Information processing apparatus, information processing method, storage medium, and information processing system

Abstract

Embodiments of the present application relate to an information processing apparatus, an information processing method, a storage medium, and an information processing system, and more efficiently perform a picking operation. The information processing apparatus includes a reception unit and a determination unit. The reception unit receives a plurality of shelf data and a plurality of order data. The plurality of shelf data includes first identification information of one or more types of goods stored in a plurality of shelves, respectively. The plurality of order data includes second identification information of one or more types of goods to be picked from at least a portion of the plurality of shelves. The determination unit determines a processing order of the plurality of order data based on the shelf data so that a proportion of goods picked from one shelf in the plurality of order data is increased. The plurality of shelves can be moved to a work station provided with a storage container corresponding to at least a portion of the plurality of order data, respectively.

Description

Technical Field

[0001] The embodiments of the present invention relate to information processing apparatus, information processing method, storage medium, and information processing system. Background Technology

[0002] A logistics system using a rack-and-carry warehouse is known, in which racks (shelves for storing goods) that can be moved by AGVs (Automated Guided Vehicles) or workers are moved to workstations (picking stations) where goods picking operations are carried out.

[0003] Such shelving and conveying systems are mostly based on the premise of adding orders one by one in real time. That is, they are not designed to provide the system with a large number of orders at the same time intervals, and the processing order can be changed. Summary of the Invention

[0004] The purpose of this invention is to provide an information processing apparatus, information processing method, program, and information processing system that can perform picking operations more efficiently.

[0005] The information processing apparatus of this embodiment includes a receiving unit and a decision unit. The receiving unit receives multiple shelf data and multiple order data. The multiple shelf data includes first identification information of one or more items stored on the multiple shelves, and the multiple order data includes second identification information of one or more items picked from at least a portion of the multiple shelves. The decision unit determines the processing order of the multiple order data based on the shelf data, thereby increasing the proportion of items picked from the multiple order data from a single shelf. The multiple shelves can be moved to a workstation equipped with storage containers corresponding to at least a portion of the multiple order data. Attached Figure Description

[0006] Figure 1 This is a diagram showing the outline of the configuration of the information processing system in the implementation method.

[0007] Figure 2 This is a block diagram of an information processing apparatus according to an implementation method.

[0008] Figure 3 This is a diagram showing the calculation examples with priority.

[0009] Figure 4 This is a diagram showing the calculation examples with priority.

[0010] Figure 5 This is a diagram showing an example of the generated index.

[0011] Figure 6This is a diagram representing an example of a generated hierarchical cluster.

[0012] Figure 7 This is a diagram showing an example of orders sorted according to processing order.

[0013] Figure 8 This is a flowchart of the sequence determination process in the implementation method.

[0014] Figure 9 This is a hardware structure diagram of the information processing device involved in the implementation method.

[0015] Label Explanation

[0016] 10. Information Processing System

[0017] 11 workstations

[0018] 12 Storage Containers

[0019] 13 shelves

[0020] 14 Monitors

[0021] 30 shelves

[0022] 100 Information Processing Device

[0023] 101 Reception Department

[0024] 102 Priority Calculation Department

[0025] 103 Production Department

[0026] 104 Distance Calculation Unit

[0027] 105 Decision Department

[0028] 106 Output Control Unit

[0029] 121 Order Data Storage Department

[0030] 122 Shelf Data Storage Department

[0031] 123 Cluster Data Storage Department

[0032] 200 conveying device

[0033] 300 Network Detailed Implementation

[0034] Hereinafter, preferred embodiments of the information processing apparatus of the present invention will be described in detail with reference to the accompanying drawings. This embodiment, for example, can be applied to a logistics system using a rack-and-carry warehouse.

[0035] As mentioned above, in traditional shelving and conveying systems, orders are selected as the objects to be picked in the order they are generated.

[0036] On the other hand, in logistics systems primarily targeting B2B (Business to Business) transactions, sometimes all orders that should be processed within a certain period (e.g., one day) are provided in advance. In such cases, the processing order can be rearranged for multiple orders as long as it's before the delivery deadline. For example, by appropriately rearranging the order processing order in advance, it's possible to increase the proportion of items picked from multiple orders from a single shelf (simultaneous picking rate), thus improving the efficiency of shelf handling and picking operations. Previously, there was no consideration given to rearranging the order processing order with the aim of improving the overall operational efficiency of the system.

[0037] Therefore, in this embodiment, when multiple order data are provided as information on unprocessed orders, the processing order of the multiple orders is determined by referring to multiple shelf data related to multiple shelves that respectively store one or more types of goods, in order to improve the simultaneous picking rate. Since order data corresponds one-to-one with orders, determining the processing order of orders is equivalent to determining the processing order of order data. Similarly, the processing of orders can be expressed as the processing of order data.

