, wherein the orders to be planned include one or more goods to be picked, and the warehouse layout information includes the warehouse area location corresponding to the goods to be picked, the channel location within the warehouse area, and the warehouse location within the channel; a generation module 902, used to generate at least one order batch and picking tasks corresponding to each order batch according to the warehouse layout information, wherein each order to be planned is included in an order batch; an optimization module 903, used to optimize the picking path for each picking task according to the picking strategy.

[0133] In the above scheme, after obtaining the warehouse layout information corresponding to the order to be planned, multiple picking tasks can be generated for each order batch based on the warehouse layout information, and the picking path of the picking tasks can be optimized according to the picking strategy to obtain the optimal picking path. Among them, the picking path optimization method provided in the embodiment of the present application can automatically optimize the picking path based on the warehouse layout information. Therefore, compared with the manual determination of the picking path in the prior art, the efficiency and accuracy of picking path planning can be improved.

[0134] Furthermore, based on the above embodiments, the optimization module 903 is specifically used to: optimize the path within the channel of the picking task according to the picking strategy to obtain the channel picking order within the channel; optimize the path between channels of the picking task according to the channel picking order and the shortest path rule to obtain the storage area picking order within the storage area.

[0135] In the above scheme, in the process of optimizing the picking path for each picking task, the path within the channel can be optimized first, and then the path between channels can be further optimized based on the optimized path within the channel. In this way, the picking path between channels can be optimized while ensuring the accuracy of the picking path planning within the channel.

[0136] Furthermore, based on the above embodiments, the picking strategy includes at least one of the following: selecting the picking path with the shortest distance in the channel, selecting the picking path with the least number of ladder climbing in the channel, selecting the picking path with the least number of squatting in the channel, and selecting the picking path with the least number of turning in the channel.

[0137] In the above scheme, a picking strategy for optimizing the path between aisles can be formulated based on the picking habits of the pickers, thereby obtaining a picking path with higher picking efficiency.

[0138] Furthermore, based on the above embodiment, the optimization module 903 is specifically used to: construct a passive shortest path problem according to the picking order in the channel and the shortest path rule; and determine the picking order in the storage area by solving the passive shortest path problem.

[0139] In this solution, the picking order within an aisle is abstracted into two nodes, and the distance between the nodes is calculated to find the optimal path between the aisles. Since no additional parameters need to be configured, the optimal solution for the order in which each aisle is visited can be directly found, thereby improving the efficiency and accuracy of picking path planning.

[0140] In an optional embodiment, the optimization module 903 is also used to: determine two nodes corresponding to the channel according to the picking order in the channel corresponding to the channel, wherein the nodes represent the shortest path in the channel covering the storage locations corresponding to the picking order in the channel, and the picking orders corresponding to the two nodes are opposite; calculate the adjacent distance between the nodes between two adjacent channels; determine multiple target nodes that make the distance from the first channel to the last channel the shortest according to the adjacent distance, wherein each channel corresponds to a target node; and determine the picking order in the storage area according to the multiple target nodes.

[0141] In this solution, the picking order within an aisle is abstracted into two nodes, and the distance between the nodes is calculated to find the optimal path between the aisles. Since no additional parameters need to be configured, the optimal solution for the order in which each aisle is visited can be directly found, thereby improving the efficiency and accuracy of picking path planning.

[0142] Furthermore, based on the above embodiment, the generating module 902 is specifically configured to: for an order batch, generate the multiple picking tasks according to the storage location layout information using a greedy algorithm.

[0143] In the above solution, a greedy algorithm can be used to generate picking tasks. Since the greedy algorithm has a lower time complexity, the efficiency and accuracy of picking path planning can be improved.

