Method and device for shelving goods and storage medium

Abstract

The invention provides a method and device for shelving goods and a storage medium, and belongs to the technical field of logistics automation. The method comprises the following steps: obtaining order goods types of goods to be shelved in a target order, available goods lattice numbers of available goods lattices of a plurality of goods shelves, and types of goods which can be placed corresponding to each available goods lattice; according to the types of the order goods, the number of the available goods grids of each goods shelf and the types of the goods capable of being placed corresponding to each available goods grid in each goods shelf, the maximum type number of the order goods capable of being placed on each goods shelf is determined through a heuristic algorithm; determining a first goods shelf with the maximum order goods type quantity in the plurality of goods shelves to obtain a target goods shelf; and shelving the to-be-shelved goods in the target order to the target goods shelf. According to the invention, when goods are shelved, the distance required by a manual or automatic guide vehicle and the consumed time can be reduced, so that the goods shelving efficiency is improved.

Description

Technical Field

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

[0002] Smart warehousing is a warehouse management solution in the field of logistics automation technology. In smart warehousing, the act of storing goods entering the warehouse on the shelves in the warehouse is called shelving. Existing shelving solutions are often proposed for shelves with the same type of available cargo compartments, and the shelving solutions will select the shelves for goods to be shelved based on the shelf vacancy rate. Existing shelving solution optimization is mainly to optimize storage space by lowering the center of gravity of the shelf to improve the stability and safety of the shelf, but there is a lack of technical solutions for how to improve the efficiency of shelving. 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] In order to achieve the above-mentioned purpose, the first aspect of the present application provides a method for putting goods on shelves, comprising: Obtaining the order goods types of the goods to be put on the shelves in the target order, the number of available cargo cells of each of the multiple shelves, and the types of goods that can be placed in each available cargo cell; According to 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, the maximum number of types of goods in the order that can be placed on each shelf is determined through a heuristic algorithm; Determine the first shelf with the largest number of the largest order goods among the multiple shelves to obtain the target shelf; Put the goods to be put on the shelves in the target order to the target shelves.

[0005] In an embodiment of the present application, the maximum number of types of ordered 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 for each available compartment to determine the types of ordered goods that can be placed in each available compartment; for each shelf, determining the maximum number of types of ordered goods that can be placed on each shelf through a heuristic algorithm based on the number of available compartments and the types of ordered goods that can be placed in each available compartment.

[0006] In an embodiment of the present application, the number of types of goods that can be placed corresponding to each available cargo compartment is one; based on the number of available cargo compartments and the types of order goods that can be placed in each available cargo compartment, 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 compartments 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 compartments; based on the maximum matching relationship, the total number of available cargo compartments that match the order goods types is determined as the maximum number of types of order goods that can be placed on the shelf.

[0007] In an embodiment of the present application, determining a first shelf with the largest number of types of goods in a maximum 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 a first shelf among the multiple first shelves, wherein a deviation between the number of available cargo compartments and the number of types of goods in the order is within a preset deviation range.

[0008] 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.

[0009] 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 maximum 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 being 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 ordered types of goods in each second shelf based on the ordered types of goods, 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.

[0010] In an embodiment of the present application, the association degree between the existing goods types and the order goods types in each second shelf is determined according to the order goods types, the historical goods types and the existing goods types of each second shelf, including: for each second shelf, taking the union of the order goods types and the existing goods types to obtain a set of goods types when the target order is assigned to the second shelf; determining the number of frequent goods combination subsets in the goods type set to obtain the association degree, wherein the frequent goods combination subset is a goods combination subset in the goods type set whose appearance ratio in the historical picking order is greater than a preset number ratio threshold.

[0011] 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; 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; and the second shelf with the same degree of association with the smallest distance is selected as the target shelf.

[0012] 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.

[0013] 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 accordance with the above embodiment when executing the instructions.

[0014] A third aspect of the present application 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 provided in the above embodiment.

