Method, device and storage medium for shelving goods

Abstract

The present application provides a method, device and storage medium for shelving goods, which belongs to the field of logistics automation technology. The above method includes: obtaining the order goods type of the goods to be shelved in the target order, the number of available shelves of each of the available shelves, and the type of goods that can be placed in each available shelf; according to the order goods type, the number of available shelves of each shelf, and the type of goods that can be placed in each available shelf, through a heuristic algorithm, determine the maximum number of order goods types that can be placed on each shelf; determine the first shelf with the largest number of the largest number of order goods types among the multiple shelves to obtain the target shelf; and shelve the goods to be shelved in the target order to the target shelf. The present application can reduce the distance and time consumed by manual or automatic guided vehicles when shelving goods, thereby improving the efficiency of shelving goods.

Description

Technical Field

[0001] The present application relates to the field of logistics automation technology, and in particular to a method, device and storage medium for shelving goods. Background Art

[0002] Smart warehousing is a warehouse management solution within the field of logistics automation technology. In smart warehousing, the act of placing incoming goods onto shelves is called shelving. Existing shelving solutions often target shelves with similar available compartments and select shelves based on their vacancy rates. Existing shelving solutions primarily optimize storage space by lowering the shelf's center of gravity to improve stability and safety, but lack technical solutions for improving shelving efficiency. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to provide a method, device and machine-readable storage medium for shelving goods, so as to solve the technical problem of low shelving efficiency in the prior art.

[0004] To achieve the above objectives, the present application provides, in a first aspect, a method for shelving goods, comprising:

[0005] Obtain the order goods type of the goods to be put on the shelves in the target order, the number of available shelves of each of the multiple shelves, and the type of goods that can be placed in each available shelf;

[0006] Based on the types of goods ordered, the number of available compartments on each shelf, and the types of goods that can be placed in each available compartment on each shelf, a heuristic algorithm is used to determine the maximum number of order goods that can be placed on each shelf.

[0007] Determine the first shelf with the largest number of the largest order items among the multiple shelves to obtain a target shelf;

[0008] Put the goods to be put on the shelves in the target order to the target shelves.

[0009] In an embodiment of the present application, the maximum number of types of order goods that can be placed on each shelf is determined based on the types of ordered goods, the number of available compartments on each shelf, and the types of goods that can be placed in each available compartment on each shelf, including: taking the intersection of the types of ordered goods and the types of goods that can be placed in each available compartment to determine the types of order goods that can be placed in each available compartment; for each shelf, based on the number of available compartments and the types of order goods that can be placed in each available compartment, determining the maximum number of types of order goods that can be placed on each shelf through a heuristic algorithm.

[0010] In an embodiment of the present application, the number of types of goods that can be placed corresponding to each available cargo grid is one; based on the number of available cargo grids and the types of order goods that can be placed in each available cargo grid, a heuristic algorithm is used to determine the maximum number of types of order goods that can be placed on the shelf, including: based on the number of available cargo grids and the types of order goods that can be placed, a heuristic algorithm is used to determine the maximum matching relationship between the types of order goods that can be placed and the available cargo grids; based on the maximum matching relationship, the total number of available cargo grids that match the order goods types is determined as the maximum number of types of order goods that can be placed on the shelf.

[0011] In an embodiment of the present application, determining a first shelf with the largest number of types of goods in the largest order among multiple shelves to obtain a target shelf includes: when there are multiple first shelves, determining a second shelf to obtain the target shelf, wherein the second shelf is the first shelf among the multiple first shelves, wherein the deviation between the number of available cargo compartments and the number of types of goods in the order is within a preset deviation range.

[0012] In an embodiment of the present application, there are multiple first shelves; determining the first shelf with the largest number of types of goods in the largest order among the multiple shelves to obtain the target shelf includes: selecting the second shelf with the smallest deviation between the number of available cargo compartments and the number of types of ordered goods as the target shelf.

[0013] In an embodiment of the present application, there are multiple second shelves, and the deviations of the multiple second shelves are the same; determining the first shelf with the largest number of types of goods in the largest order among the multiple shelves to obtain the target shelf also includes: obtaining the historical types of goods of the historical picking orders for goods taken off the shelves, and the existing types of goods on the second shelves; determining the correlation between the existing types of goods and the types of goods in the order on each second shelf based on the types of goods in the order, the historical types of goods, and the existing types of goods on each second shelf; and selecting the second shelf with the highest correlation as the target shelf.

