Information processing system, warehouse management method, and warehouse control device

Abstract

The present application provides an information processing system including: a transport device controlled by a warehouse control device to be able to transport a storage unit for storing an article; and a terminal connected to the warehouse control device to perform reception and transmission of work information of a work station in which one or more kinds of work on storage and withdrawal of the article in the storage unit are performed, the warehouse control device having: a storage unit that acquires information on actual results of the storage and withdrawal in the work station from the terminal and stores the information as log information; and a control unit that, based on the log information, generates a plurality of actual result data on work time in each of a plurality of periods of different lengths set in advance for each kind of the one or more kinds of work, and estimates a predicted work time for each of the one or more kinds of work based on the plurality of actual result data.

Description

[0001] Introduced through reference

[0002] This application claims priority to Japanese Patent Application No. 2021-103716, filed on June 23, 2021 (Reiwa 3), which is incorporated herein by reference to its contents. Technical Field

[0003] This invention relates to an information processing system for predicting operations in a logistics center, a warehouse management method, and a warehouse control device. Background Technology

[0004] Examples of logistics centers or warehouses include storing delivered items, retrieving matching items through sorting operations when orders are received, classifying and packaging the items through classification operations, and then sending them to customers.

[0005] As an example of a technology for predicting the operation time of a logistics center, there is Patent Document 1.

[0006] Existing technical documents

[0007] Patent documents

[0008] Patent Document 1: Japanese Patent Application Publication No. 2001-322707 Summary of the Invention

[0009] The technical problem that the invention aims to solve

[0010] Delays in logistics center operations can lead to delays in subsequent deliveries, increasing costs. Therefore, to improve operational decision-making, such as by increasing personnel at locations prone to delays, it is necessary to accurately predict the completion times of various operations within the logistics center and understand these delays. Here, the inventors of this application have discovered that there are several main factors affecting the duration of various operations in a logistics center. For example, in addition to variations in the work efficiency of operators on a given day, differences in seasons, trends, the type of goods being shipped, and the location of shipments can also cause variations in the duration of various operations.

[0011] For example, it was found that recent operational results strongly reflect differences between operators and temporary orders, in contrast, operational results over a longer period reflect various changes in operational time caused by differences in the types and locations of goods shipped out, depending on the season and trends.

[0012] Therefore, the present invention was made in view of the above-mentioned problems, and its purpose is to predict the operation time by taking into account the main causes of various changes that affect the operation time of the logistics center.

[0013] Technical solutions for solving technical problems

[0014] This invention provides an information processing system comprising: a warehouse control device having a processor and a memory; a transport device capable of transporting a storage unit for storing items according to transport instructions from the warehouse control device; and a terminal connected to the warehouse control device for receiving and transmitting work information of a work station, wherein one or more operations concerning the storage and retrieval of items in the storage unit are performed at the work station; the warehouse control device comprising: a storage unit that acquires information about actual results of the operations from the work information concerning at least one of the storage and retrieval operations at the work station from the terminal and stores it as log information; and a control unit that, based on the log information, generates information about the operation time, i.e., multiple actual result data, for each type of the one or more operations in a pre-set multiple period of different lengths, and calculates the predicted operation time for each of the one or more operations based on the multiple actual result data.

[0015] The effects of the invention

[0016] Therefore, this invention predicts operation time based on operation times over multiple periods of varying lengths, enabling highly accurate predictions of operation time according to various primary causes of variation. Furthermore, by appropriately managing delays in operations within the logistics center, adjustments can be made to personnel, task selection, and other operational decisions to mitigate delays, thereby reducing cost increases caused by operation delays.

[0017] A detailed description of at least one embodiment of the subject matter described in this specification is set forth in the accompanying drawings and the following description. Other features, methods, and effects of the described subject matter will become clear from the following description, the accompanying drawings, and the claims. Attached Figure Description

[0018] Figure 1 The diagram shown is a block diagram illustrating an example of the structure of an information processing system, representing an embodiment of the present invention.

[0019] Figure 2 The embodiment of the present invention is shown in a perspective view of a logistics center.

[0020] Figure 3 The following is a flowchart illustrating an embodiment of the present invention, showing an example of a process performed in an information processing system.

[0021] Figure 4A The first half of the diagram illustrates an embodiment of the present invention and represents an example of a station log.

[0022] Figure 4BThe latter half of the diagram illustrates an embodiment of the present invention and represents an example of a station log.

[0023] Figure 5 The diagram illustrates an embodiment of the present invention and is an example of actual result data from a station.

[0024] Figure 6 The diagram illustrating an embodiment of the present invention is an example of actual result data from an operator.

[0025] Figure 7A The diagram illustrating an embodiment of the present invention is an example of actual result data at different times.

[0026] Figure 7B The diagram illustrating an embodiment of the present invention is a summary of the calculation of actual result data at different times.

[0027] Figure 8 The diagram illustrates an embodiment of the present invention and is one example of the characteristics of a workday.

[0028] Figure 9 The diagram illustrates an embodiment of the present invention and is an example of a weighting coefficient.

[0029] Figure 10 The diagram illustrates an embodiment of the present invention and is an example of job scheduling information.

[0030] Figure 11 The diagram illustrates an embodiment of the present invention and is an example of predicted data.

[0031] Figure 12 The diagram illustrates an embodiment of the present invention and is an example of device information.

[0032] Figure 13 The diagram illustrates an embodiment of the present invention and is an example of operator duty information.

[0033] Figure 14 The diagram illustrates an embodiment of the present invention and is an example of a predicted image.

[0034] Figure 15 The diagram illustrating an embodiment of the present invention is an example of order information.

[0035] Figure 16 The diagram illustrates an embodiment of the present invention and is an example of inventory information.

[0036] Figure 17 The diagram illustrates an embodiment of the present invention and is an example of shelf information.

[0037] Figure 18 The diagram illustrates an embodiment of the present invention and is another example of a predicted image. Detailed Implementation

[0038] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.

[0039] Figure 1 This is a block diagram illustrating an example of the structure of an information processing system in an embodiment of the present invention. The information processing system in this embodiment includes a warehouse control device 100, a network 90, multiple transport devices 1 connected to the warehouse control device 100 via the network 90, and multiple station terminals 7.

[0040] In this embodiment, the following example is shown: by the operator operating the station terminal 7 set in the warehouse of the logistics center, the warehouse control device 100 causes the conveying device 1 to transport the shelf 8 (storage section) to the sorting station (or work station), where the operator performs the sorting operation.

[0041] The warehouse control device 100 is a computer including a processing unit 110, a memory 120, an input device 130, an output device 140, a storage device 150, and a communication interface 170. However, the warehouse control device 100 is not limited to... Figure 1 The structure is shown. The warehouse control device 100 can be a single computer or composed of multiple computers. Furthermore, the various devices included in the warehouse control device 100 can be configured within a single computer or distributed among multiple devices. The various programs and information contained in the storage device 150 can be stored in a single storage device or distributed across multiple storage devices.

[0042] Storage device 150 has a non-volatile storage medium that stores the program executed by arithmetic unit 110 and the data used by the program. As an example of a program, path generation program 161, data input / output program 162, data analysis program 163 (control unit), and transport device control program 164 are stored in storage device 150. Arithmetic unit 110 downloads the required program to memory 120 for execution.

[0043] In addition, as examples of data stored in storage device 150, order information 200, inventory information 220, shelf information 230, operator duty information 240, job reservation information 250, equipment information 260, path data 270, work day characteristics 280, forecast data 290, station log 310, station actual result data 320, operator actual result data 330, actual result data for different periods 340, and weighting coefficient 350 are stored.

[0044] The path generation program 161 calculates the movement path of the transport device 1 based on pre-set map information (illustration omitted). For example, the path generation program 161 calculates the movement path of the transport device 1 based on the location of the items (or goods) to be sorted and the location of the sorting station at the destination.

[0045] Based on the path calculated by the path generation program 161 and device information 260, the transport device control program 164 instructs the shelf 8 to be transported and the sorting station to be transported to the destination of the transport device 1.

[0046] The data input / output program 162 handles the acceptance of order information, the acceptance of input from the station terminal 7 operated by the operator, and the acceptance of sensor data from the transport device 1, and accumulates it in the station log 310. In addition, when the data input / output program 162 receives a departure instruction from the transport device 1 from the station terminal 7, it sends the instruction generated by the transport device control program 164 to the transport device 1.

[0047] Data analysis program 163, based on station log 310, generates station actual result data 320 with operation time recorded for each sorting station and operator actual result data 330 with operation time recorded for each operator. It then aggregates the actual result data 340 for different periods as described later, calculates operation prediction data for each sorting station, and saves it in prediction data 290.

[0048] The data analysis program 163 aggregates the contents of the prediction data 290 to generate a prediction screen 51 and displays it on the output device 140, making the progress of the sorting station operations in the warehouse of the logistics center visible.

[0049] Order information 200 contains information about orders requesting the shipment of items, and saves information about the items to be sorted. Inventory information 220 contains information about the inventory of items, including information about the shelf 8 where the items are configured, the configuration position of the items within shelf 8, the quantity, weight, etc. Shelf information 230 saves information such as the position and weight of the shelves.