[0038] Shelf data includes shelf IDs, which serve as information to identify the shelves, and identification information (first identification information) of one or more types of goods stored on each shelf as inventory at the current point in time. Shelf data may include, for example, identification information representing the type of each type of goods that constitute the set of goods stored on the shelves (goods set).

[0039] Order data includes an order ID as information to identify the order, and identification information (second identification information) of one or more items picked from at least a portion of multiple shelves. Order data, for example, represents a unit of outbound instruction for items bundled in the same storage container for the same recipient.

[0040] Figure 1 This is a diagram showing an outline of the configuration of the information processing system 10 (an example of a shelf-transfer system) according to this embodiment. Figure 1 As shown, the information processing system 10 includes an information processing device 100, a conveying device 200, a movable shelf 30, a workstation 11, multiple storage containers 12, a work shelf 13, a display 14, and a network 300.

[0041] Workstation 11 indicates the work area where workers 21 perform picking. Figure 1Only one workstation 11 is shown in the figure, but the information processing system 10 may also include multiple workstations 11.

[0042] The shelf 13 can be configured with multiple storage containers 12. Each storage container 12 corresponds to a different order, for example. The shelf 13 is configured with storage containers 12 that correspond to at least a portion of the order data from the multiple order data sets. The display 14 is, for example, a display device for displaying information such as work instructions output from the information processing device 100.

[0043] The conveying device 200 is a device used to move (transport) the rack 30 to the work station 11, such as an AGV. In this way, the rack 30 can be moved to the work station 11. Figure 1 Only one shelf 30 is shown, but the information processing system 10 has multiple shelves 30. The shelf 30 selected according to the determined order processing sequence is moved to the work station 11. When the shelf is moved by the operator 21, the conveying device 200 may not be required. The shelf 30 may, for example, have multiple partitions to store various types of goods.

[0044] Network 300 is a network that connects information processing device 100, conveying device 200, and display 14. Network 300 can also be any type of network such as the Internet and local area network (LAN). Network 300 can also be any network among wireless networks, wired networks, and networks with both wireless and wired connections.

[0045] like Figure 1 As shown, multiple storage containers 12 corresponding to multiple orders are placed on shelves 13, allowing operators 21 to perform picking for multiple orders in parallel. Alternatively, picking can be performed by picking robots or the like, replacing operators 21. Each storage container 12 is considered picked when all the items to be bundled have been placed inside. Completed orders (storage containers) are then removed from workstation 11 and placed into storage containers 12 corresponding to the next order.

[0046] Figure 2 This is a block diagram illustrating an example of the configuration of the information processing apparatus 100 according to this embodiment. For example... Figure 1 As shown, the information processing device 100 includes a receiving unit 101, a priority calculation unit 102, a generation unit 103, a distance calculation unit 104, a decision unit 105, an output control unit 106, an order data storage unit 121, a shelf data storage unit 122, and a cluster data storage unit 123.

[0047] The receiving unit 101 accepts inputs of various types of data used in the information processing device 100. For example, the receiving unit 101 accepts multiple order data and multiple shelf data that are the objects of processing. The order data and shelf data can be generated by any method, but for example, they can also be generated by an external system such as a system for receiving orders or a system for managing the inventory of goods. In addition, the input method for each data can also be any method, but for example, it can be a method of receiving data from an external system via network 300.

[0048] Order data includes at least an order ID and identification information for the goods contained in the order. The order ID may be represented, for example, as order O1, order O2, order O3, ... The identification information for the goods may be represented, for example, as goods A, goods B, goods C, ... The information included in the order data is not limited to this. For example, order data may also include the number and / or quantity of each good.

[0049] Shelf data includes at least the shelf ID and identification information of the goods stored on that shelf. Shelf IDs are, for example, represented as shelf R1, shelf R2, shelf R3, ... The information contained in shelf data is not limited to this. For example, shelf data may also include the number of each item and / or the storage location information of the item on which side and in which section of the shelf it is stored.

[0050] The receiving department 101 can also accept data other than order data and shelf data, such as product data including detailed information of each product (size, packaging shape, etc.) as supplementary input data.

[0051] Priority calculation unit 102 calculates the priority of each of the multiple shelves. The priority is referenced when the generation unit 103 generates an index. The priority is calculated, for example, in a way that the shelf that can perform picking operations more efficiently has a higher value. For example, priority calculation unit 102 calculates the priority using the following two methods.