[0014] In an embodiment of the present application, a correlation between the existing goods types and the ordered goods types on each second shelf is determined based on the ordered goods types, the historical goods types, and the existing goods types on each second shelf. The method includes: for each second shelf, taking the union of the ordered goods types and the existing goods types to obtain a set of goods types when the target order is assigned to the second shelf; and determining the number of frequent goods combination subsets in the set of goods types to obtain the correlation, wherein the frequent goods combination subsets are the set of goods combinations in the set of goods types whose appearance ratio in historical picking orders is greater than a preset number ratio threshold.

[0015] In an embodiment of the present application, there are multiple second shelves with the same degree of association; the distance between each of the multiple second shelves with the same degree of association and the workstation is determined; the workstation is used to receive goods from the second shelves with the same degree of association, and / or the workstation is used to distribute goods arriving at the second shelves with the same degree of association; the second shelf with the same degree of association with the smallest distance is selected as the target shelf.

[0016] In an embodiment of the present application, putting the goods to be put on the shelves in the target order on the target shelf includes: when there are multiple target shelves, allocating the target order to a randomly selected target shelf.

[0017] A second aspect of the present application provides an apparatus for shelving goods, comprising: a memory configured to store instructions; a processor configured to call the instructions from the memory and to implement the method for shelving goods provided in the above embodiment when executing the instructions.

[0018] A third aspect of the present application provides a machine-readable storage medium having stored thereon instructions for causing a machine to execute the method for shelving goods provided in the above embodiment.

[0019] The above technical solution first considers that each shelf may include multiple compartments, and only some of the compartments may have space to accommodate more goods. Therefore, the compartments on each shelf that can accommodate more goods are selected as available compartments. Then, the number of available compartments on each shelf and the types of goods that can be placed in each available compartment are counted. In combination with the types of goods in the order, a heuristic algorithm is used to match as many types of goods in the target order as possible to its available compartments for each shelf. Then, among the multiple shelves, the first shelf with the largest number of order goods that can be placed is selected, that is, the shelf with the most types of goods in the target order is matched as the target shelf. The goods to be shelved in the target order are then shelved on the target shelf. Therefore, when shelving goods according to the method for shelving goods provided in the embodiment of the present application, multiple goods in the target order can be shelved on the target shelf at the same time, thereby avoiding the need to shelve the various goods in the target order on multiple shelves in a dispersed manner. Therefore, the method for shelving goods provided in the embodiment of the present application can reduce the distance and time required by manual or automatic guided vehicles when shelving goods, thereby improving the efficiency of shelving goods.

[0020] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present application but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0022] Figure 1 The following schematically shows a flow chart of a method for shelving goods according to an embodiment of the present application;

[0023] Figure 2 A flow chart of another method for shelving goods according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0024] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0025] If there are descriptions involving "first", "second", etc. in the embodiments of this application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0026] In existing methods for shelving goods, the main factor considered for shelving goods is the vacancy rate of the shelves. For example, if the vacancy rate of the shelves is high, it is more likely to be allocated goods. Since there are many types of goods in actual warehousing scenarios, if the vacancy rate of the shelves is simply considered, various types of goods will be randomly distributed on each shelf. If so, then in the process of shelving goods, workers or intelligent automatic guided vehicles may need to walk through multiple rear shelves before they can distribute the goods in the order. Therefore, existing methods for shelving goods will consume extra distance and time. Based on the above analysis of the prior art and the problems found, the embodiments of the present application provide the following method for shelving goods.

[0027] Figure 1The following schematically shows a flow chart of a method for shelving goods according to an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a method for shelving goods, which may include the following steps:

[0028] S120: Obtain the order goods type of the goods to be put on the shelves in the target order, the number of available slots of each of the multiple shelves, and the type of goods that can be placed in each available slot;

[0029] S140, determining the maximum number of order goods types that can be placed on each shelf using a heuristic algorithm based on the types of goods in the order, the number of available compartments on each shelf, and the types of goods that can be placed in each available compartment on each shelf;

[0030] S160: Determine the first shelf with the largest number of goods of the largest order among the multiple shelves to obtain a target shelf;

[0031] S180: Put the goods to be put on the shelves in the target order onto the target shelves.