[0050] Operator duty information 240 includes the operator's duty schedule, information about the operator's experience and status. The information about the operator's experience and status may include, in addition to the operator's length of service, information such as height, whether there are any injuries, and information about the continuous working hours for the day. Job scheduling information 250 stores information such as the items to be processed, the scheduled completion time of the job, and the operator performing the job for each sorting station. The job scheduling information 250 is pre-generated data, which can be input from the input device 130 of the warehouse control device 100 or received from an external computer.

[0051] Device information 260 stores the identification information, location, and operating status of the transport device 1. Route data 270 stores information about the routes within the warehouse for each transport device 1. Workday characteristics 280 assigns attributes to workdays based on various conditions. For example, in addition to information such as the overall volume of inbound and outbound operations, attributes can also be assigned based on conditions such as season, presence or absence of events, weather, disasters, and obstacles. Here, as an example, it includes information about events such as promotions held in a commercial center treated as a logistics center, seasonal information, and information about disasters and obstacles.

[0052] Station log 310 stores logs of operations performed at the sorting station and the work done by conveying device 1. Station actual result data 320 extracts data from each sorting station from station log 310, saving the start and end times of the operations and the content of the operations. Operator actual result data 330 extracts data from each operator from station log 310, saving the start and end times of the operations, the content of the operations, and the workload of the operations.

[0053] The actual result data 340 for different periods, for multiple pre-defined periods, saves statistical information on the operation time extracted from the actual result data 320 of each station according to each type of operation. In this embodiment, an example of using average time as statistical information is shown. The weighting coefficient 350 is stored as a coefficient used when calculating the predicted completion time of various operations for each sorting station. The weighting coefficient 350 is pre-defined information. The weighting coefficient 350 can be a variable value set by the user, or it can be automatically calculated by AI based on past actual result data when setting the weighting coefficient, and the calculated value is set as the weighting coefficient.

[0054] Predictive data 290 stores the time of business completion calculated for each sorting station by the data analysis program 163 using actual station result data 320, operator actual result data 330, and weighting coefficient 350.

[0055] Input device 130 consists of a keyboard, mouse, or touch panel, etc. Output device 140 consists of a display, etc. Communication interface 170 communicates with transport device 1 and other computers via network 90 wirelessly or otherwise.

[0056] The conveying device 1 is an autonomous moving body capable of automatically conveying shelves 8 carrying items according to instructions from the warehouse control device 100. The conveying device 1 is an automated conveying device having a control device 2, a storage device 4, a drive device 3, sensors 5, and a communication interface 6. Sensors 5 include, for example, vibration sensors (accelerometers) and image sensors.

[0057] The control device 2 includes a computing unit 21 and a memory 22. The self-position calculation program 23, the driving control program 24, the measurement program 25, and the communication program 26 are downloaded into the memory 22 and executed by the computing unit 21. The computing unit 21 consists of a microcomputer and a processor.

[0058] The self-positioning calculation program 23 calculates the position of the transport device 1 based on image data (images or moving image data) acquired from an image sensor. In this embodiment, an example is shown where a marker indicating the position is displayed in advance on the warehouse floor. The self-positioning calculation program 23 calculates the position of the transport device 1 based on the marker read by the image sensor. The marker placed on the floor is information that can be read by the sensor 5 of the transport device 1, such as a QR code (registered trademark). Alternatively, the position calculation of the transport device 1 can be configured such that image data acquired from the image sensor is sent to the warehouse control device 100, where the warehouse control device 100 performs the position calculation. The marker can also be called a symbol or reference marker.

[0059] For example, the warehouse floor is managed by multiple zones, each zone marked with its location information. A transport device 1 travels across the floor, reading the markings on the floor of each zone to obtain information about its location. The markings simply contain information used to determine the zone's location; for example, it could be the zone's location information itself, or information corresponding to that location information (such as the zone's identification information).

[0060] The driving control program 24 controls the drive unit 3 based on the current position of the transport device 1 and the path data 270 received from the warehouse control unit 100. The warehouse control unit 100 sends the path data 270 generated by the path generation program 161 for each transport device 1 to the transport device 1, and the transport device 1 saves the path data 41 in the storage device 4.

[0061] Measurement program 25 acquires sensor data from sensor 5, driving speed and acceleration from driving control program 24, and the position of transport device 1 calculated by its own position estimation program 23, and sends it to warehouse control device 100. The sensor data includes vibration data from vibration sensor and image data of the floor surface from image sensor. Furthermore, the timing of measurement program 25 sending sensor data to warehouse control device 100 can be performed at predetermined times and cycles (e.g., every 24 hours).

[0062] Storage device 4 is composed of a non-volatile storage medium and stores each program and the data used by each program. Examples of data include path data 41, map information 42, measurement data 43, device information 44, actual driving result data 45, and floor information 46.

[0063] Path data 41 stores the path data received from the warehouse control device 100. Map information 42 stores the map information received from the warehouse control device 100. Measurement data 43 stores the sensor data acquired by the aforementioned sensor 5, and the data acquired or calculated by each program.

[0064] Device information 44 stores the identifier (device ID) of transport device 1, the device's status, information about whether the shelf is loaded, the device's location, remaining battery level, cumulative travel distance, and cumulative acceleration count. For example, device information 44 can be the same information about transport device 1 found in device information 260. Actual travel result data 45 stores the travel path of transport device 1, the state of the floor surface (vibration) in each area, and the travel pattern, among other historical data.

[0065] The drive unit 3 includes a trolley 31, drive wheels 33, a platform 32, auxiliary wheels (casters) 34, a motor 38 that serves as the power source for driving the drive wheels 33 and the platform 32, and a battery (not shown) that supplies power to the motor 38. The motors 38 that drive the drive wheels 33 and the platform 32 can each be composed of independent motors.

[0066] The drive unit 3 lifts the shelf 8 by raising the platform 32 after entering under the shelf 8. The drive unit 3 travels to the indicated position with the shelf 8 raised, and lowers the platform 32 to place the shelf 8 on the floor.

[0067] The arithmetic unit 21 functions as a functional unit that provides specified functions by executing processing according to the programs of each functional unit. For example, the arithmetic unit 21 functions as a driving control unit by executing processing according to the driving control program 24. The same applies to other programs. Furthermore, the arithmetic unit 21 also functions as a functional unit that provides the functions of multiple processes executed by each program.

[0068] Station terminal 7 is set up for each sorting station where operations are performed by operators. Station terminal 7 displays the operation reservation information 250 sent from warehouse control device 100 to prompt the operator with the operation content. In addition, it accepts input from the operator and sends it to warehouse control device 100.

[0069] The station terminal 7 includes a communication interface 71, an input device 72, an output device 73, a control device 74, and a storage device 75. The communication interface 71 communicates with the warehouse control device 100 via a network 90. ​​The input device 72 consists of a touch panel, keyboard, etc. The output device 73 consists of a display, speaker, etc. The control device 74 consists of a microcomputer, etc., and executes prescribed programs. The storage device 75 stores programs and data.

[0070] The station terminal 7 receives the sorting operation reservations made at the sorting station from the warehouse control device 100 and stores them in the storage device 75 as sorting operation information 76. The station terminal 7 outputs instructions corresponding to the operator's work status from the sorting operation information 76 to the output device 73.

[0071] The operator operates the station terminal 7 at the start of the operation and after the specified operation is completed to obtain operation instructions. The input device 72 of the station terminal 7 includes a sorting start button, a sorting complete button, a classification start button, a classification complete button, a start button, a stop button, and a resume button.

[0072] For example, after pressing the sorting start button, the operator retrieves the designated item from shelf 8 and transports it to the designated location. Once the picking of the designated item is complete, the operator presses the sorting complete button. Next, the operator presses the sorting start button to sort and pack the sorted items. When the specified sorting and packaging are complete, the operator presses the sorting complete button. When proceeding to the next task, the operator presses the dispatch button, causing the warehouse control device 100 to move the transport device 1, moving the shelf 8 to the sorting station.

[0073] When the aforementioned buttons are pressed, the control device 74 sends the content of the operation received via the input device 72 to the warehouse control device 100. Upon receiving the operation content from the slave terminal 7, the warehouse control device 100 stores the received content in the station log 310 (described later).

[0074] <The Structure of a Logistics Center>

[0075] Figure 2 This is a perspective view illustrating an example of the layout of a warehouse in a logistics center. The logistics center has storage space 12. Within storage space 12, multiple shelves 8 are arranged in a grid pattern along the longitudinal and transverse directions. The shelves 8 form "islands" consisting of 2×6 or 1×6 shelves 8.

[0076] Multiple conveying devices 1 are arranged within the storage space 12. The conveying devices 1 enter the lower part of the shelf 8, lift the shelf 8, and move it. Multiple chargers 15 for charging the conveying devices 1 are arranged at designated locations around the perimeter of the storage space 12.