[0052] (M1) Priority is calculated based on multiple order data, with the value being higher if the number of products whose identification information matches the identification information of the products contained in the order data is larger.

[0053] (M2) Priority is calculated by the number of order data that include identification information consistent with the identification information of the goods contained in the shelf data.

[0054] The aforementioned (M1) is equivalent to the following method: calculating the degree to which the set of goods in each order that have not yet been selected for shelf placement is covered (sufficient) by the set of goods placed on each shelf (the set of goods) as a priority. Coverage can be, for example, a value representing the number of identification information that matches the identification information of the goods included in the order data, or a value representing the proportion of that number, but is not limited to these.

[0055] The above (M2) is equivalent to a method that pre-calculates a shared priority for multiple order data. Details of each method will be described later.

[0056] Based on the calculated priority of each shelf, the generation unit 103 generates an index representing one or more shelves among the shelves containing goods whose identification information matches the identification information of the goods included in the order data, based on multiple order data. For example, the generation unit 103 prioritizes shelves with higher priority than other shelves as shelves for picking goods and generates an index representing the selected shelves. The index may include, for example, information about the shelf IDs of one or more shelves for picking goods.

[0057] The distance calculation unit 104 calculates the distance between the multiple indexes generated for each of the multiple order data. The method for calculating the distance will be described in detail later.

[0058] The decision unit 105 determines the processing order of multiple order data based on shelf data to increase the proportion of goods picked from multiple order data from a single shelf (e.g., the proportion of simultaneous picking from a single shelf: simultaneous picking rate). "Simultaneous" does not mean strictly consistent moments in time, but rather that picking can be performed from the same shelf. The decision unit 105 determines the processing order of multiple order data in a manner that the smaller the calculated distance, the closer the order. For example, the decision unit 105 performs hierarchical (agglomerated) clustering, which involves repeatedly merging orders corresponding to indices with small distances into clusters to generate clusters represented by a hierarchical structure. Furthermore, the decision unit 105 determines the order processing order so that orders within the same cluster are configured in a closer order.

[0059] Hierarchical clustering repeatedly identifies and groups the two clusters with the smallest distance between their assigned indexes into a single cluster, until all order data is grouped into one cluster. This allows for the clustering of multiple order data sets, ensuring that orders with close distances between their corresponding indexes are included in the same cluster.

[0060] The decision unit 105 stores a binary tree as a historical record of clustering in the cluster data storage unit 123. This binary tree has the clusters (or orders) before merging as child nodes (sub-clusters) and the clusters after merging as parent nodes (parent clusters). Additionally, the decision unit 105 stores a hierarchically constructed cluster representing the clustering results at the current time point in the cluster data storage unit 123. When all order data is merged into one cluster, a hierarchically constructed cluster representing the final processing result is stored in the cluster data storage unit 123. The decision unit 105 determines the order processing order by recursively expanding the aforementioned binary tree so that child clusters belonging to the same parent cluster are adjacent, thereby configuring the leaves (equivalent to order data) of the binary tree belonging to the same parent cluster in the nearest order.

[0061] The output control unit 106 controls the output of various information processed in the information processing device 100. For example, the output control unit 106 outputs information indicating the processing order of the determined orders (sequence information). The sequence information is referenced, for example, when assigning orders to more than one workstation 11.

[0062] When multiple workstations 11 are present, the output control unit 106 can also output a collection (data set) of order data allocated to each of the multiple workstations 11. For example, the output control unit 106 outputs multiple data sets as data sets allocated to each of the multiple workstations 11. The output control unit 106 divides a list of multiple orders arranged in the processing order determined by the decision unit 105 and outputs them separately to average the estimated workload of each station. For example, it can be divided to make the number of orders equal, or to make the total number of items in the orders equal, etc.

[0063] The output control unit 106 can also output instructions on the order in which the shelves 30 are called to the station and instructions on the picking operation, based on the determined order processing sequence and the information in the index mentioned above. The output control unit 106 can also output operation instructions on the display 14 regarding the moved shelves 30.

[0064] The aforementioned units (acceptance unit 101, priority calculation unit 102, generation unit 103, distance calculation unit 104, decision unit 105, and output control unit 106) can be implemented by one or more processors. For example, each unit can be implemented by executing a program, i.e., software, using a processor such as a CPU (Central Processing Unit). Each unit can also be implemented by a dedicated IC (Integrated Circuit) processor, i.e., hardware. Each unit can also be implemented using both software and hardware. When using multiple processors, each processor can implement one or more of the aforementioned units.