[0032] In the method for shelving goods provided in the embodiments of the present application, first, it is considered that each shelf may include multiple compartments, and that only some compartments may have space for more goods. Therefore, the compartments on each shelf that can accommodate more goods are selected as available compartments. The number of available compartments on each shelf and the types of goods that can be placed in each available compartment are then counted. Based on the types of goods in the order, a heuristic algorithm is used to match as many of the goods types in the target order as possible to the available compartments on each shelf. Then, among the multiple shelves, the first shelf with the largest number of order goods types that can be placed is selected (i.e., the shelf with the most goods types in the target order) as the target shelf. The goods to be shelved in the target order are then shelved on the target shelf. Therefore, when shelving goods according to the method for shelving goods provided in the embodiments of the present application, multiple goods in the target order can be shelved on the target shelf at once, avoiding the need to shelve the various goods in the target order across multiple shelves in a dispersed manner. Therefore, the method for shelving goods provided in the embodiments of the present application can reduce the distance and time required for manual or automated guided vehicles (AGVs) to shelve goods, thereby improving the efficiency of shelving goods. Furthermore, through the method for shelving goods provided in the embodiments of the present application, after goods are shelved, the various shelves on the target shelf are filled with goods from the target order. During the subsequent unshelving process, workers or automated guided vehicles can also retrieve multiple goods from the target shelf at once. Therefore, the method for shelving goods provided in the embodiments of the present application can also improve the efficiency of picking goods when they are unshelved.

[0033] It is understandable that the types of goods that can be placed in each available cargo box can be multiple or one, and the types of goods actually placed in each available cargo box can be a single type or multiple. For example, if an available cargo box is empty, that is, no goods are placed in it, then the types of goods that can be placed in this empty cargo box can be multiple. If an available cargo box already has existing goods placed in it, and the type of existing goods is one, then the type of goods that can be placed in this available cargo box is also one. As another example, if an available cargo box already has existing goods placed in it, and the types of existing goods can also be multiple, then the types of goods that can be placed in this available cargo box are also multiple, and the types of goods that can be placed correspond one-to-one with the types of existing goods. Heuristic algorithms are algorithms constructed based on intuition or experience, and provide feasible solutions with acceptable computing power and time expenditure. Heuristic algorithms may include genetic algorithms, bipartite graph maximum matching algorithms, etc. Bipartite graph maximum matching algorithms may further include: Hungarian algorithm, Kuhn-Munkres algorithm, Hopcroft-Karp algorithm, etc., which are used to solve the matching maximization problem of bipartite graphs. The bipartite graph maximum matching algorithm is used to determine the maximum number of order goods types that can be placed on the shelf based on the order goods types, the number of available shelves on the shelf, and the types of goods that can be placed on the available shelves.

[0034] Specifically, step S140 may first preliminarily determine the types of goods included in the order goods types that can be placed in each available cargo grid based on the order goods types and the types of goods that can be placed in the available cargo grids; then, through the bipartite graph maximum matching algorithm, each available cargo grid may be allocated to one or more order goods types that can be placed in the target order as much as possible, thereby determining the maximum number of order goods types that can be placed on the shelf.

[0035] In some embodiments of the present application, step S140 includes:

[0036] For each available slot, take the intersection of the order goods type and the available goods type to determine the order goods type that can be placed in each available slot;

[0037] For each shelf, the maximum number of order goods types that can be placed on each shelf is determined through the bipartite graph maximum matching algorithm based on the number of available shelves and the types of order goods that can be placed on each available shelf.

[0038] It can be understood that the types of goods that can be placed in each available cargo grid are limited, and the types of ordered goods in the target order are also limited. Therefore, the types of ordered goods that can be placed in the available cargo grid are determined by taking the intersection method, that is, the types of ordered goods that can be placed in the available cargo grid and are included in the target order.

[0039] In some embodiments of the present application, each available shelf may be, for example, an empty shelf, and each empty shelf can accommodate all types of goods. Then, the types of goods that can be placed in the available shelves are the types of goods in the target order.