[0077] Multiple sorting stations 16-1 to 16-4 are configured at designated locations on the outer edge of the storage space 12. At sorting stations 16-1 to 16-3, operators 17-1 to 17-3 perform inbound and outbound operations of goods, while at sorting station 16-4, a work robot 18-1 performs inbound and outbound operations of goods.

[0078] In the following description, without specifically identifying the sorting station, reference numeral "16" with the part after "-" omitted is used. The reference numerals for other constituent elements are the same.

[0079] Safety light curtains 81 and 81 are installed at the sorting station 16 connected to the storage space 12 to detect operator intrusion. The area between the safety light curtains 81 and 81 forms a doorway 80 for sorting operations with shelves 8.

[0080] When the shelf 8 is positioned at the doorway 80 by the conveying device 1, the safety light curtains 81 and 81 close, allowing the operator 17 to perform sorting operations. On the other hand, when the sorting operation is completed and the conveying device 1 moves the shelf 8 from the doorway 80, the safety light curtains 81 and 81 open, and an alarm is triggered if the operator 17 or others intrude through the doorway 80.

[0081] In the sorting station 16 where operator 17 performs operations, a station terminal 7 is installed near the entrance 80. In addition, sorting and packaging work spaces 19-1 to 19-4 are set up in designated locations around the perimeter of the sorting station 16.

[0082] The size of the workspace 19, the size of the storage area for sorting and packaging boxes, etc., sometimes varies for each sorting station 16, and these differences are the main reason for the impact on the workability of the operators 17.

[0083] Furthermore, the different locations of sorting stations 16-1 to 16-3 within the warehouse are also a major factor affecting the work efficiency of operators 17. For example, the environment of sorting stations 16 is not entirely uniform; sorting stations 16 closer to the toilets have higher work efficiency, while sorting stations 16 farther from the toilets tend to have lower work efficiency corresponding to the amount of walking distance.

[0084] Furthermore, the relative location of the sorting station to the storage location of the items being handled can sometimes affect processing time. For example, sorting stations located closer to items with higher frequency of handling may be more efficient. In other cases, sorting stations located near heavy and bulky items may be more efficient but also have a higher workload. Thus, the workload, processing time, and tasks vary for each sorting station. Moreover, these variations are not constant and may change with seasons, trends, and the storage location of items.

[0085] In this embodiment, the work performed by operator 17 at sorting station 16 includes sorting, classification, dispatching, and standby operations for each of the outbound and inbound operations. Standby operations, as referred to here, mean that the sorting station 16 is in a standby state without performing any prescribed operations. This can be included within the outbound or inbound operations as described above, or it can be any state where no operations related to outbound or inbound operations are performed, treated equally with outbound and inbound operations.

[0086] The outbound operation consists of the following steps: the recipient retrieves the items stored on shelf 8, sorts them according to their respective destinations, and places them into the storage compartments of each destination. The inbound operation involves sorting the items arriving at the warehouse according to their respective storage destinations on each shelf 8 and placing the sorted items in their designated positions on the shelves 8. In this embodiment, the outbound and inbound operations are defined as follows.

[0087] The outbound sorting operation is the task of operator 17 retrieving designated items from shelves 8 that have arrived at the door 80 and moving them to the workspace 19. The designated items can be displayed on the output device 73 of the station terminal 7.

[0088] The outbound sorting operation involves placing the items retrieved to the work space 19 into boxes (conveyor components) corresponding to the recipients, and performing packaging and other similar operations. The recipient designation for the items can be displayed on the output device 73 of the station terminal 7.

[0089] The outbound operation involves the operator station terminal 7 requesting the next shelf 8 to be sorted after the sorting operation of the shelf 8 located at the entrance 80 is completed. The warehouse control device 100 sends instructions to move the shelf 8 at the entrance 80 to the conveying device 1, and sends instructions to move the next shelf 8 to the entrance 80 to other conveying devices 1.

[0090] Standby operations are operations that wait for instructions regarding the next operation, such as outbound and inbound operations, or the allocation of tasks to sorting station 16. Here, standby operation refers to the situation where sorting station 16 is in a standby state without performing any scheduled tasks.

[0091] The sorting operation in the inbound process involves retrieving the designated item from the truck or pallet that has arrived at door 80 and moving it to the workspace 19. The item designation can be displayed on the output device 73 of the station terminal 7 in the same manner as in the outbound process.

[0092] The sorting operation of the receiving business involves storing the items retrieved from the work space 19 on the shelves 8 corresponding to their storage destination. The designation of the shelves 8 for storing the items can be displayed on the output device 73 of the station terminal 7.

[0093] The vehicle dispatch operation for the inbound business is initiated by the operation station terminal 7 requesting the next operation for the shelf 8 after the sorting operation of the shelf 8 at the entrance is completed.

[0094] <Summary of the Process>

[0095] Figure 3 This is a flowchart illustrating an example of processing performed by the information processing system of a logistics center. First, based on order information 200, work reservation information 250 is generated according to inventory information 220, shelving information 230, and operator duty information 240 (S1). This process can be generated by input device 72 or by an external computer and acquired by warehouse control device 100.

[0096] The job scheduling information 250, as described below, includes the sorting station 16 where the job will be performed, the assignment of the operator 17, information on the items and shelves 8 to be worked on, and the time when the job will be completed.

[0097] When the operation begins, operator 17 performs the dispatch operation by pressing the dispatch button on station terminal 7, causing the initial shelf 8 to move to sorting station 16 (S2). After operator 17 presses the sorting start button on station terminal 7, the sorting operation begins. Information about the items to be sorted is displayed on the output device 73 of station terminal 7.

[0098] The control device 74 of station terminal 7 notifies the warehouse control device 100 of the start of the sorting operation. The warehouse control device 100 assigns timestamps to the information of sorting station 16 and operator 17 and generates sorting start log information (S3) in station log 310.

[0099] When the sorting operation is completed, operator 17 presses the sorting start button on station terminal 7, and then sorts and packages the items in the receiving and storage section displayed by the output device 73 of station terminal 7 (S4).

[0100] The control device 74 of station terminal 7 notifies the warehouse control device 100 of the start of the sorting operation. The warehouse control device 100 assigns timestamps to the information of sorting station 16 and operator 17, and generates log information of the completion of sorting operation and the start of sorting operation in station log 310.

[0101] When the sorting work is completed, operator 17 presses the dispatch button on station terminal 7 to request the next shelf 8 (S5).

[0102] The control device 74 of station terminal 7 notifies the warehouse control device 100 of the completion of the sorting operation. The warehouse control device 100 assigns timestamps to the information of sorting station 16 and operator 17, and generates log information in station log 310 indicating the completion of the sorting operation and the start of the dispatch operation. When the shelf 8 requesting the dispatch operation arrives at the designated sorting station 16, the warehouse control device 100 generates log information indicating the completion of the dispatch operation in station log 310.

[0103] As described above, when steps S3 to S5 are completed, operator 17 enters standby mode. Operator 17 operates station terminal 7 at the start and end times of the standby mode to notify warehouse control device 100 of the start and end of the standby mode. Warehouse control device 100 receives these notifications, generates log information for the start and end of the standby mode, and stores it in station log 310. Standby mode, as described here, refers to the situation where the sorting station 16 is in a standby state without performing any scheduled operations. This is described here in a way that includes outbound or inbound operations, but the state of not performing any outbound or inbound operations can also be considered a standby state and treated equally with outbound and inbound operations.

[0104] When the standby operation ends, operator 17 returns to step S3 above to begin the next outbound or inbound operation. If operator 17 has finished their shift or is on break, they can use the designated button on the operating station terminal 7 to notify the warehouse control device 100 of their status. Upon receiving the notification, the warehouse control device 100 generates log information corresponding to the notification content and stores it in the station log 310.

[0105] The warehouse control device 100 processes the predicted data 290 for the completion of the calculation operation asynchronously with the processing in steps S2 to S6. The calculation and processing of the predicted data is performed by the data analysis program 163 of the warehouse control device 100. The data analysis program 163 reads the station log 310 at a predetermined period and calculates the business completion time of each sorting station 16.

[0106] First, the warehouse control device 100 reads unprocessed log information from the station log 310, obtains the start and end times of the operation for each sorting station 16, generates information about the progress of the business, and saves it in the station actual result data 320 (S7). Furthermore, as described later, information about the operator 17 responsible for the business and the content of the business can be appended to the station actual result data 320.

[0107] Next, the warehouse control device 100 reads unprocessed log information from the station log 310, obtains the start and end times of the work for each operator 17, generates information about the progress of the work, and saves it in the operator actual result data 330 (S8). Furthermore, as described later, the content of the work and the workload of the operator 17 can be appended to the operator actual result data 330.

[0108] The workload for operator 17 can be preset based on factors such as the weight of the items being processed and the operator's height. The weight of items can also vary seasonally; for example, clothing tends to be heavier in winter and lighter in summer. Even within the same type of clothing, processing winter clothing tends to take longer. Therefore, setting a workload corresponding to the weight of the same type of item improves the accuracy of task completion prediction.