[0065] Order data storage unit 121 stores order data received by processing unit 101. Shelf data storage unit 122 stores shelf data received by processing unit 101. Cluster data storage unit 123 stores data generated in hierarchical clustering of decision unit 105 (as a historical binary tree, hierarchically constructed cluster, etc.).

[0066] Furthermore, each storage unit (order data storage unit 121, shelf data storage unit 122, cluster data storage unit 123) can be composed of any commonly used storage media such as flash memory, memory card, RAM (Random Access Memory), HDD (Hard Disk Drive), and optical disc.

[0067] Each storage unit can be implemented using physically different storage media, or it can be implemented as different storage areas of physically identical storage media. Furthermore, each storage unit can also be implemented using multiple physically different storage media.

[0068] Next, the method for calculating the priority will be explained in detail. First, an example of calculating the priority based on the method described above (M1) will be explained. Figure 3 This is a diagram showing the calculation examples with priority.

[0069] exist Figure 3 In the example, shelf R1 stores products A, B, C, D, and E, while shelf R2 stores products F, G, H, I, and J. Additionally, order O1 includes products A, F, G, H, and I, and order O2 includes products C, D, G, and K.

[0070] Priority calculation unit 102 calculates the priority of each shelf that is a candidate shelf (in this case, two shelves) for order data with order ID O1. For example, the shelves with shelf ID R1 containing products A, B, C, D, and E are identical to the shelves with order O1 containing products A, F, G, H, and I, except for product A. Therefore, priority calculation unit 102 calculates 1 as the coverage (priority). Similarly, the coverage (priority) of each shelf for each order is calculated as follows.

[0071] • For order O1, the coverage of the shelf with shelf ID R2 is: 4

[0072] • For order O2, the coverage of the shelf with shelf ID R1 is: 2

[0073] • For order O2, the coverage of the shelf with shelf ID R2 is: 1.

[0074] In addition, the priority calculation unit 102 can also calculate the coverage of shelves for items not included in the order.

[0075] Next, an example of calculating the priority based on the above (M2) method will be explained. Figure 4 This is a diagram showing the calculation examples with priority.

[0076] exist Figure 4 In this example, we use 10 orders with order IDs O1 to O10 and 6 shelf data with shelf IDs R1 to R6. Each order includes product identification information recorded next to the order ID. Each shelf stores products with identification information recorded on the shelf ID.

[0077] The priority calculation unit 102 first counts the items included in several orders based on the items contained in any given order. Figure 4 In the example, product A is contained in 5 orders (order IDs are order O1, order O5, order O7, order O9, and order O10). Similarly, the count value representing the number of orders containing each product is calculated as follows.

[0078] Product A: 5

[0079] ·Product B: 3

[0080] ·Product C: 1

[0081] ·Product D: 1

[0082] ·Product F: 5

[0083] ·Product G: 4

[0084] Product H: 1

[0085] ·Product I: 3

[0086] ·Product K: 2

[0087] Next, the priority calculation unit 102 calculates the sum of the counts of the stored goods for each shelf as the priority. For example, the priority of shelf R1 becomes 5+3+1+1=10. Similarly, the priority of each shelf is calculated as follows.

[0088] • Shelf R1: 10

[0089] • Shelf R2: 10

[0090] • Shelf R3: 5

[0091] • Shelf R4: 7

[0092] • Shelf R5: 13

[0093] • Shelf R6:7

[0094] Next, the method for generating the index will be explained in detail. For each order, the generation unit 103 prioritizes selecting the shelf with the highest priority among the candidates for shelves that can potentially be selected (i.e., shelves containing the goods included in the order), and generates an index including the selected shelf. The generation unit 103 repeatedly processes all the goods included in the order until the shelf to be picked is selected.

[0095] Figure 5 This is a diagram showing an example of the generated index. Figure 5 Indicates for Figure 4 Examples of indexes generated based on the priority of the method described above (M2).

[0096] The generation unit 103 repeatedly selects the highest-priority shelf from the candidate shelves that may be selected for each order, and associates the selected shelf with the order (selects) until all products in the order are covered. For example, regarding product A in order O1, the generation unit 103 selects shelf R5, which has the highest priority among shelves R1, R5, and R6, and associates it with order O1. The generation unit 103 generates an index to include the shelf IDs of the associated shelves.

[0097] In this example, shelf R5 also includes product G; therefore, for order O1, product G is also simultaneously selected. Regarding product F that has not yet been selected, generation unit 103 selects shelf R2, which has the highest priority among shelves R2 and R4, and associates it with order O1. For example... Figure 5 As shown, the generation unit 103 generates an index including R5 and R2 as an index including the shelf ID of the associated shelf.