[0144] In an optional embodiment, the generation module 902 is also used to: determine a seed commodity, wherein the seed commodity is the commodity to be picked that has the most channels in common with other commodities to be picked among the unscheduled commodities to be picked, and has the smallest number of channels, columns and layers; use the seed commodity to initialize the current picking task; use the greedy rule to determine an insert commodity from the unscheduled commodities to be picked, wherein the insert commodity is the commodity to be picked that has the smallest insertion cost when inserted into the current picking task; insert the insert commodity into the current picking task until the number of commodities to be picked in the current picking task is greater than the quantity threshold.

[0145] In the above solution, a greedy algorithm can be used to generate picking tasks. Since the greedy algorithm has a lower time complexity, the efficiency and accuracy of picking path planning can be improved.

[0146] In an optional embodiment, the insertion cost represents the added value of the picking path after the product to be picked is inserted.

[0147] Furthermore, based on the above embodiment, the generation module 902 is specifically used to: determine whether the order to be planned includes multiple goods to be picked; if the order to be planned includes multiple goods to be picked, then the multiple orders to be picked are divided into multiple order batches according to the warehouse layout information and batch conditions; otherwise, an order batch is generated based on all the orders to be planned.

[0148] In the above scheme, when the order to be planned includes multiple goods to be picked, the multiple orders to be picked can be divided into order batches, so as to ensure that the multiple goods to be picked in the same order to be planned are in the same order batch, and the warehouse area covered by one order batch is minimized, thereby obtaining a picking path with higher picking efficiency.

[0149] Furthermore, based on the above embodiment, the generation module 902 is specifically used to: determine the order batch with the smallest number of covered warehouse areas according to constraint conditions; wherein the constraint conditions include: one order to be picked is assigned to an order batch, the number of orders to be picked in an order batch is not greater than the maximum number of orders to be picked that can be assigned to the order batch, and the number of goods to be picked for a warehouse area in an order batch is not greater than the total number of orders to be picked.

[0150] In the above scheme, the order batch division method that minimizes the warehouse area covered by an order batch can be solved, so that the optimal solution for order batch division can be obtained, thereby improving picking efficiency.

[0151] In an optional embodiment, the generating module 902 is further configured to determine the plurality of order batches using the following formula: min∑ j ∑ a y aj ;

[0152] The constraints are:

[0153] Among them, j represents the order batch, a represents the warehouse area, y aj =1 indicates that the order batch j covers the warehouse area a, i indicates the order to be picked, x ij=1 indicates that the order i to be picked is assigned to the order batch j, M indicates the maximum number of orders to be picked that can be assigned to each order batch, s ia It means that the order i to be picked has the goods to be picked in the warehouse area a.

[0154] In the above scheme, the order batch division method that minimizes the warehouse area covered by an order batch can be solved, so that the optimal solution for order batch division can be obtained, thereby improving picking efficiency.

[0155] In an optional embodiment, the generation module 902 is specifically used to: construct a product location set corresponding to the order to be picked, wherein the values ​​in the product location set represent the warehouse area number where the product to be picked in the order to be picked is located; sort the values ​​in the product location set and concatenate them into a string; sort the multiple orders to be picked according to the strings corresponding to the multiple orders to be picked; and split the sorted orders to be picked according to the maximum number of orders to be picked that can be allocated to the order batch.

[0156] In the above scheme, when the order to be planned includes multiple goods to be picked, the multiple orders to be picked can be divided into order batches, so as to ensure that the multiple goods to be picked in the same order to be planned are in the same order batch, and the warehouse area covered by one order batch is minimized, thereby obtaining a picking path with higher picking efficiency.

[0157] Please refer to Figure 10, which is a structural block diagram of an electronic device provided in an embodiment of the present application. The electronic device 1000 includes: at least one processor 1001, at least one communication interface 1002, at least one memory 1003 and at least one communication bus 1004. Among them, the communication bus 1004 is used to realize direct connection and communication between these components, the communication interface 1002 is used to communicate signaling or data with other node devices, and the memory 1003 stores machine-readable instructions executable by the processor 1001. When the electronic device 1000 is running, the processor 1001 communicates with the memory 1003 through the communication bus 1004, and the above-mentioned picking path optimization method is executed when the machine-readable instructions are called by the processor 1001.