[0109] Furthermore, when the operator 17 is short, there is a tendency for the time to retrieve items stored on the upper part of the shelf 8 to increase during sorting operations. Therefore, by setting a load that corresponds to the height difference of the operator 17, the accuracy of the prediction of the completion of the operation can be improved.

[0110] Next, the warehouse control device 100, based on the actual result data 320 of the station and the actual result data 330 of the operator, as described later, calculates the average time for each operation (or each row) at each sorting station 16 and updates the actual result data 340 for different periods (S9). In this embodiment, the warehouse control device 100, for the average time of each operation, calculates the average time of multiple periods of different lengths for each operation in advance, as described later.

[0111] The warehouse control device 100 uses the actual result data 340, weighting factor 350, and workday characteristics 280 updated in step S9 above to calculate the predicted completion time of each business and each job at each sorting station 16 to generate prediction data 290 (S10).

[0112] The warehouse control device 100 predicts the completion time of operations and business by averaging multiple periods of different lengths, and can generate high-precision forecast data 290 that takes into account the main causes in the short term, medium term, and long term. The weighting coefficient 350 can be preset for each period.

[0113] Furthermore, the warehouse control device 100, by referring to the characteristics of the workday 280, can predict the completion time of the work, taking into account the unevenness of the processed items due to events occurring on each workday. For example, if a known event or obstacle occurs on the workday or the previous day, the accuracy can be improved by referring to the actual station result data 320 and the operator's actual result data 330 of the day when the same event or obstacle occurred.

[0114] Furthermore, by calculating the predicted data 290 for each sorting station 16, the accuracy of the predicted data 290 can be improved, taking into account the differences in operation time caused by the different environments of each sorting station 16. Here, the differences in the environment of each sorting station 16 include: the operation content, workload, deviation of operation time due to the location of the sorting station, the influence of season, changes in popularity relative to the deviation, and changes in the storage location of the items.

[0115] Based on the generated prediction data 290, the warehouse control device 100 generates a prediction screen 51 representing the progress of the entire warehouse and displays it on the output device 140, based on the predicted completion time of the operations of the entire warehouse and each sorting station 16 in the logistics center.

[0116] Managers of logistics centers and warehouses can monitor the progress of operations in the entire warehouse or at each sorting station 16 by referring to the prediction screen 51 on the output device 140. This allows for the prediction of operation times after considering various factors that may affect the operation time of the logistics center.

[0117] The following section provides a detailed explanation of the data used in each process.

[0118] <Data>

[0119] Figure 4A , Figure 4B This is a diagram representing an example of site log 310. Site log 310 is... Figure 3 Steps S2 to S6 are generated by the warehouse control device 100.

[0120] Station log 310 contains device name 311, device ID 312, tag 1 (313), tag 2 (314), tag 3 (315), tag 4 (316), extraction start trigger 317, extraction end trigger 318, and timestamp 319 in a single record.

[0121] Device Name 311 stores the name of the device, etc., that acquired the log. If the device that acquired the log is station terminal 7, Device Name 311 stores the code "ST". Device ID 312 stores the identifier of sorting station 16, station terminal 7, etc. In the illustrated example, "E001" is the identifier of sorting station 16 (or station terminal 7).

[0122] Tag 1 (313) saves the status of operations in progress, standby, and other business processes. Tag 2 (314) saves the status of inbound, outbound, and other business processes. Tag 3 (315) and Tag 4 (316) save the content of operations such as sorting, classifying, and dispatching.

[0123] Extraction start trigger 317 defines the trigger for the start time of extracting log information defined by labels 1 (313) to 4 (316). Extraction end trigger 318 defines the trigger for the end time of extracting log information defined by labels 1 (313) to 4 (316).

[0124] Figure 5 This is a graph representing an example of the actual result data 320 for the station. The actual result data 320 for the station is... Figure 3 Step S7 is generated by the warehouse control device 100.

[0125] The actual result data 320 for the station contains the station ID 321, status 322, operator ID 323, start / end 324, time 325, process ID 326, row number 327, shelf ID 328, and item ID × quantity 329 in a single record.

[0126] Station ID 321 stores the identifier of sorting station 16. Status 322 stores the content of the business. Operator ID 323 stores the identifier of the operator 17 responsible for the business. Operator ID 323 can store the identifiers of multiple operators 17. Start / End 324 stores whether the record represents the start or end of a business. Time 325 stores the date and time of the start or end.

[0127] Processing ID 326 stores the identifier of the business set in the job reservation information 250 (described later). Line number 327 stores the type (number of records) of the item specified by processing ID 326.

[0128] Shelf ID 328 stores the identifier of the shelf 8 for which the business is performed. Item ID × Quantity 329 stores the identifier and quantity of the item for which the business is performed on the specified shelf 8.

[0129] In the illustrated example, state 322 also includes a situation where the prescribed operation is not performed and sorting station 16 is in standby mode. Figure 5In this context, standby (standby operations) are treated the same as outbound or inbound operations. However, as mentioned above, standby (standby operations) can also be included within outbound or inbound operations.

[0130] In the example shown, the following information is stored.

[0131] From 10:00:00 to 10:10:00 on November 1, 2018, at station ID 321 = "E001", operator 17 was in a "standby (ready to wait)" state. "Ready to wait" means that in order to perform a specific task, sorting station 16 is waiting to be assigned to the task.

[0132] From 10:10:00 to 10:12:00, station ID = "E001" and operator ID = C001 were in "standby (going to or shelving transport)" state. "Going to" means that during the operator's standby period, the designated transport device 1 traveled to retrieve the shelf 8 containing the item to be stored. "Shelving transport" means that during the operator ID = C001's standby period, the transport device 1 transported the item with the shelf 8 containing the item already lifted.

[0133] From 10:12:00 to 10:32:00, station ID = E001 and operator ID = C001 performed an "inbound operation". The processing line number for the operation's job reservation information 250 is "1". Based on job reservation information 250, operator ID = C001 of station ID = "E001" performed an inbound operation for 20 items D031 for shelf ID = S011.

[0134] From 10:32:00 to 10:35:00, the station ID = "E001" and the operator ID = "C001" were in the "standby (ready to wait)" state.

[0135] From 10:35:00 to 10:36:00, the station ID = "E001" and the operator ID = "C001" were in the "standby (go to or shelf transport)" state.

[0136] From 10:36:00 to 11:10:00, station ID = "E001" and operator ID = C001 performed an outbound operation. The processing line number for the operation's job reservation information 250 was "1". Based on job reservation information 250, station ID = "E001" performed an outbound operation from shelf S049, removing at least 30 items D021.

[0137] From 10:40:00 to 10:41:00, station ID = E001 and operator ID = C001 were in "standby (safety sensor detected)". "Safety sensor detected" means, for example, that the safety light curtain 81 of sorting station 16 was blocked. That is, during this period, station ID = "E001" will interrupt operations.

[0138] From 11:10:00 to 11:15:00, the station ID = "E001" and the operator ID = "C001" were in the "standby (ready to wait)" state.

[0139] Figure 6 This is a graph representing an example of operator's actual result data 330. Operator's actual result data 330 is in... Figure 3 Step S8 is generated by the warehouse control device 100.

[0140] The actual result data of the operator 330 includes operator ID 331, status 332, start / end 333, time 334, process ID 335, item ID × quantity 336, and load 337 in a single record.

[0141] Operator ID 331 stores the identifier of Operator 17. Status 332 stores the content of the operation. Start / End 333 stores which of the operations this record belongs to, the start or the end. Time 334 stores the date and time of the start or end.

[0142] Processing ID 335 stores the identifier of the business set using the job reservation information 250 described later. Item ID × Quantity 336 stores the identifier and quantity of the item to be performed. Load 337 stores the ratio of the load assigned to operator 17. Load 337 can also be determined based on the characteristics of each operator 17, the size and weight of the item to be performed, etc.

[0143] The warehouse control device 100 can calculate the operation time of each state 322 according to each processing ID 335 of the operator 17, and use it as the operation time of the business stored in the processing ID 335 of the operator actual result data 330 and the state 322 of the station actual result data 320 corresponding to the operator ID 331.

[0144] Figure 7A This is a graph representing an example of actual result data 340 over different periods. The actual result data 340 over different periods is... Figure 3 Step S9 is generated and updated by the warehouse control device 100.

[0145] The actual results data for different periods 340 in a single record include station ID 341, job content 342, average for period A 343, average for period B 344, average for period C 345, average for period D 346, and update date and time 347.

[0146] Station ID 341 stores the identifier of sorting station 16. Job content 342 stores the content of the jobs performed at sorting station 16. Average of period A 343 to Average of period D 346 stores the average time of each job for multiple periods of different lengths.

[0147] Figure 7B This represents the length of each period A through D in the actual result data 340 for different periods. The length of each period A through D is preset and can be appropriately set according to the operational status of the logistics center, warehouse, etc. In the example shown, period A represents the past 1 hour, period B represents the past 1 week, period C represents the past 1 month, and period D represents the past 3 months.