[0015] The above technical solution first considers that each shelf may include multiple cargo compartments, and only some cargo compartments may have space to accommodate more goods. Therefore, cargo compartments that can accommodate more goods in each shelf are selected as available cargo compartments; then the number of available cargo compartments of each shelf and the types of goods that can be placed in each available cargo compartment are counted, and combined with the types of goods in the order, through a heuristic algorithm, for each shelf, as many types of goods in the target order as possible are matched to its available cargo compartments; then among the multiple shelves, the first shelf with the largest number of order goods types that can be placed on the shelf is selected, that is, the shelf with the most types of goods in the target order is matched as the target shelf. Then the goods to be put on the shelves in the target order are put on the target shelf; therefore, when putting goods on the shelves according to the method for putting goods on the shelves provided in the embodiment of the present application, multiple goods in the target order can be put on the shelves on the target shelf at one time, thereby avoiding putting various goods in the target order on multiple shelves in a dispersed manner; therefore, the method for putting goods on the shelves provided in the embodiment of the present application can reduce the distance and time consumed by manual or automatic guided vehicles when putting goods on the shelves, thereby improving the efficiency of putting goods on the shelves.

[0016] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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 specific implementations, 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: Figure 1 A schematic diagram of a process for putting goods on shelves according to an embodiment of the present application is shown; Figure 2 A flowchart of another method for shelving goods according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme 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.

[0019] If there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used 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 the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. 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.

[0020] In the existing methods for putting goods on the shelves, the main factor considered for putting goods on the shelves is the vacancy rate of the shelves, that is, 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 allocated on each shelf. If so, then in the process of putting goods on the shelves, workers or intelligent automatic guided vehicles may need to walk through multiple rear shelves before they can allocate the goods in the order. Therefore, the existing methods for putting goods on the shelves 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 putting goods on the shelves.

[0021] Figure 1 The flowchart of a method for putting goods on shelves according to an embodiment of the present application is schematically shown. Figure 1As shown, the embodiment of the present application provides a method for putting goods on shelves, which may include the following steps: S120, obtaining the order goods types of the goods to be put on the shelves in the target order, the number of available cargo cells of each of the multiple shelves, and the types of goods that can be placed in each available cargo cell; S140, determining the maximum number of order goods types that can be placed on each shelf through a heuristic algorithm according to the types of order goods, the number of available cargo compartments on each shelf, and the types of goods that can be placed in each available cargo compartment on each shelf; S160, determining a first shelf with the largest number of the largest order types of goods among the multiple shelves to obtain a target shelf; S180: Put the goods to be put on the shelves in the target order on the target shelves.

[0022] In the method for putting goods on the shelves provided in the embodiment of the present application, it is first considered that each shelf may include multiple cargo compartments, and only some cargo compartments may have space for more goods. Therefore, the cargo compartments that can accommodate more goods in each shelf are selected as available cargo compartments. Then the number of available cargo compartments of each shelf and the types of goods that can be placed in each available cargo compartment are counted, and combined with the types of goods in the order, through a heuristic algorithm, as many types of goods in the target order as possible are matched to its available cargo compartments for each shelf. Then, among multiple shelves, the first shelf with the largest number of order goods types that can be placed on the shelf is selected, that is, the shelf with the most types of goods in the target order is matched as the target shelf. Then the goods to be put on the shelves in the target order are put on the target shelf. Therefore, when the goods are put on the shelves according to the method for putting goods on the shelves provided in the embodiment of the present application, multiple goods in the target order can be put on the shelves at the target shelf at one time, thereby avoiding putting various goods in the target order on multiple shelves in a dispersed manner. Therefore, the method for putting goods on the shelves provided in the embodiment of the present application can reduce the distance and time consumed by manual or automatic guided vehicles when putting goods on the shelves, thereby improving the efficiency of putting goods on the shelves. Furthermore, through the method for putting goods on shelves provided by the embodiment of the present application, after putting goods on shelves, various cargo compartments in the target shelves will be filled with goods in the target order, and in the subsequent process of taking goods off shelves, workers or automatic guided vehicles can also obtain multiple goods at the target shelves at one time. Therefore, the method for putting goods on shelves provided by the embodiment of the present application can also improve the picking efficiency of goods off shelves.