[0040] In some embodiments of the present application, the multiple shelves in a shelf may include available shelves with goods already placed in them, as well as empty shelves. The types of goods that can be placed in the available shelves can be determined by taking the intersection of the order goods types and the available goods types for the available shelves. For empty shelves, the types of goods that can be placed in the shelves are the order goods types of the target order.

[0041] Furthermore, in some embodiments of the present application, the number of types of goods that can be placed in each available shelf is one. Based on the number of available shelves and the types of ordered goods that can be placed in each available shelf, a maximum number of ordered goods types that can be placed on the shelf is determined using a bipartite graph maximum matching algorithm, including:

[0042] Based on the number of available slots and the types of goods that can be placed, a maximum matching relationship between the types of goods that can be placed and the available slots is determined using a bipartite graph maximum matching algorithm.

[0043] According to the maximum matching relationship, the total number of available shelves that match the order goods types is determined as the maximum number of order goods types that can be placed on the shelf.

[0044] Specifically, taking the Hungarian algorithm as an example of the maximum matching algorithm for bipartite graphs, after determining the number of available shelves and the types of goods that can be placed on the shelves, a two-dimensional matrix of available shelves-types of goods that can be placed on the shelves, or a bipartite graph of available shelves-types of goods that can be placed on the shelves can be determined for each shelf. Taking the two-dimensional matrix of available shelves-types of goods that can be placed on the shelves as an example, as shown in the following matrix (1), each element in this two-dimensional matrix can be 0 or 1, and They are used to express the available shelves and the types of goods that can be placed in the order. For example, when When the element value of is 1, it means that the first available shelf in the current shelf is allocated to the first type of goods in the order type; on the contrary, if When the element value is 0, it means that the first available shelf in the current shelf is not allocated to the first type of goods in the order goods category. Due to the restriction that the number of available goods categories corresponding to each available shelf is one; therefore, if The value is 1, then The rest of the elements in are all 0.

[0045]

[0046] Continuing with the above example, the Hungarian algorithm can be used to obtain the maximum matching relationship between the types of goods that can be placed on each shelf and the available shelves, for example:

[0047]

[0048] That is, the total number of available shelves in the current shelf that match the order type is determined as the maximum number of order types that can be placed on the shelf. .

[0049] Furthermore, in the embodiment of the present application, after determining the maximum number of order types that a shelf can hold, the shelf's fulfillment rate for the target order can be calculated based on the order types and the maximum number of order types that the shelf can hold. If the fulfillment rate is 1, it means that the current shelf can hold all types of goods for the target order. If the number of shelves with a fulfillment rate of 1 is 1, the goods to be put on the shelf in the target order can be directly put on the shelf.

[0050] The method for shelving goods provided in the embodiments of the present application also takes into account the problem of avoiding the accumulation of the same type of goods on a single shelf. Therefore, in some embodiments, in the method for shelving goods provided in the embodiments of the present application, step S160 includes:

[0051] In the case that there are multiple first shelves, a second shelf is determined to obtain a target shelf, wherein the second shelf is a first shelf among the multiple first shelves whose deviation between the number of available shelves and the number of ordered goods types is within a preset deviation range.

[0052] First, each first shelf can hold the same maximum number of order items, meaning it can hold the same number of items in the target order. Since each item requires at least one available slot, limiting the number of available slots on the second shelf to those within a preset tolerance range between the number of available slots and the number of ordered items reduces the number of slots occupied by items of the same type, thereby reducing the accumulation of similar items on the shelves.

[0053] Considering the problem of avoiding the accumulation of the same type of goods on a single shelf, in other embodiments, step S160 includes:

[0054] The first shelf with the smallest deviation between the number of available shelves and the number of ordered goods types is selected as the target shelf.

[0055] It can be understood that for each first shelf, a corresponding deviation can be obtained.

[0056] Specifically, the above deviations may be sorted from small to large, and the first shelf with the smallest deviation may be selected as the target shelf.

[0057] In the above embodiment, the deviations are sorted from small to large, and the first shelf with the smallest deviation is selected as the target shelf, which complies with the principle of minimum deviation and can also reduce the number of cargo compartments occupied by the same type of goods.