[0098] Production unit 103 repeats this process for all orders. Figure 5 In the image, an example of the index generated for an order with that order ID is shown next to each order ID.

[0099] The generation unit 103 can also generate indexes that assign weights to each shelf. The weight, for example, is the number of identification information entries for items selected from that shelf among the items included in an order. For example, in... Figure 5 In order O1, product A and product G are drawn from shelf R5, therefore the weight becomes 2; product F is drawn from shelf R2, therefore the weight becomes 1. The method for calculating the weights is not limited to this; for example, the priority can also be used directly as the weight.

[0100] Furthermore, the generated index is information used to determine the hierarchical clustering of section 105. Therefore, the shelves contained in the index do not need to be used as shelves that become actual selected objects.

[0101] For example, in the secondary processing after the sequence determination processing of the information processing device 100, such as Figure 4 In cases where there are multiple potential shelf candidates for a product (where the same type of product is stored separately), the shelf that becomes the target for each order can also be changed.

[0102] Next, an example of hierarchical clustering using indexes will be explained. Figure 6 This is a diagram representing an example of a hierarchical cluster generated by hierarchical clustering.

[0103] First, the distance calculation unit 104 calculates the distance between the generated multiple indexes. For example, the distance calculation unit 104 calculates the sum of the number of inconsistent shelves (shelf IDs) between two indexes as the distance. The distance is not limited to this; for example, it can also be calculated using methods such as those described below, and the calculation method is not limited.

[0104] • The distance is obtained by multiplying the total number of consistent shelves by a negative coefficient.

[0105] • The distance is obtained by multiplying the total number of inconsistent shelves and the total number of consistent shelves by a coefficient and summing the results.

[0106] Alternatively, if weights are assigned to each shelf in the index, the distance can be calculated by summing the weights (or weights obtained by multiplying by a negative coefficient) for consistent shelves. For example, in Figure 5 When the weight of shelf R5 in the index of order O1 is 2 and the weight of shelf R2 is 1, the distance calculation unit 104 calculates the distance as -2 when only shelf R5 is consistent, as -1 when only shelf R2 is consistent, and as -3 when both shelf R5 and shelf R2 are consistent.

[0107] Next, the decision unit 105 repeatedly performs the process of merging orders with small distance indexes into one cluster, clusters into one cluster, or orders and clusters into one cluster, generating hierarchically structured clusters. Figure 5 For example, the indexes of orders O2 and O5 are consistent in that they only include R5. Using a distance that makes the value of the index, which only includes the consistent shelf ID, smaller, the decision unit 105 merges the orders O2 and O5 into a single cluster.

[0108] Figure 6 Cluster C1 represents the cluster that has been merged in this way. Furthermore, C1 through C9 represent information identifying each cluster. The decision unit 105 stores a binary tree in the cluster data storage unit 123, with the two indices before merging as child nodes and the merged cluster as the parent node. Similarly, the decision unit 105 repeats this process until all orders are merged into one cluster.

[0109] Figure 6 This represents an example of a cluster that is collectively grouped into one. For example... Figure 6 As shown, this serves as an index for the merged cluster, for example, using the union of the shelf IDs extracted from each order belonging to the cluster.

[0110] Next, the decision unit 105 expands the binary tree recursively so that child clusters belonging to the same parent cluster are adjacent, thereby determining the order of order processing so that two orders corresponding to leaf nodes belonging to the same cluster are in the nearest order.

[0111] Figure 7 This is a diagram showing an example of orders that have been sorted according to the determined processing order. Figure 7 Based on Figure 6 An example of a cluster that has sorted orders. For example... Figure 6As shown, the cluster comprises 10 leaf nodes corresponding to 10 orders. The decision unit 105 determines the order processing order, for example, by recursively exploring the parent-child relationships of the binary tree from the last discovered cluster C9 (expanding the binary tree). Figure 6 In the example, decision unit 105 explores the parent-child relationship as follows, arranging them according to the order of the leaves (orders) reached during the exploration, thereby... Figure 7 The orders are sorted in the following order.

[0112] C9→C8→C3→C1→Order O2→C1→Order O5→C1→C3→C2→Order O9→C2→Order O6→C2→C3→C8→C7→C5→C4→Order O1→(omitted)→C8→C9→Order O8

[0113] As described above, the output control unit 106 can also output data sets of order data that are distributed to multiple workstations 11. For example, in the case of N workstations 11 (where N is an exponent of 2 or more), the output control unit 106 divides the order data into N data sets to average the estimated workload of each workstation 11. The output control unit 106 can use a division method as follows.

[0114] • Divide the orders so that the number of orders is equal.

[0115] • Divide the order so that the total number of items contained in the order is equal.