[0148] For example, Figure 7A The average of period A is 343. Figure 7B As shown in equation (1), the average time from the start to the end of the task is calculated for each task content 342 based on the actual result data 330 of the operator from the current time to the past hour.

[0149] Similarly, the average of 344 for period B is the average retention time for each task 342 based on the actual results data 330 of operators over the past week from the present; the average of 345 for period C is the average retention time for each task 342 based on the actual results data 330 of operators over the past month from the present; and the average of 346 for period D is the average retention time for each task 342 based on the actual results data 330 of operators over the past three months from the present. The actual results data 340 for each period is the average retention time for each task at each sorting station 16.

[0150] The average time for each period is calculated by dividing the sum of the times for all operations performed at each sorting station 16 by the number of rows for each operation. Here, the number of rows for each operation, as described later, is obtained by counting each record of the operation scheduling information 250 as one row. For example, in... Figure 10 In the text, the item IDs "D009" and "1XXX" are counted in different rows.

[0151] In addition, the outbound and inbound operations in the diagram, as the sum of the average time of sorting, classification, outbound operations, and standby operations, can also be stored as actual result data 340 for different periods at each sorting station 16 (ST in the diagram).

[0152] Furthermore, a short period like period A can extract the actual results dependent on operator 17, while a long period like period B to period D is not limited to a specific operator 17 but can extract the actual results of multiple operators 17 at each sorting station 16.

[0153] Figure 8 This is a diagram illustrating an example of workday characteristic 280. Workday characteristic 280 is information pre-set to correct for predicted completion times. A record of workday characteristic 280 includes date 281, event 282, season 283, and correction factor 284.

[0154] Event 282 stores the content of the phenomenon (or the predetermined phenomenon) that occurred on date 281. Season 283 stores the season of date 281. Correction coefficient 284 stores the coefficient used to correct the predicted completion time according to event 282 and season 283. The correction coefficient stores the value preset according to the content of event 282 and season 283.

[0155] Figure 9 This is a diagram illustrating an example of a weighting factor of 350. The weighting factor of 350 is information that is pre-set to apply weights to each period A through D when calculating the completion time of the prediction.

[0156] The weighting coefficient 350 contains station ID 351 and coefficients A352 to D355 in a single record.

[0157] Station ID 351 stores the identifier of sorting station 16. Coefficients A352 to D355 are weighting coefficients corresponding to periods A to D of the actual result data 340 for different periods mentioned above, and are set for each sorting station 16. Among them, the weighting coefficients A to D for each period A to period D can also be a single value for the entire warehouse (or logistics center), as shown in the figure where station ID = "whole".

[0158] Figure 10 This is a diagram illustrating an example of job scheduling information 250. Job scheduling information 250 is... Figure 3 The information set in step S1.

[0159] The job scheduling information 250 contains the following information in a record: Process ID 251, Business Content 252, Station ID 253, Operator ID 254, Scheduled Completion Time 255, Item ID 256, Quantity 257, Shelf ID 258, Category Destination 259, and Actual Completion Time 2511.

[0160] Processing ID 251 is a unique identifier stored within the warehouse. Business content 252 stores the details of outbound and inbound operations. Station ID 253 stores the identifier of the sorting station 16 that performs the operations.

[0161] Operator ID 254 stores the identifier of operator 17 assigned to sorting station 16. Completed Scheduled Time 255 stores the target date and time for the completion of this operation. Item ID 256 stores the identifier of the item processed in this operation. Quantity 257 stores the quantity of the items.

[0162] Shelf ID 258 stores the identifier of the shelf 8 containing the item. The relationship between the item and shelf 8 is set using inventory information 220 (described later). Category Destination 259 stores the destination of the delivered item. Furthermore, in the case of an inbound transaction, the purchaser of the item can be stored. Actual Result Completion Time 2511 stores the date and time the transaction was actually completed.

[0163] In the job reservation information 250, one record for item ID 256 is designated as row number 1. For example, because processing ID 251 = "22" has two records with item IDs "D009" and "1XXX", processing ID 251 is designated as row number 2. That is, in this embodiment, the row number represents an example that corresponds to the type of item regardless of quantity 257. The ticket number of order information 200 and the processing ID of job reservation information 250 can also use corresponding data or the same data.

[0164] Figure 11 This is a graph representing an example of predicted data 290. Predicted data 290 is... Figure 3 The information generated in step S10. The prediction data 290 contains in a record the process ID 291, business content 292, station ID 293, operator ID 294, prediction completion time 295, job content 296, start / end 297, and start prediction time / end prediction time 298.

[0165] Processing ID 291 stores the identifier corresponding to Processing ID 251 in Job Reservation Information 250. Business Content 292 stores the business corresponding to Business Content 252 in Job Reservation Information 250. Station ID 293 stores the identifier of sorting station 16 corresponding to Station ID 253 in Job Reservation Information 250. Operator ID 294 stores the identifier corresponding to Operator ID 254 in Job Reservation Information 250.

[0166] 295 Saves the predicted completion time of the business from the warehouse control device 100. 296 Saves the items included in the business (standby operations, sorting operations, classification operations, dispatch operations). 297 Saves the start and end labels for each operation. 298 Saves the predicted start or end time for each operation from the warehouse control device 100.

[0167] In this embodiment, the start prediction time is the end prediction time of the upcoming operation, and the start prediction time of the initial operation at the start of the business can be the time when the warehouse control device 100 receives the operation start information.

[0168] Figure 12 This is a diagram illustrating an example of device information 260. Device information 260 is information obtained from the transport device 1 by the transport device control program 164 of the warehouse control device 100. Figure 3 The information of the transport device 1 indicating the start of transport is provided in step S5, etc.

[0169] Device information 260 includes device ID 261, operating status 262, remaining battery level 263, load information 264, location information 265, and comprehensive judgment 266 in a single record.

[0170] Device ID 261 stores the identifier assigned to the transport device 1. Operating status 262 indicates the state of the transport device 1, such as being in operation, standby, moving, stopped, or charging. Battery remaining amount 263 stores the remaining amount (percentage) of the battery in the transport device 1.

[0171] Load information 264 stores information about the load accumulated in the transport device 1 (travel time, travel distance, etc.). Location information 265 stores the location of the transport device 1 within the warehouse. In this embodiment, an example is shown where the floor surface within the storage space 12 is divided into a grid pattern to allocate locations.

[0172] The comprehensive judgment 266 saves the transport device control program 164 based on the remaining battery level 263 and load information 264 to determine the status of the transport device 1. For example, the comprehensive judgment 266 indicates that the device can be used if it is "A", that maintenance is recommended if it is "B", and that maintenance is required if it is "C".

[0173] The device information 260 can include information about the sorting station 16 that is assigned to the transport destination of the transport device 1, its operating status, cumulative load, and whether it is in normal working order.

[0174] The warehouse control device 100 can calculate and predict the operation time by correcting the operation time of the sorting station 16 based on the device information 260 and the number of transport devices 1 going to the sorting station 16 and whether there are any abnormalities in the transport devices 1.

[0175] In addition, the calculation of the predicted operation time can be corrected if the indicators (such as the number of workstations) included in the device information 260 are lower than a specified threshold.

[0176] Furthermore, if the conveying device 1 is pre-assigned to one or more specific sorting stations 16, the above-described processing is performed on each corresponding sorting station 16 and the conveying device 1.

[0177] Figure 13 This is a diagram representing an example of operator duty information 240. Operator duty information 240 contains operator ID 241, length of service 242, height 243, duty schedule consisting of date 244 and time 245, and status 246 in a single record.

[0178] Operator ID 241 stores the identifier assigned to Operator 17. Length of Service 242 stores Operator 17's work history. Height 243 stores Operator 17's height. Date 244 and Time 245 store Operator 17's work schedule. Status 246 stores values ​​entered based on Operator 17's health status, whether they are injured, etc.

[0179] Figure 14 This is an example of a prediction screen 51. Prediction screen 51 is... Figure 3 Step S11 is generated by the data analysis program 163. An illustrated example is a dashboard 52 displaying a forecast of the business progress calculated by the data analysis program 163 on the screen of the output device 140. The display of the forecast screen 51 can be indicated by the input device 130 of the warehouse control device 100.

[0180] The dashboard 52 includes: a business readiness target window 52a that shows the overall business objectives and progress of the warehouse; a progress information window 52b that shows the progress status and actual results of processing; an overall progress window 52c that shows the overall progress of the warehouse; a job completion time prediction window 52d that shows the predicted completion time of the warehouse; a master productivity shift window 52e that shows the warehouse's productivity; and a job rate / work rate window 52f that shows the job rate and work rate within the warehouse.

[0181] The business readiness target window 52a displays the start and end times of today's jobs, as well as the current progress rate and scheduled end time of the business. The job start and end times are data set using the job scheduling information 250.

[0182] The progress rate is the ratio of the number of outbound or inbound processing rows actually completed by the application today, based on the station logs generated up to the current time (310). The scheduled completion time is the predicted completion time for all business operations at the current time.

[0183] The progress information window 52b displays the progress rate, number of rows, number of rows / station, workstation, station operation rate, equipment working efficiency, and equipment operation rate.