[0023] It can be understood that the types of goods that can be placed corresponding to each available cargo grid can be multiple or one, and the types of goods actually placed in each available cargo grid can be single or multiple. For example, if a certain available cargo grid is an empty cargo grid, that is, no goods are placed, then the types of goods that can be placed in the empty cargo grid can be multiple. A certain available cargo grid has been placed with existing goods, and the type of existing goods is one, then the type of goods that can be placed in the available cargo grid is one. As another example, a certain available cargo grid has been placed with existing goods, and the types of existing goods can also be multiple, then the types of goods that can be placed in the available cargo grid are also multiple, and the types of goods that can be placed correspond to the types of existing goods one by one. Heuristic algorithms are algorithms constructed based on intuition or experience, and give feasible solutions under acceptable computing power and time expenditure. Heuristic algorithms may include genetic algorithms, bipartite graph maximum matching algorithms, etc. The bipartite graph maximum matching algorithm may also 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 in the available shelves.

[0024] 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 compartment based on the order goods types and the types of goods that can be placed in the available cargo compartments; then, through the bipartite graph maximum matching algorithm, each available cargo compartment 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.

[0025] In some embodiments of the present application, step S140 includes: For each available cargo grid, take the intersection of the order goods type and the placeable goods type to determine the order goods type that can be placed in each available cargo grid; For each shelf, according to the number of available shelves and the types of order goods that can be placed in each available shelf, the maximum number of order goods types that can be placed on each shelf is determined through the bipartite graph maximum matching algorithm.

[0026] It can be understood that each available cargo grid has a limited number of possible cargo types, and the number of cargo types in the target order is also limited. Therefore, the number of possible cargo types in the available cargo grid is determined by taking the intersection method, that is, the number of cargo types in the target order that can be placed in the available cargo grid.

[0027] In some embodiments of the present application, each available cargo compartment may be, for example, an empty cargo compartment, and each empty cargo compartment may be used to place all types of goods. Then, the types of goods that can be placed in the available cargo compartments are the types of goods in the target order.

[0028] In some embodiments of the present application, the plurality of shelves in the shelf may include available shelves with goods already placed therein and empty shelves. Then, the types of goods that can be placed in the available shelves can be determined by taking the intersection of the types of ordered goods and the types of goods that can be placed therein for the available shelves with goods already placed therein; for the empty shelves, the types of goods that can be placed therein are the types of goods of the target order.

[0029] Further, in some embodiments of the present application, the number of types of goods that can be placed in each available shelf is one. According to the number of available shelves and the types of ordered goods that can be placed in each available shelf, the maximum number of types of ordered goods that can be placed on the shelf is determined by a bipartite graph maximum matching algorithm, including: According to the number of available shelves and the types of goods that can be placed, the maximum matching relationship between the types of goods that can be placed and the available shelves is determined through the bipartite graph maximum matching algorithm; 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.

[0030] Specifically, taking the Hungarian algorithm as an example of the maximum matching algorithm of the bipartite graph, after determining the number of available shelves and the types of goods that can be placed, a two-dimensional matrix of available shelves-types of goods that can be placed, or a bipartite graph of available shelves-types of goods that can be placed, can be determined for each shelf. Taking the two-dimensional matrix of available shelves-types of goods that can be placed 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 goods category; 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 available order goods. Since the restriction conditions include: the number of available goods types corresponding to each available shelf is one; therefore, if The value is 1, then The rest of the elements in are all 0.

[0031]

[0032] Based on 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:

[0033] That is, the total number of available shelves in the current shelf that match the order goods type is determined as the maximum number of order goods types that can be placed on the shelf. .

[0034] Furthermore, in the embodiment of the present application, after determining the maximum number of order goods that can be placed on the shelf, the satisfaction rate of the shelf for the target order can be obtained according to the order goods and the maximum number of order goods that can be placed on the shelf. If the satisfaction rate is 1, it means that the current shelf can hold all types of goods of the target order. If the number of shelves with a satisfaction rate of 1 is 1, the goods to be put on the shelf in the target order can be directly put on the shelf.

[0035] The method for putting goods on shelves provided in the embodiment 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 putting goods on shelves provided in the embodiment of the present application, step S160 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 a first shelf among the multiple first shelves for which a deviation between the number of available cargo compartments and the number of ordered goods types is within a preset deviation range.

[0036] First, the maximum number of order goods types that can be placed on each first shelf is the same, that is, each first shelf can place the same number of goods in the target order. Since each type of goods needs to occupy at least one available shelf, by further limiting the second shelf to the first shelf whose deviation between the number of available shelves and the number of order goods types is within a preset deviation range, the number of shelves occupied by the same type of goods can be reduced, thereby reducing the accumulation of the same type of goods on the shelf.