[0116] • Perform partitioning so that the total number of shelves used in an order (the size of the union of shelf IDs contained in the index) is equal.

[0117] Next, the sequence determination process of the information processing apparatus 100 according to this embodiment will be described. Figure 8 This is a flowchart illustrating an example of the sequence determination process in this embodiment.

[0118] The receiving department 101 accepts the input of multiple order data and multiple shelf data (step S101). In subsequent steps S102 to S106, the process of generating an index by associating shelves with each order is repeatedly executed for all orders.

[0119] First, the priority calculation unit 102 obtains the unindexed (unprocessed) order data from the received order data (step S102). The priority calculation unit 102 calculates the priority of the shelf, for example, using (M1) or (M2) as described above (step S103). Furthermore, in the case of (M2), the priority calculation unit 102 first determines the priority of each shelf in step S101.

[0120] For products that were not selected for shelf selection (unprocessed), generation unit 103 associates them with the shelf that has the highest priority and generates an index with the shelf ID of the associated shelf (step S104). Generation unit 103 determines whether shelves have been associated with all products included in the order data (step S105). If shelves have not been associated with all products (step S105: No), it returns to step S104 and repeats the processing for the next unprocessed products.

[0121] If all products are associated with a shelf (step S105: Yes), the generation unit 103 determines whether all order data has been processed (step S106). If not all order data has been processed (step S106: No), the process returns to step S102 and repeats the processing of the remaining unprocessed order data.

[0122] After all order data has been processed (step S106: Yes), hierarchical clustering is performed in subsequent steps S107 to S109.

[0123] The decision unit 105 merges the orders or clusters with the smallest distance into one cluster (step S107). In addition, the distance calculation unit 104 calculates the distance between the indices. The decision unit 105 generates a binary tree with the orders or clusters before merging as child nodes and the merged clusters as parent nodes, and stores it in the cluster data storage unit 123 (step S108).

[0124] The decision unit 105 determines whether all orders have been merged into a single cluster (step S109). If all orders have not been merged into a single cluster (step S109: No), the process returns to step S107 and repeats the process.

[0125] If all orders are merged into a single cluster (step S109: Yes), the decision unit 105 determines the order of the order data based on the generated cluster (step S110).

[0126] The output control unit 106 outputs a work instruction based on the order data with a determined order (step S111). In the case of multiple workstations, the output control unit 106 divides the list of multiple order data with a determined order into multiple data groups and outputs multiple data groups as information representing the orders processed by each of the multiple workstations 11.

[0127] Thus, in this embodiment, multiple order data and multiple shelf data obtained in advance can be used to determine the processing order of multiple order data, so as to enable picking operations to be performed more efficiently.

[0128] Next, use Figure 9The hardware structure of the information processing device involved in the implementation method will be described. Figure 9 This is an explanatory diagram showing an example of the hardware structure of an information processing device involved in the implementation method.

[0129] The information processing apparatus according to the embodiment includes a control device such as a CPU 51, a storage device such as a ROM (Read Only Memory) 52 and a RAM 53, a communication I / F (interface) 54 for communication with a network, and a bus 61 connecting each part.

[0130] The program executed in the information processing device according to the embodiment is pre-loaded into ROM52 or the like and provided.

[0131] The program executed in the information processing apparatus according to the embodiments may also be configured to be provided as a finished computer program, which is recorded in a computer-readable recording medium such as CD-ROM (Compact Disk Read Only Memory), floppy disk (FD), CD-R (Compact Disk Recordable), DVD (Digital Versatile Disk), in an installable or executable form.

[0132] Furthermore, the program executing in the information processing device according to the embodiment can also be configured to be stored on a computer connected to a network such as the Internet, and provided by downloading it via the network. Alternatively, the program executing in the information processing device according to the embodiment can also be provided or published via a network such as the Internet.

[0133] The program executed in the information processing apparatus according to the embodiment enables the computer to function as a component of the aforementioned information processing apparatus. The computer can read the program from a computer-readable storage medium and load it into the main storage device for execution by the CPU 51.

[0134] Several embodiments of the present invention have been described above, but these embodiments are merely illustrative and not intended to limit the scope of the invention. These new embodiments can be implemented in a wide variety of other ways, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included within the scope and spirit of the invention, and are included within the scope of the invention as described in the claims and its equivalents.

[0135] Furthermore, the above-described implementation methods can be summarized as the following technical solutions.

[0136] Technical Solution 1

[0137] An information processing device comprising:

[0138] The receiving department receives multiple shelf data and multiple order data. The multiple shelf data includes first identification information of one or more items stored on multiple shelves, and the multiple order data includes second identification information of one or more items picked from at least a portion of the multiple shelves.