[0184] The progress rate, like the progress rate in the business readiness target window 52a, represents the overall value of the warehouse. The number of rows is the percentage of the processed rows of all scheduled tasks 250 to be executed today, based on the actual station result data 320 and the actual operator result data 330 generated up to the current time.

[0185] Rows / Station: The percentage of the actual completed processing rows in the actual station result data 320 and job reservation information 250 generated up to the current time, representing the percentage of the total number of processing rows for inbound and outbound operations to be executed today (this can be a value for a specific sorting station 16 or a value for all sorting stations 16 in the warehouse).

[0186] Workstation represents the percentage of all sorting stations 16 that have worked at least once in the past n minutes (e.g., 10 minutes) since the current moment.

[0187] The sorting station operation rate represents the percentage of time elapsed from the start of the operation to the present time during which outbound or inbound operations were carried out at the sorting station 16 (this can be a value for a specific sorting station 16 or a value for all sorting stations 16 in the warehouse).

[0188] The device efficiency represents the percentage of time that the conveying device 1 has spent on "preparatory work", "external preparatory work" or "charging" from the start of the operation to the present time (it can be a value for a specific conveying device 1 or a value for the entire conveying device 1).

[0189] The equipment operation rate represents the percentage of time that the transport device 1 has spent in "preparatory work" from the start of the operation to the present time (it can be a value for a specific transport device 1 or a value for the entire transport device 1).

[0190] The Overall Progress window 52c uses a chart to represent the values ​​of Overall Progress, Block 2, and Block 3. Overall Progress is the same value as the Progress Rate in the Business Readiness Target window 52a. Block 2 represents the Progress Rate looking back at a specified time (e.g., 60 minutes) from the current moment. Block 3 represents the Progress Rate looking back at a specified time (e.g., 120 minutes) from the current moment.

[0191] The 52d window for job completion time prediction uses a chart to represent the values ​​of the inbound and outbound predictions. Here, the horizontal axis of the chart represents time, and the vertical axis represents the quantity of items. The inbound prediction is represented by a line segment connecting the origin to the inbound completion prediction time and the inbound prediction quantity. The outbound prediction is represented by a line segment connecting the origin to the outbound completion prediction time and the outbound prediction quantity. Inbound represents the cumulative quantity of items actually received into the warehouse over the time series. Outbound represents the cumulative quantity of items actually shipped out over the time series.

[0192] The Master Productivity Shift Window 52e uses a chart to represent the values ​​for rows / hour, picks / hour, and number of workstations. Here, the horizontal axis of the chart represents time, and the vertical axis represents the number of rows (left scale), picks, and number of workstations (right scale).

[0193] Rows / h represents the cumulative time series value indicating the number of rows processed in a job that was executed within one hour. Picks / h represents the cumulative time series value of the number of outbound (or inbound) transactions executed within one hour. Workstations represents the cumulative time series value of the number of sorting stations 16 that have performed at least one job in the past n minutes (e.g., 10 minutes) since the current time.

[0194] The Operation Rate / Efficiency window 52f uses charts to represent the station operation rate, conveyor efficiency, and conveyor operation rate values. Here, the horizontal axis of the chart represents time, and the vertical axis represents the operation rate or efficiency.

[0195] The station operation rate represents the time series of the station operation rate in progress information window 52b. The transport device efficiency represents the time series of the transport device efficiency in progress information window 52b. The transport device operation rate represents the time series of the transport device operation rate in progress information window 52b.

[0196] The predicted image 51 is not limited to the methods described above and can be appropriately changed based on the operational status of the logistics center and warehouse. For example, it can also be like... Figure 18 As shown, the job completion time prediction window 52d indicates the time to be completed (scheduled completion time 522) and the predicted completion time (predicted completion time 523) in descending order of the business delay 524 from sorting stations 16 (station ID 521).

[0197] By using the aforementioned operation completion time prediction window 52d, warehouse managers can easily and quickly grasp the operational delays at sorting stations 16 within the warehouse and the magnitude of those delays.

[0198] Figure 15This is a diagram representing an example of order information 200. Order information 200 contains, in a single record, serial number 201, ticket number 202, store name 203, store code 204, product name 205, product code 206, quantity 207, delivery deadline 208, and order receipt date and time 209.

[0199] Serial number 201 is a unique number assigned by warehouse control device 100. Ticket number 202 is a unique number assigned by warehouse control device 100 for each order. Store name 203 indicates the destination of the goods.

[0200] In this embodiment, we illustrate an example where different serial numbers 201 are assigned when the ticket number 202 is the same but the product name 205 and product code 206 are different. This is because, when the product name 205 and product code 206 are different, the shelves 8 storing each product may be different.

[0201] The number 207 in the voucher number 202 of this record indicates the quantity of goods ordered, identified by product name 205 and product code 206. The order receipt date and time 209 is the date and time on which the warehouse control device 100 (or logistics center) accepted the order for voucher number 202.

[0202] Figure 16 This is a diagram representing an example of inventory information 220. Inventory information 220 contains a serial number 221, product name 222, product code 223, inventory quantity 224, shelf ID 225, and configuration position within the shelf 226 in a single record.

[0203] Shelf ID 225 stores an identifier for the shelf 8 containing the item. The shelf configuration position 226 stores information used by the sorting station operator 17 and robot 18 during sorting operations. For example, a record labeled "U3R2" indicates that an item is configured in shelf 8 at the "3rd layer from top (U), 2nd position from right (R)".

[0204] Figure 17 This is a diagram representing an example of shelf information 230. Shelf information 230 includes a serial number 231, shelf ID 232, storage location 233, shelf weight 234, and product weight 235 in a single record.

[0205] Shelf ID 232 stores a unique identifier assigned to each shelf 8. For example, shelf ID 232 can also store an identifier assigned to the shelf 8 by the warehouse control device 100. Storage location 233 stores the location information of the storage space 12 of the stored shelf 8, such as coordinates from a map. In the case of transporting the shelf 8, storage location 233 stores "in transit".

[0206] The shelf weight 234 stores the weight of the shelf 8 itself, and the goods weight 235 stores the weight of the items (goods, containers for storing goods, etc.) carried on the shelf 8. The weight of the transported items (shelf + goods) transported by the transport device 1 is at least the sum of the "shelf weight" and the "goods weight".

[0207] For example, in Figure 16 In the inventory information 220, the weight and inventory quantity of each item are recorded in advance. For example, the weight of the transported item (shelf + items) can be calculated. When the "weight" is calculated, if the error between the actual weight of the transported item and the calculated value is within the allowable range, the weight of some items on shelf 8 and the items carried on shelf 8 can be excluded from the calculation.

[0208] Furthermore, as another example, a weight sensor equipped with a weight sensor capable of measuring the weight of the "transported item (shelf + goods)" transported by the conveyor 1 can be used to measure the weight when the sorted shelf 8 is returned to its storage location. In this case, the warehouse control device 100 may receive the weight measured by the conveyor 1 and record it as the "weight of the transported item (shelf + goods)" in the shelf information 230.

[0209] <Predictive Data Generation>

[0210] Next, regarding Figure 3 The generation and processing of job prediction data in step S10 will be explained. Warehouse control device 100, in Figure 3 In step S9, the average operation time is calculated for each sorting station 16 for each period A to D of different lengths according to the type of operation, and the average operation time is saved as actual result data 340 for different periods.

[0211] That is, in the actual result data 340 for different periods, for each period A to D, the average time of sorting operation, the average time of classification operation, the average time of dispatch operation and the average time of standby time of each sorting station 16 are saved.

[0212] Next, in Figure 3 In step S10, the warehouse control device 100 uses actual result data 340 from different periods and weighting coefficient 350 to calculate the predicted completion time 295 for each operation at each sorting station 16. The warehouse control device 100 calculates the predicted time (sorting prediction time, classification prediction time, dispatch prediction time, and standby prediction time) for each operation according to the following formula (2).

[0213] [Mathematical Expression 1]

[0214]

[0215] In equation (2) above, the average of period A ~ the average of period D is Figure 7A The actual results data for different periods are: the average of each task content in period A (342) to period D (343) of the actual results data for each period (342), and the coefficients A to D are... Figure 9 The weighting coefficient 350 is coefficient A352 to coefficient D355.

[0216] In addition, coefficients A352 to D355 can use the weighting coefficients inherent to sorting station 16 as described above, or they can use the weighting coefficients of the entire warehouse.

[0217] The values ​​obtained by adding the sorting prediction time, classification prediction time, departure prediction time, and standby prediction time to the start prediction time (298) are respectively: Figure 11 The prediction data 290 is the end prediction time (start prediction time / end prediction time 298). Each prediction time is the predicted operation time obtained by predicting the operation time of the currently implemented operation based on the actual result data of each operation content (station actual result data 320, operator actual result data 330).

[0218] Next, the warehouse control device 100 envisions implementing the corresponding business (processing ID 251) at multiple sorting stations 16 and calculates the predicted time of the business according to the following formula (3).