[0058] In some embodiments, whether setting a preset deviation range for the deviation or selecting the target shelf based on the principle of minimum deviation, the same deviation may occur in two situations: one in which the number of available shelves exceeds the number of ordered goods, and the other in which the number of available shelves is less than the number of ordered goods. In both cases, the first shelf where the number of available shelves exceeds the number of ordered goods is preferentially selected as the target shelf. This satisfies the requirement that the maximum number of ordered goods can be placed on a shelf, i.e., each type of goods in the target order must be allocated to the target shelf as much as possible.

[0059] The method for shelving goods provided in the embodiment of the present application also takes into account the problem of whether various goods can be picked from the shelf at the same time. If multiple goods can be picked from the same shelf at the same time, the picking time and picking distance of the picker (worker or automatic guided vehicle) will be reduced.

[0060] Therefore, in some embodiments of the present application, when there are multiple second shelves and the deviations of the multiple second shelves are the same, step S160 further includes:

[0061] Get the historical goods types of the historical picking orders for goods removed from the shelves, and the existing goods types on the second shelf;

[0062] Determine the degree of correlation between the types of goods in the order and the types of goods in the order on each second shelf according to the types of goods in the order, the types of goods in the history, and the types of goods in the second shelf;

[0063] The second shelf with the highest correlation is selected as the target shelf.

[0064] It can be understood that historical picking orders refer to historical orders in which picking workstations picked goods for delivery to customers. Historical picking orders can come from workstations within the same warehouse or from workstations in different warehouses. Historical picking orders record the combinations of goods types in historical picking orders. The correlation between existing goods types and order goods types refers to the number of combined subsets of various goods types that appear with a frequency greater than a certain value in historical picking orders, resulting from combining the order goods types of the target order and the existing goods types for any second shelf.

[0065] Specifically, according to the ordered goods types, the historical goods types, and the existing goods types on each second shelf, determining the correlation between the existing goods types and the ordered goods types on each second shelf includes:

[0066] For each second shelf, take the union of the ordered goods types and the existing goods types to obtain the set of goods types when the target order is assigned to the second shelf;

[0067] The number of frequent commodity combination subsets in the commodity type set is determined to obtain a correlation degree, wherein the frequent commodity combination subset is a commodity combination subset in the commodity type set whose appearance ratio in historical picking orders is greater than a preset frequency ratio threshold.

[0068] It can be understood that the historical picking order may include multiple historical picking orders, and each historical picking order includes a certain type of goods. The preset occurrence ratio threshold can be set manually based on experience, or the preset occurrence ratio threshold can be set and adjusted based on the frequency of occurrence of a combination of certain types of goods in multiple historical picking orders. If the ratio of a certain combination of goods types in multiple historical picking orders is higher than the preset occurrence ratio threshold, it means that the combination of goods types appears frequently, otherwise it does not appear frequently. The goods type set includes multiple types of goods, and each type of goods is an element in the goods type set. By selecting elements in the goods type set, a goods combination subset can be obtained. Each goods combination subset corresponds to a goods combination. For example, if the goods type set includes N types of goods, then the number of goods combination subsets in the goods type set is , that is, from N types of goods, select from 1 to N types of goods to combine, a total of K combinations. Of course, in another example, we can also exclude the case of selecting only one type of goods.

[0069] Specifically, as an example, after selecting K product combination subsets, the frequency of occurrence of the product combinations represented by these product combination subsets in historical picking orders is determined. If this frequency is greater than or equal to a preset occurrence ratio threshold, the product combination represented by this product combination subset is determined to be a frequent set. The above steps are repeated for each product combination subset to determine whether it is a frequent set. Finally, the number of frequent sets in the product type set is obtained. This number of frequent sets represents the degree of correlation between the target order's order type and the existing product types at the workstation to be assigned.

[0070] As an example, the correlation between the order goods types of the target order and the existing goods types on the second shelf is determined by first using the Apriori algorithm to perform data mining on historical picking orders, and then analyzing the correlation between the target order and the goods types on the second shelf by calculating the support degree to obtain the number of frequent goods combination subsets.

[0071]

[0072] Refers to historical picking orders The order quantity, Refers to historical picking orders Includes a subset of cargo combinations The order quantity, when When the cargo combination subset is the frequent commodity combination subset, where Is a subset of the cargo combination support, is the preset occurrence ratio threshold.