[0158] For example, the processor 1001 of an embodiment of the present application reads a computer program from the memory 1003 via the communication bus 1004 and executes the computer program to implement the following method: Step S101: Obtain storage location layout information corresponding to multiple orders to be planned, wherein the order to be planned includes one or more goods to be picked, and the storage location layout information includes the warehouse area location corresponding to the goods to be picked, the channel location within the warehouse area, and the storage location location within the channel. Step S102: Generate at least one order batch and picking tasks corresponding to each order batch based on the storage location layout information, wherein each order to be planned is included in an order batch. Step S103: Optimize the picking path for the picking task according to the picking strategy.

[0159] Among them, the processor 1001 includes one or more, which can be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 1001 can be a general-purpose processor, including a central processing unit (CPU), a micro control unit (MCU), a network processor (NP) or other conventional processors; it can also be a special-purpose processor, including a neural network processor (NPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. Moreover, when there are multiple processors 1001, some of them can be general-purpose processors and the other part can be special-purpose processors.

[0160] The memory 1003 includes one or more, which may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0161] It will be understood that the structure shown in FIG10 is for illustration only, and the electronic device 1000 may also include more or fewer components than those shown in FIG10 , or have a configuration different from that shown in FIG10 . The components shown in FIG10 may be implemented using hardware, software, or a combination thereof. In an embodiment of the present application, the electronic device 1000 may be, but is not limited to, a physical device such as a desktop computer, a laptop computer, a smart phone, a smart wearable device, an in-vehicle device, and may also be a virtual device such as a virtual machine. In addition, the electronic device 1000 is not necessarily a single device, but may also be a combination of multiple devices, such as a server cluster, and the like.

[0162] An embodiment of the present application further provides a computer-readable storage medium storing computer program instructions. When the computer program instructions are executed by a computer, the computer executes the picking path optimization method described in the aforementioned method embodiment.

[0163] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0164] In addition, the units described as separate components may or may not be physically separate, and 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0165] Furthermore, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0166] It should be noted that if the function is implemented in the form of a software function module 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 the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0167] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0168] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A picking path optimization method, characterized in that: include: Obtaining storage location layout information corresponding to a plurality of orders to be planned, wherein the orders to be planned include one or more commodities to be picked, and the storage location layout information includes a storage area location, an aisle location within the storage area, and a storage location location within the aisle corresponding to the commodities to be picked; generating at least one order batch and a picking task corresponding to each order batch according to the storage location layout information, wherein each order to be planned is included in one order batch; The picking path of the picking task is optimized according to the picking strategy.

2. The picking path optimization method according to claim 1, characterized in that: Optimizing the picking path for the picking task according to the picking strategy includes: Optimizing the path within the aisle for the picking task according to the picking strategy to obtain an aisle picking sequence within the aisle; The picking task is optimized for paths between channels according to the picking sequence within the channel and the shortest path rule to obtain the picking sequence within the storage area.

3. The picking path optimization method according to claim 2, characterized in that: The picking strategy includes at least one of the following: Choose the picking path with the shortest distance in the aisle, choose the picking path with the least number of ladders to climb in the aisle, choose the picking path with the least number of squats in the aisle, and choose the picking path with the least number of turns in the aisle.

4. The picking path optimization method according to claim 2, characterized in that: The inter-aisle path optimization of the picking task according to the picking sequence in the aisle and the shortest path rule to obtain the picking sequence in the warehouse area includes: Constructing a passive shortest path problem according to the picking order in the channel and the shortest path rule; The picking order in the storage area is determined by solving the passive shortest path problem.