[0219] [Mathematical Expression 2]

[0220]

[0221] The predicted time from the end of the business consisting of each operation of the sorting station 16 that performs the corresponding business (processing ID251) is calculated by adding the sum of the predicted sorting time, predicted classification time, predicted departure time and predicted standby time of each sorting station 16 divided by the number of sorting stations 16 that perform the corresponding business (the number of work STs in the figure) according to the above formula (3).

[0222] The predicted time until the end of each operation is calculated as the predicted time for each sorting operation, the predicted time for each classification operation, the predicted time for each dispatch operation, and the predicted time for each standby operation.

[0223] Next, the warehouse control device 100 calculates the predicted completion time of the corresponding business (processing ID 251) according to the following formula (4).

[0224] [Mathematical Expression 3]

[0225]

[0226] In equation (4) above, the number of remaining rows represents the number of unprocessed item ID256 records in the record of processing ID251 for this service. Moreover, the progress rate can be obtained by subtracting the number of unprocessed item ID256 records from the total number of records with the same processing ID251, and dividing the result by the total number of records with the same processing ID251.

[0227] As shown above, the sum of the predicted times for each operation of the business unit, multiplied by the number of rows of the operation reservation information 250 for outbound or inbound operations, divided by the number of sorting stations 16 (work STs) for that operation, is used to calculate the predicted time from the current moment until the completion of that operation.

[0228] The value obtained by adding the predetermined completion time of the above formula (4) to the current time is calculated as the predicted completion time 295 of the prediction data 290. In addition, the data analysis program 163 of the warehouse control device 100 can also calculate the above progress rate for each sorting station 16 and display it on the prediction screen 51.

[0229] As described above, in the warehouse control device 100 of this embodiment, by predicting the operation time until the completion of the business based on the average operation time of multiple periods of different lengths for each type of operation, a high-precision operation time prediction can be achieved while taking into account various main causes of variation.

[0230] Furthermore, the date at which the predicted completion time is calculated (295) belongs to... Figure 8 In the case of the date with the workday characteristic 280 shown, the scheduled completion time calculated by the above formula (4) can also be multiplied by Figure 8 The correction factor is used. Therefore, the completion time can be corrected using the correction factor 284, which corresponds to the event inherent in the date of the outbound or inbound transaction, thus improving forecast accuracy.

[0231] Furthermore, when calculating the predicted work time for the current work (work on the first work day), if the work day characteristic 280 of the previous current work day meets the specified conditions (event 282), it is possible to obtain the actual result data (station actual result data 320, operator actual result data 330) of one or more other work days that meet the specified conditions, and calculate the predicted work time based on the actual result data.

[0232] also, Figure 9The values ​​of coefficients A352 to D355 of the weighting factor 350 shown gradually decrease from the current moment to the past. The recent actual operation results strongly reflect the status of operator 17 and the unevenness of temporary orders. In contrast, the actual operation results for a long period can reflect the various changes in operation time caused by the unevenness of the types and locations of goods shipped out due to seasons and trends.

[0233] In the above example, an operator 17 is shown operating the station terminal 7 set up in the sorting station 16, but it is not limited to this. For example, a mobile terminal such as a smartphone or smartwatch can also be used to communicate with the warehouse control device 100 to receive work instructions and send work progress information.

[0234] <Conclusion>

[0235] The information processing system described in the above embodiments can adopt the following structure.

[0236] (1) An information processing system, characterized in that it comprises: a warehouse control device (100) having a processor (arithmetic unit 110) and a memory (120); a transport device (1) capable of transporting a storage unit for storing items according to a transport instruction from the warehouse control device (100); and a terminal (station terminal 7) connected to the warehouse control device (100) for receiving and sending work information of a work station (sorting station 16), wherein one or more operations concerning the storage or retrieval of items in the storage unit (shelf 8) are performed in the work station, and the warehouse control device (100) has: a storage unit (data input / output program 162), It obtains information about the actual results of the work from the work information of at least one of the inbound and outbound processes in the work station (16) from the terminal (7) and stores it as log information (station log 310); and the control unit (data analysis program 163) generates information about the work time, i.e., multiple actual result data (320, 330), in each of the multiple periods (A to D) of different lengths with pre-set values ​​for each of the above-mentioned types of work based on the log information (310), and calculates the predicted work time (completion time 255) for each of the above-mentioned types of work based on the multiple actual result data (320, 330).

[0237] By adopting the above structure, the warehouse control device 100 can accurately estimate the end time of the operation based on the actual station result data 320 and the actual operator result data 330 in each of the multiple periods of different lengths.

[0238] (2) The information processing system as described in (1) above, characterized in that: the plurality of periods (A to D) includes a first period (period A) and a second period (period D) longer than the first period (period A), the first period (period A) is the period during which the first operator (17) performs the work at the first work station (16) in the work station (16), the second period (period D) is the period during which the work is performed by a plurality of operators (17) at the first work station (16), and the control unit (163) The first actual result data (320, 330) and the second actual result data (320, 330) of the operation time of the operation in the first period (period A) are obtained, and the predicted operation time (255) of the first operator (17) in the first work station (16) is estimated based at least on the first actual result data (320, 330) and the second actual result data (320, 330).

[0239] By adopting the above structure, the warehouse control device 100 is able to calculate the operating time of the sorting station 16 environment, which reflects the actual results dependent on the operator in the short period A and reflects the influence of the specific operator 17 in the long period D.

[0240] (3) The information processing system as described in (1) above, characterized in that: the plurality of periods (A to D) includes a first period (period A) and a second period (period D) longer than the first period (period A), the first period (period A) being the period from the first moment to the second moment in a specific workday, and the second period (period D) including at least a plurality of workdays.

[0241] By adopting the above structure, the warehouse control device 100 can improve the accuracy of work time estimation by using a first period that reflects the recent work efficiency that is highly dependent on the operator 17, etc., and a second period that reflects the work efficiency that changes due to the long-term trend of the type and quantity of items.

[0242] (4) The information processing system as described in (1) above is characterized in that: the storage unit (162) further includes information representing the characteristics of each workday, and when the control unit (163) calculates the predicted work time (255) of the work on the first workday, if the characteristics (workday characteristics 280) of the first workday meet the specified conditions (event 282), it acquires actual result data (320, 330) of the work time of the work on one or more second workdays that meet the specified conditions (282), and calculates the predicted work time (255) based on the actual result data (320, 330).

[0243] By adopting the above structure, the warehouse control device 100 can predict the current operation time by referring to the past station actual result data 320 and operator actual result data 330 with similar characteristics of the operation day, thereby ensuring the prediction accuracy of the operation time for a specific operation day.

[0244] (5) The information processing system as described in (4) above, characterized in that: the characteristic (280) of the first working day satisfies the specified condition (282) when the first working day is a date (281) associated with a specific event (282).

[0245] By adopting the above structure, the warehouse control device 100 can ensure the accuracy of the prediction of the operation time for the operation day in which the specific event 282 occurs by referring to the past actual station result data 320 and operator actual result data 330, which are similar to the characteristics of the operation day.

[0246] (6) The information processing system as described in (1) above, characterized in that: the control unit (163) calculates the predicted operation time (255) of each of the one or more operations by calculating the weighted average of the plurality of actual result data (320, 330), wherein the plurality of actual result data (320, 330) includes: first actual result data (320, 330) as information about operation time in a first period (period A); and second actual result data (320, 330) as information about operation time in a second period (period D) longer than the first period (period A), wherein the weighting coefficient corresponding to each of the plurality of actual result data (320, 330) when calculating the weighted average of the plurality of actual result data (320, 330) is set to a smaller value than the weighting coefficient corresponding to the first actual result data (320, 330).

[0247] By adopting the above structure, when the warehouse control device 100 calculates the actual result data 340 for each different period A to D by weighted average, the weighting coefficient 350 is set to be smaller for longer periods D, thereby strongly reflecting the influence of the most recent operator 17, etc.

[0248] (7) The information processing system as described in (1) above, characterized in that: the storage unit (162) obtains information about the actual results of the job from multiple job stations (16) and stores it as the log information (310); the control unit (163) obtains the multiple actual results data (320, 330) of each job station (16) and calculates the predicted job time (255) according to each job station (16).

[0249] By adopting the above structure, the warehouse control device 100 can predict the operation time according to the environment of the sorting station 16, thereby improving the prediction accuracy of the overall warehouse operation time.

[0250] (8) The information processing system as described in (7) above, characterized in that: at least one of the multiple workstations (16), multiple operators (17) perform operations.

[0251] By adopting the above structure, the warehouse control device 100 can predict the operation time corresponding to the environment of the sorting station 16, which is not limited to a specific operator, by acquiring actual result data for each sorting station 16 when multiple operators 17 take turns working at the same sorting station 16.

[0252] (9) The information processing system as described in (1) above is characterized in that: the storage unit (162) stores at least transport device information (device information 260) including information about the working status of the plurality of transport devices (1), and the control unit (163) calculates the predicted operation time (255) based on the plurality of actual operation result data (320, 330) and the transport device information (260).