[0073] The calculation formula for the correlation between the target order and the type of goods on the second shelf is:

[0074]

[0075] That is, correlation is the number of frequent product combination subsets of the target order. Product combination subset It is a subset of the set of goods types obtained by taking the union of the existing goods types on the second shelf and the order goods types of the target order.

[0076] According to the above technical solution, it can be determined that the method for shelving goods provided in the embodiment of the present application can determine the correlation between the existing goods types and the order goods types on the second shelf by obtaining the order goods types of the target order, the existing goods types on the second shelf, and the historical goods types of the historical picking orders, and select the second shelf with the highest correlation as the target shelf. Therefore, during the period of removing goods from the shelves, various goods on the shelves are more likely to be picked and removed from the shelves according to the needs of a certain picking order, thereby reducing the picking time and distance of the pickers, thereby improving the picking efficiency.

[0077] Furthermore, in the embodiment of the present application, there may be multiple second shelves with the same degree of association. Therefore, if there are multiple second shelves with the same degree of association, step S160 further includes:

[0078] Determining the distance between each of a plurality of second shelves with the same degree of association and a workstation; wherein the workstation is used to receive goods from the second shelves with the same degree of association, and / or the workstation is used to distribute goods arriving at the second shelves with the same degree of association;

[0079] The second shelf with the smallest distance and the same degree of association is selected as the target shelf.

[0080] By considering the distance between the second shelf and the workstation, the target order can be allocated to the second shelf closest to the workstation, which facilitates the unloading of goods from the second shelf to the workstation, or the loading of target orders from the workstation to the second shelf, thereby reducing the picking time and distance of the pickers and improving the picking efficiency.

[0081] It is understandable that the distance between the second shelf and the workstation may be, for example, a straight-line distance, or a distance that the picker needs to travel calculated according to a preset trajectory.

[0082] In addition, because there may be multiple target orders, in some embodiments of the present application, step S180 includes: when there are multiple target shelves, allocating the target order to a randomly selected target shelf.

[0083] In summary and see Figure 2 , the method for shelving goods provided in the embodiment of the present application first determines the maximum number of order goods types that can be placed on each shelf through a heuristic algorithm, and selects the first shelf with the largest number as the target shelf. If there are multiple first shelves, then based on the principle that the deviation between the number of available compartments in the first shelf and the number of order goods types is minimized, the second shelf that meets this principle in the first shelf is selected as the target shelf, and it is also necessary to consider that the second shelf with a larger number of available compartments than the number of order goods types is given priority. If there are multiple second shelves on the basis of considering the above-mentioned deviation, then the correlation between the existing goods types and the order goods types in different second shelves is also considered, and the second shelf with the highest correlation is selected as the target shelf. On this basis, if there are multiple second shelves with the same high correlation that can be selected as the target shelf, then the distance of these second shelves from the workstation is considered, and the second shelf with the shortest distance to the workstation is selected as the target shelf.

[0084] Therefore, the method for shelving goods provided in the embodiments of the present application can allocate target orders to the shelves with the highest fulfillment rates, avoid the accumulation of large quantities of similar goods on a single shelf, and because the allocation of target orders to shelves may also take into account relevance, the allocation of goods types on the shelves is more convenient for picking a variety of goods during the picking phase of goods removal. This method considers the shortest distance between shelves and workstations and can also improve the efficiency of allocating target orders and the picking efficiency of goods removal.

[0085] The present application also provides an apparatus for shelving goods, comprising a memory and a processor. The memory is configured to store instructions. The processor is configured to retrieve the instructions from the memory and, when executing the instructions, implement the method for shelving goods provided in the above embodiment.

[0086] An embodiment of the present application also provides a machine-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute the method for shelving goods in the above embodiment.