[0139] The decision-making department determines the processing order of multiple order data based on the shelf data, so as to increase the proportion of goods picked from multiple order data from one shelf.

[0140] The multiple shelves can be moved to a work station, the work station being equipped with a storage container corresponding to at least a portion of the multiple order data.

[0141] Technical Solution 2

[0142] According to the information processing device described in technical solution 1,

[0143] It also includes a generation unit that generates an index representing one or more shelves containing picked items that match the first identification information and is based on a priority calculated for each of the plurality of shelves and for each of the plurality of order data.

[0144] The decision-making unit uses the index to determine the processing order.

[0145] Technical Solution 3

[0146] According to the information processing device described in technical solution 2

[0147] It also includes a distance calculation unit, which calculates the distance between the multiple indexes generated for the multiple order data respectively.

[0148] The decision unit determines the processing order in such a way that the smaller the distance, the closer the order.

[0149] Technical Solution 4

[0150] The information processing device according to technical solution 3

[0151] The decision unit performs hierarchical clustering, which repeatedly groups the order data corresponding to the index with the small distance into clusters. This is done recursively to make the child clusters common to the parent clusters adjacent, thereby determining the processing order.

[0152] Technical Solution 5

[0153] According to the information processing device described in technical solution 2

[0154] The generation unit prioritizes selecting shelves with higher priority than other shelves as shelves for picking goods, and generates an index representing the selected shelf.

[0155] Technical Solution 6

[0156] According to the information processing device described in technical solution 2

[0157] It also includes a priority calculation unit, which calculates the priority for each of the plurality of order data, such that the more times the number of the first identification information that is consistent with the second identification information contained in the order data increases, the higher the priority becomes.

[0158] Technical Solution 7

[0159] According to the information processing device described in technical solution 2

[0160] It also includes a priority calculation unit, which calculates the priority by increasing the number of order data that include the second identification information that is consistent with the first identification information.

[0161] Technical Solution 8

[0162] The information processing apparatus according to any one of technical solutions 1 to 7,

[0163] It also includes an output control unit that divides the multiple order data into multiple data groups such that each of the multiple data groups includes the order data that is closest in the determined processing order, and outputs the multiple data groups as data groups to be respectively allocated to multiple workstations for picking.

[0164] Technical Solution 9

[0165] The information processing apparatus according to any one of technical solutions 1 to 8,

[0166] The decision-making unit determines the processing order based on the shelf data, so as to increase the proportion of items picked from multiple order data at the same time from a shelf.

[0167] Technical Solution 10

[0168] An information processing method, executed in an information processing apparatus, includes:

[0169] The acceptance step includes accepting multiple shelf data and multiple order data. The multiple shelf data includes first identification information of one or more items stored on multiple shelves, and the multiple order data includes second identification information of one or more items picked from at least a portion of the multiple shelves.

[0170] The decision-making process involves determining the processing order of multiple order data based on the shelf data, in order to increase the proportion of goods picked from multiple order data from a single shelf.

[0171] The multiple shelves can be moved to a work station, the work station being equipped with a storage container corresponding to at least a portion of the multiple order data.

[0172] Technical Solution 11

[0173] A storage medium containing programs that enable a computer to perform acceptance and decision steps.

[0174] In the acceptance step, multiple shelf data and multiple order data are accepted. The multiple shelf data includes first identification information of one or more products stored on multiple shelves, and the multiple order data includes second identification information of one or more products picked from at least a portion of the multiple shelves.

[0175] In the decision-making step, the processing order of multiple order data is determined based on the shelf data, so as to increase the proportion of goods picked from multiple order data from one shelf.

[0176] The multiple shelves can be moved to a work station, the work station being equipped with a storage container corresponding to at least a portion of the multiple order data.

[0177] Technical Solution 12

[0178] An information processing system comprising a conveying device and an information processing device.

[0179] The conveying device moves goods between multiple shelves.

[0180] The information processing device includes:

[0181] The receiving department receives multiple shelf data and multiple order data. The multiple shelf data includes first identification information of one or more items stored on multiple shelves, and the multiple order data includes second identification information of one or more items picked from at least a portion of the multiple shelves.

[0182] The decision-making department determines the processing order of multiple order data based on the shelf data, so as to increase the proportion of goods picked from multiple order data from one shelf.

[0183] The multiple shelves can be moved to a work station, the work station being equipped with a storage container corresponding to at least a portion of the multiple order data.