[0037] Considering the problem of avoiding the accumulation of the same type of goods on a single shelf. In other embodiments, step S160 includes: 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.

[0038] It can be understood that for each first shelf, a corresponding deviation can be obtained.

[0039] Specifically, the above deviations may be sorted from small to large, wherein the first shelf with the smallest deviation may be selected as the target shelf.

[0040] 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.

[0041] In some embodiments, whether it is setting a preset deviation range for the deviation or selecting a target shelf according to the principle of minimum deviation, the same deviation may occur in two situations, one is that the number of available shelves is greater than the number of ordered goods, and the other is that the number of available shelves is less than the number of ordered goods. In the above two situations, the first shelf where the number of available shelves is greater than 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 the shelf, that is, the various goods in the target order need to be allocated to the target shelf as much as possible.

[0042] The method for putting goods on shelves 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.

[0043] 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: Obtain the historical goods types of the historical picking orders for goods removed from the shelves, and the existing goods types on the second shelf; Determine the correlation between the existing goods types and the ordered goods types in each second shelf according to the ordered goods types, the historical goods types and the existing goods types in each second shelf; The second shelf with the highest correlation is selected as the target shelf.

[0044] It can be understood that historical picking orders refer to historical orders for picking goods to be sent to customers after the picking workstation picks the goods. Historical picking orders can come from workstations in the same warehouse or from workstations in different warehouses. Historical picking orders record the combination of goods types in historical picking orders. The correlation between existing goods types and order goods types refers to the set of goods types obtained by putting the order goods types of the target order and the existing goods types together for any second shelf, and the number of subsets of various goods type combinations in the set of goods types that appear in historical picking orders with a frequency higher than a certain value.

[0045] 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: 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; 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 number ratio threshold.

[0046] 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 according to 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, from 1 to N types of goods are selected for combination, and there are a total of K combinations. Of course, in another example, the case of selecting only one type of goods can also be excluded.

[0047] Specifically, as an example, after selecting K number of cargo combination subsets, the frequency of the cargo combination expressed by the cargo combination subset in the historical picking order is determined according to the cargo combination subset. If the frequency is greater than or equal to the preset occurrence ratio threshold, the cargo combination expressed by the cargo combination subset is determined to be a frequent set. The above steps are performed for each cargo combination subset to determine whether it is a frequent set, and finally the number of frequent sets in the cargo type set can be obtained, which is the correlation between the order cargo type of the target order and the existing cargo type of the workstation to be assigned.

[0048] 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 calculating the support to analyze the correlation between the target order and the goods types on the second shelf to obtain the number of frequent goods combination subsets.

[0049]

[0050] Refers to historical picking orders The order quantity, Refers to historical picking orders Included in the cargo combination subset The order quantity, when When the cargo combination subset is the frequent commodity combination subset, where Is a subset of cargo combinations The support of is the preset occurrence ratio threshold.

[0051] The calculation formula for the correlation between the target order and the type of goods on the second shelf is:

[0052] Relevance is the number of frequent commodity combination subsets of the target order. 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.

[0053] 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, so that during the period of taking goods off the shelves, various goods on the shelves are more likely to be picked and taken off 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.

[0054] Furthermore, in the embodiment of the present application, there may be multiple second shelves with the same degree of association. Therefore, when there are multiple second shelves with the same degree of association, step S160 further includes: Determine 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; The second shelf with the smallest distance and the same degree of association is selected as the target shelf.

[0055] 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, so as to facilitate the goods in the second shelf to be unloaded from the workstation, or the target order can be put on the second shelf from the workstation, thereby reducing the picking time and distance of the picker and improving the picking efficiency.

[0056] 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.

[0057] 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.

[0058] In summary, see Figure 2 , the method for shelving goods provided in the embodiment of the present application first determines the maximum number of ordered 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, based on the principle that the deviation between the number of available cargo compartments in the first shelf and the number of ordered goods types is the smallest, the second shelf in the first shelf that meets this principle is selected as the target shelf, and it is also necessary to consider that the second shelf with a larger number of available cargo compartments than the number of ordered goods types is given priority. If there are multiple second shelves on the basis of considering the above deviation, the correlation between the existing goods types and the ordered 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, the distances of these second shelves from the workstation are considered, and the second shelf with the shortest distance from the workstation is selected as the target shelf.