[0087] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0091] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0092] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0093] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0094] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0095] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for shelving goods, characterized in that: include: Obtaining the order goods type of the goods to be put on the shelves in the target order, the number of available slots of each of the multiple shelves, and the type of goods that can be placed in each of the available slots; Determining the maximum number of order goods types that can be placed on each shelf using a heuristic algorithm based on the types of goods in the order, the number of available compartments on each shelf, and the types of goods that can be placed on each available compartment on each shelf; Determine a first shelf having the largest number of the types of goods in the largest order among the multiple shelves to obtain a target shelf; Putting the goods to be put on the shelves in the target order onto the target shelves; The target shelf is the shelf that matches the largest number of goods in the target order; The types of goods that can be placed can be multiple or one; Determining the maximum number of types of ordered goods that can be placed on each shelf according to the types of ordered goods, the number of available compartments on each shelf, and the types of goods that can be placed on each available compartment on each shelf includes: For each available cargo slot, the intersection of the ordered goods type and the storable goods type is obtained to determine the storable ordered goods type of each available cargo slot; For each shelf, determining the maximum number of order goods types that can be placed on each shelf by using the heuristic algorithm according to the number of available compartments and the types of order goods that can be placed on each available compartment; The maximum number of order types that can be placed on the shelf is: ； in, and They are used to express the available shelves and the types of goods that can be placed in the order. The available shelves are allocated to the first type of goods that can be placed in the order In the case of goods, The value is 1; Available shelves are not allocated to the type of goods that can be placed in the order In the case of goods, The value is 0.

2. The method according to claim 1, characterized in that The number of types of goods that can be placed in each available cargo compartment is one; determining the maximum number of types of ordered goods that can be placed in the shelf by the heuristic algorithm based on the number of available cargo compartments and the types of ordered goods that can be placed in each available cargo compartment includes: According to the number of available shelves and the types of goods that can be placed in the order, determining, by the heuristic algorithm, a maximum matching relationship between the types of goods that can be placed in the order and the available shelves; According to the maximized matching relationship, the total number of the available shelves that match the order goods types is determined as the maximum number of order goods types that can be placed on the shelf.

3. The method according to claim 1, characterized in that The step of determining a first shelf having the largest number of the types of goods in the maximum order among the plurality of shelves to obtain a target shelf includes: In the case that there are multiple first shelves, a second shelf is determined to obtain a target shelf, wherein the second shelf is the first shelf in which the deviation between the number of available compartments among the multiple first shelves and the number of the types of ordered goods is within a preset deviation range.

4. The method according to claim 1, wherein There are multiple first shelves; and determining the first shelf with the largest number of the largest order type of goods among the multiple shelves to obtain the target shelf includes: The second shelf having the smallest deviation between the number of available shelves and the number of the ordered goods types is selected as the target shelf.

5. The method according to claim 3 or 4, characterized in that There are multiple second shelves, and the deviations of the multiple second shelves are the same; The step of determining the first shelf having the largest number of the types of goods in the maximum order among the plurality of shelves to obtain a target shelf further includes: Obtain the historical product types of the historical picking orders for the products removed from the shelves, and the existing product types on the second shelf; Determining, based on the ordered goods type, the historical goods type, and the existing goods type on each second shelf, a correlation between the existing goods type on each second shelf and the ordered goods type; The second shelf with the highest correlation degree is selected as the target shelf.

6. The method according to claim 5, characterized in that The determining, based on the ordered goods type, the historical goods type, and the existing goods type on each second shelf, a correlation between the existing goods type on each second shelf and the ordered goods type, includes: For each second shelf, taking the union of the ordered goods types and the existing goods types to obtain a set of goods types when the target order is assigned to the second shelf; The number of frequent commodity combination subsets in the commodity type set is determined to obtain the association degree, wherein the frequent commodity combination subsets are commodity combination subsets in the commodity type set whose appearance ratio in the historical picking orders is greater than a preset frequency ratio threshold.

7. The method according to claim 5, characterized in that There are multiple second shelves with the same degree of association; determining a distance between each of the plurality of second shelves having the same degree of association and a workstation; wherein the workstation is configured to receive goods from the second shelves having the same degree of association, and / or the workstation is configured to distribute goods arriving at the second shelves having the same degree of association; The second shelf with the smallest distance and the same degree of association is selected as the target shelf.

8. The method according to claim 1, characterized in that Putting the goods to be put on the shelves in the target order onto the target shelves includes: In the case that there are multiple target shelves, the target order is allocated to a randomly selected target shelf.

9. A device for placing goods on shelves, characterized in that: include: a memory configured to store instructions; A processor is configured to call the instructions from the memory and implement the method for goods shelving according to any one of claims 1 to 8 when executing the instructions.

10. A machine-readable storage medium, characterized in that The machine-readable storage medium stores instructions for causing a machine to execute the method for shelving goods according to any one of claims 1 to 8.

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