5. The picking path optimization method according to claim 4, characterized in that: The constructing of a passive shortest path problem according to the picking order in the channel and the shortest path rule includes: Determine two nodes corresponding to the channel according to the picking order within the channel corresponding to the channel, wherein the nodes represent the shortest paths within the channel covering the storage locations corresponding to the picking order within the channel, and the picking orders corresponding to the two nodes are opposite; Determining the picking order in the storage area by solving the passive shortest path problem includes: Calculate the adjacent distance between nodes of two adjacent channels; Determine, based on the adjacent distances, a plurality of target nodes that minimize the distance between the first channel and the last channel, wherein each channel corresponds to a target node; The picking order in the storage area is determined according to the multiple target nodes.

6. The picking path optimization method according to claim 1, characterized in that: Generate multiple picking tasks based on the storage location layout information, including: For an order batch, a greedy algorithm is used to generate the multiple picking tasks according to the storage location layout information.

7. The picking path optimization method according to claim 6, characterized in that: The generating the plurality of picking tasks according to the storage location layout information by using a greedy algorithm includes: Determine a seed product, wherein the seed product is a product that has the most channels shared with other products to be selected and has the smallest number of channels, columns, and layers among the products to be selected that have not been arranged; Initialize the current picking task using the seed product; Determine an inserted commodity from the commodities to be picked that have not been scheduled using a greedy rule, wherein the inserted commodity is a commodity to be picked that has the lowest insertion cost when inserted into the current picking task; The inserted commodity is inserted into the current picking task until the number of commodities to be picked in the current picking task is greater than a quantity threshold.

8. The picking path optimization method according to claim 7, characterized in that: The insertion cost represents the added value of the picking path after the product to be picked is inserted.

9. The picking path optimization method according to any one of claims 1 to 8, characterized in that: Generating at least one order batch according to the storage location layout information includes: Determining whether the order to be planned includes multiple items to be picked; If the order to be planned includes multiple commodities to be picked, the multiple orders to be picked are divided into multiple order batches according to the storage location layout information and the batching conditions; otherwise, one order batch is generated according to all the orders to be planned.

10. The picking path optimization method according to claim 9, characterized in that: The step of dividing the plurality of to-be-picked orders into a plurality of order batches according to the storage location layout information and the batching conditions includes: Determine the order batch with the smallest number of covered storage areas according to the constraint conditions; Among them, the constraints include: one order to be picked is assigned to an order batch, the number of orders to be picked in an order batch is not greater than the maximum number of orders to be picked that can be assigned to the order batch, and the number of goods to be picked for a warehouse area in an order batch is not greater than the total number of orders to be picked.

11. The picking path optimization method according to claim 9, characterized in that: The step of dividing the plurality of to-be-picked orders into a plurality of order batches according to the storage location layout information and the batching conditions includes: Constructing a commodity location set corresponding to the to-be-picked order, wherein the values ​​in the commodity location set represent the warehouse area numbers where the to-be-picked commodities in the to-be-picked order are located; Sort the values ​​in the product location set and concatenate them into a string Sort multiple orders to be picked according to character strings corresponding to the multiple orders to be picked; The sorted orders to be picked are divided according to the maximum number of orders to be picked that can be allocated to the order batch.

12. A picking path optimization device, characterized in that: include: an acquisition module, configured to acquire storage location layout information corresponding to a plurality of orders to be planned, wherein the orders to be planned include one or more commodities to be picked, and the storage location layout information includes the warehouse area locations, aisle locations within the warehouse area, and storage location locations within the aisles corresponding to the commodities to be picked; a generating module, configured to generate at least one order batch and a picking task corresponding to each order batch according to the storage location layout information, wherein each order to be planned is included in one order batch; The optimization module is used to optimize the picking path of the picking task according to the picking strategy.

13. An electronic device, characterized in that: include: processor, memory, and bus; The processor and the memory communicate with each other via the bus; The memory stores computer program instructions that can be executed by the processor, and the processor calls the computer program instructions to execute the picking path optimization method according to any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, which, when executed by a computer, enable the computer to execute the picking path optimization method according to any one of claims 1 to 11.

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