[0059] Therefore, the method for putting goods on shelves provided by the embodiment of the present application can allocate the target order to the shelf with the highest satisfaction rate, and can avoid the large accumulation of similar goods on a single shelf. Moreover, since the association degree may also be considered when allocating the target order to the shelf, the allocation of goods types in the shelf is more convenient for the picking of multiple goods in the picking link of goods off the shelf. This method considers the shortest distance between the shelf and the workstation, and can also improve the allocation efficiency of the target order and the picking efficiency of the picking link of goods off the shelf.

[0060] The embodiment of the present application also provides a device for putting goods on shelves, comprising: a memory and a processor. The memory is configured to store instructions. The processor is configured to call instructions from the memory and implement the method for putting goods on shelves according to the above embodiment when executing the instructions.

[0061] An embodiment of the present application also provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the method for shelving goods in the above embodiment.

[0062] 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 a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, 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 disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0063] 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 the 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 generate 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.

[0064] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0065] 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 instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0066] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0067] 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.

[0068] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. 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 memory (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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (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 temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0069] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0070] The above are only 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 modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for putting goods on shelves, characterized in that: include: Obtaining the order goods types of the goods to be put on the shelves in the target order, the number of available cargo spaces of each of the multiple shelves, and the types of goods that can be placed in each of the available cargo spaces; Determine the maximum number of order goods types that can be placed on each shelf by using a heuristic algorithm according to the types of the order goods, the number of available cargo compartments of each shelf, and the types of goods that can be placed in each available cargo compartment of each shelf; Determine a first shelf having the largest number of the types of goods in the maximum order among the multiple shelves to obtain a target shelf; Put the goods to be put on the shelves in the target order on the target shelves.

2. The method according to claim 1, characterized in that Determining the maximum number of order goods types that can be placed on each shelf according to the order goods types, the number of available cargo compartments of each shelf, and the types of goods that can be placed corresponding to each available cargo compartment in each shelf, includes: For each of the available cargo cells, the intersection of the ordered goods types and the types of goods that can be placed is taken to determine the types of ordered goods that can be placed in each of the available cargo cells; For each of the shelves, the maximum number of types of order goods that can be placed on each of the shelves is determined through the heuristic algorithm according to the number of available cargo compartments and the types of order goods that can be placed on each of the available cargo compartments.

3. The method according to claim 2, characterized in that The number of types of goods that can be placed in each of the available cargo compartments is one; the maximum number of types of order goods that can be placed on the shelf is determined by the heuristic algorithm according to the number of available cargo compartments and the types of order goods that can be placed in each of the available cargo compartments, including: According to the number of available cargo spaces and the types of goods that can be placed in the order, the heuristic algorithm is used to determine the maximum matching relationship between the types of goods that can be placed in the order and the available cargo spaces; According to the maximum matching relationship, the total number of the available cargo compartments that match the order goods types is determined as the maximum number of order goods types that can be placed on the shelf.

4. 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 cargo compartments and the number of the ordered goods types among the multiple first shelves is within a preset deviation range.

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

6. The method according to claim 4 or 5, 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 the target shelf further includes: Obtaining the historical goods types of the historical picking orders for goods removed from the shelves, and the existing goods types on the second shelf; Determine, according to the ordered goods type, the historical goods type and the existing goods type of each second shelf, the degree of association between the existing goods type in each second shelf and the ordered goods type; The second shelf with the highest correlation degree is selected as the target shelf.

7. The method according to claim 6, characterized in that The determining, according to the ordered goods type, the historical goods type and the existing goods type of each second shelf, the correlation between the existing goods type in each second shelf and the ordered goods type includes: For each of the second shelves, 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 subset is a commodity combination subset in the commodity type set whose appearance ratio in the historical picking order is greater than a preset frequency ratio threshold.

8. The method according to claim 6, characterized in that The number of the second shelves having the same degree of association is multiple; Determine the distance between each of the plurality of second shelves having the same degree of association and a workstation; wherein the workstation is used to receive goods from the second shelves having the same degree of association, and / or the workstation is used 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.

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

10. 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 putting goods on shelves according to any one of claims 1 to 9 when executing the instructions.

11. 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 9.

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