Claims

1. An information processing device, comprising: The receiving department receives multiple shelf data and multiple order data. The multiple shelf data includes first identification information of one or more items stored on multiple shelves, and the multiple order data includes second identification information of one or more items picked from at least a portion of the multiple shelves. The decision-making department determines the processing order of multiple order data based on the shelf data, so as to increase the proportion of goods picked from multiple order data from one shelf. Multiple of the aforementioned shelves can be moved to a workstation, the workstation being equipped with storage containers corresponding to at least a portion of the order data from the multiple sets of order data. The information processing device further includes a generation unit that generates an index representing one or more shelves containing picked items that match the first identification information and is based on a priority calculated for each of the plurality of shelves, for each of the plurality of order data. The decision-making unit uses the index to determine the processing order.

2. The information processing device according to claim 1, It also includes a distance calculation unit, which calculates the distance between the multiple indexes generated for the multiple order data respectively. The decision unit determines the processing order in such a way that the smaller the distance, the closer the order.

3. The information processing device according to claim 2, The decision unit performs hierarchical clustering, which repeatedly groups the order data corresponding to the index with the small distance into clusters. This is done recursively to make the child clusters common to the parent clusters adjacent, thereby determining the processing order.

4. The information processing device according to claim 1, The generation unit prioritizes selecting shelves with higher priority than other shelves as shelves for picking goods, and generates an index representing the selected shelf.

5. The information processing device according to claim 1, It also includes a priority calculation unit, which calculates the priority for each of the plurality of order data, such that the more times the number of the first identification information that is consistent with the second identification information contained in the order data increases, the higher the priority becomes.

6. The information processing apparatus according to claim 1, It also includes a priority calculation unit, which calculates the priority by increasing the number of order data that include the second identification information that is consistent with the first identification information.

7. The information processing apparatus according to any one of claims 1 to 6, It also includes an output control unit that divides the multiple order data into multiple data groups such that each of the multiple data groups includes the order data that is closest in the determined processing order, and outputs the multiple data groups as data groups to be respectively allocated to multiple workstations for picking.

8. The information processing apparatus according to any one of claims 1 to 6, The decision-making unit determines the processing order based on the shelf data, so as to increase the proportion of items picked from multiple order data at the same time from a shelf.

9. An information processing method, executed in an information processing apparatus, comprising: The acceptance step involves accepting multiple shelf data and multiple order data. The multiple shelf data includes first identification information of one or more products stored on multiple shelves, and the multiple order data includes second identification information of one or more products picked from at least a portion of the multiple shelves. and The decision-making process involves determining the processing order of multiple order data based on the shelf data, in order to increase the proportion of goods picked from multiple order data from a single shelf. Multiple of the aforementioned shelves can be moved to a workstation, the workstation being equipped with storage containers corresponding to at least a portion of the order data from the multiple sets of order data. The information processing method further includes a generation step, which generates an index representing one or more shelves containing picked items that match the first identification information and are consistent with the second identification information, based on a priority calculated for each of the plurality of shelves and for each of the plurality of order data. In the decision-making step, the index is used to determine the processing order.

10. A storage medium storing a program that causes a computer to perform an acceptance step, a decision step, and a generation step. In the acceptance step, multiple shelf data and multiple order data are accepted. The multiple shelf data includes first identification information of one or more products stored on multiple shelves, and the multiple order data includes second identification information of one or more products picked from at least a portion of the multiple shelves. In the decision-making step, the processing order of multiple order data is determined based on the shelf data, so as to increase the proportion of goods picked from multiple order data from one shelf. In the generation step, based on the priority calculated for each of the plurality of shelves, an index is generated for each of the plurality of order data to represent one or more shelves containing picked items that match the first identification information and are consistent with the second identification information. Multiple of the aforementioned shelves can be moved to a workstation, the workstation being equipped with storage containers corresponding to at least a portion of the order data from the multiple sets of order data. In the decision-making step, the index is used to determine the processing order.

11. An information processing system comprising a conveying device and an information processing device. The conveying device moves goods between multiple shelves. The information processing device includes: The receiving department receives multiple shelf data and multiple order data. The multiple shelf data includes first identification information of one or more items stored on multiple shelves, and the multiple order data includes second identification information of one or more items picked from at least a portion of the multiple shelves. The decision-making department determines the processing order of multiple order data based on the shelf data, so as to increase the proportion of goods picked from multiple order data from one shelf. A plurality of the shelves can be moved to a work station, and the work station is provided with a storage container corresponding to at least part of the order data, The information processing device further includes a generation unit configured to generate, for each of the order data, an index indicating one or more shelves in which the goods corresponding to the first identification information are stored, based on the priority calculated for each of the plurality of shelves, The determination unit determines the processing order using the index.

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