[0253] By adopting the above structure, the warehouse control device 100 can improve the accuracy of prediction by calculating the operation time after taking into account the information of the transport device 1.

[0254] (10) The information processing system as described in (1) above is characterized in that: the storage unit (162) stores the information of the operators (17) performing the work at the multiple work stations (16), namely the operator (17) information, and stores it in the log information (310); the control unit (163) calculates the predicted work time (255) based on the actual result data (320, 330) of the multiple work and the operator (17) information.

[0255] By adopting the above structure, the warehouse control device 100 can improve the accuracy of the prediction data 290 by taking into account the differences in operation time caused by the different environments of each sorting station 16 based on the information of the operator 17.

[0256] (11) The information processing system as described in (1) above, characterized in that: the one or more operations include a sorting operation performed on the storage unit (8), and the control unit (163) calculates the predicted operation time (255) of the sorting operation based on the plurality of actual result data (320, 330).

[0257] By adopting the above structure, the warehouse control device 100 is able to predict the operation time of sorting operations performed at the sorting station 16.

[0258] (12) The information processing system as described in (1) above is characterized in that: the control unit (163) acquires load information (337) about the workload corresponding to the work content of the sorting operation, and calculates the predicted work time (255) of the sorting operation based on the load information (337) and the plurality of actual result data (320, 330).

[0259] By adopting the above structure, the warehouse control device 100 can improve the prediction accuracy by taking into account the workload of the sorting operator 17 when predicting the operation time.

[0260] (13) The information processing system as described in (1) above is characterized in that: the one or more operations include a sorting operation of sorting items sorted from the storage unit (8), and the control unit (163) calculates the predicted operation time (255) of the sorting operation based on the plurality of actual result data (320, 330).

[0261] By adopting the above structure, the warehouse control device 100 is able to predict the operation time of the sorting operation performed at the sorting station 16.

[0262] (14) The information processing system as described in (1) above is characterized in that: the one or more operations include a dispatch operation in which the transport device (1) starts the transport of the storage unit (8) after the sorting operation performed on the storage unit (8), and the control unit (163) calculates the predicted operation time (255) required for the dispatch operation based on the plurality of actual result data (320, 330).

[0263] By adopting the above structure, the warehouse control device 100 is able to predict the operation time of the dispatch operation carried out at the sorting station 16.

[0264] (15) The information processing system as described in (1) above, characterized in that: the log information (310) includes information on the time when the workstation (16) is in standby mode without performing the one or more of the above-mentioned tasks, and the control unit (163) acquires information on the time when the workstation (16) is in standby mode in each of the multiple periods (A to D) of different lengths set in advance, and calculates the predicted standby time when the workstation (16) becomes the standby mode based on the information.

[0265] By adopting the above structure, the warehouse control device 100 is able to calculate and predict the standby time when the sorting station 16 will become standby.

[0266] (16) The information processing system as described in (15) above is characterized in that: the one or more operations include at least: sorting operations performed on the storage unit (8); sorting operations performed on the sorted items from the storage unit (8); and dispatch operations performed by the transport device (1) to transport the storage unit (8) after the sorting operations performed on the storage unit (8); the storage unit (162) has operation reservation information (250) regarding the operation of either or both of the inbound and outbound operations to be performed on the specified operation day; and the control unit (163) calculates the date and time of completion of either or both of the inbound and outbound operations on the specified operation day, i.e., the end prediction time (completion reservation time 255), based on the predicted operation time (255) for each type of the one or more operations, the predicted standby time, and the operation reservation information (250).

[0267] By adopting the above structure, the warehouse control device 100 can accurately calculate the completion time of inbound and outbound operations for a specified workday.

[0268] (17) The information processing system as described in (16) above is characterized in that it has an output device that can visually display the predicted end time (completion time 255) of the specified workday at the current time.

[0269] By adopting the above structure, the warehouse control device 100 is able to visualize and display the scheduled completion time 255 of the work for the specified workday at the current moment.

[0270] This invention is not limited to the embodiments described above, but includes various modifications. For example, the embodiments described above are detailed for ease of understanding and illustration of the invention, and are not limited to having all the structures described. Furthermore, a portion of the structure of one embodiment can be replaced with the structure of another embodiment, and it is also possible to add structures of other embodiments to the structure of one embodiment. In addition, for a portion of the structure of each embodiment, the addition, deletion, or substitution of other structures can be applied individually or in combination.

[0271] Furthermore, some or all of the aforementioned structures, functions, processing units, and processing modules can be implemented in hardware, for example, through integrated circuit design. Additionally, the aforementioned structures and functions can also be implemented in software by a processor interpreting and executing programs to achieve their respective functions. The programs implementing each function, stage folders, and other information can be stored in recording devices such as memory, hard disks, SSDs (Solid State Drives), or recording media such as IC cards, SD cards, and DVDs.

[0272] Furthermore, control lines and information lines represent what is considered necessary for the description, but for the product itself, they do not necessarily represent all control lines and information lines. In fact, almost all the structures can be considered to be interconnected.

Claims

1. An information processing system, characterized in that, include: A warehouse control device with a processor and memory; A transport device capable of transporting the storage section for storing items according to transport instructions from the warehouse control device; and The terminal, connected to the warehouse control device, receives and sends operational information for the workstation, wherein one or more operations related to the storage and retrieval of items in the storage section are performed at the workstation. The warehouse control device has the following features: The storage unit obtains information about the actual result of the job from the job information of at least one of the inbound and outbound processes in the job station from the terminal, and stores it as log information; and The control unit, based on the log information, generates multiple actual result data points about the operation time for each of the above-mentioned types of operations, each with a pre-set duration of different lengths, and calculates the predicted operation time for the corresponding operation based on these multiple actual result data points.

2. The information processing system as described in claim 1, characterized in that: The plurality of periods includes a first period and a second period that is longer than the first period. The first period refers to the period during which the first operator performs the work at the first work station in the work station. The second period refers to the period during which multiple operators performed the work at the first work station. The control unit acquires information about the operation time of the task during the first period, i.e., first actual result data, and information about the operation time of the task during the second period, i.e., second actual result data, and calculates the predicted operation time for the first operator to perform the task at the first work station based at least on the first actual result data and the second actual result data.

3. The information processing system as described in claim 1, characterized in that: The plurality of periods includes a first period and a second period that is longer than the first period. The first period is the period from time 1 to time 2 within a specific workday. The second period includes at least multiple working days.

4. The information processing system as described in claim 1, characterized in that: The storage unit also includes information representing the characteristics of each workday. When the control unit calculates the predicted operation time for the operation on the first operation day, if the characteristics of the first operation day meet the specified conditions, it acquires actual result data on the operation time of the operation on one or more second operation days that meet the specified conditions, and calculates the predicted operation time based on the actual result data.

5. The information processing system as described in claim 4, characterized in that: The characteristic of the first working day satisfies the specified condition if the first working day is a date associated with a specific event.

6. The information processing system as described in claim 1, characterized in that: The control unit calculates the predicted operation time for each of the above-mentioned tasks by calculating a weighted average of the multiple actual result data. The plurality of actual result data includes: first actual result data as information about work time in the first period; and second actual result data as information about work time in the second period, which is longer than the first period. When calculating the weighted average of the multiple actual result data, the weighting coefficients corresponding to each of the multiple actual result data are set to a smaller value than the weighting coefficients corresponding to the first actual result data.

7. The information processing system as described in claim 1, characterized in that: The storage unit obtains information about the actual results of the job from multiple job stations and stores it as the log information. The control unit acquires the actual result data of multiple operations at each workstation and calculates the predicted operation time for each workstation.

8. The information processing system as described in claim 7, characterized in that: At least one of the multiple workstations, multiple operators perform tasks.

9. The information processing system as described in claim 1, characterized in that: The storage unit stores transport device information, including at least information about the operating status of the various transport devices. The control unit calculates the predicted operation time based on the actual result data of multiple operations and the information of the transport device.

10. The information processing system as described in claim 1, characterized in that: The storage unit stores information about the operators performing tasks at the multiple workstations, i.e., operator information, and stores this information in the log information. The control unit calculates the predicted operation time based on the actual result data of multiple operations and the operator information.

11. The information processing system as described in claim 1, characterized in that: The above-mentioned operations include sorting operations performed on the storage section. The control unit calculates the predicted operation time of the sorting operation based on the multiple actual result data.

12. The information processing system as described in claim 11, characterized in that: The control unit acquires load information about the workload corresponding to the work content of the sorting operation, and calculates the predicted operation time of the sorting operation based on the load information and the multiple actual result data.

13. The information processing system as described in claim 1, characterized in that: The one or more operations include a sorting operation of classifying items picked out from the storage section. The control unit calculates the predicted operation time for the classification operation based on the multiple actual result data.

14. The information processing system as described in claim 1, characterized in that: The above-mentioned operations include a dispatch operation in which the transport device begins transporting the storage unit after the sorting operation performed on the storage unit. The control unit calculates the predicted operation time required for the departure operation based on the multiple actual result data.

15. The information processing system as described in claim 1, characterized in that: The log information includes information about the time during which the job station was in standby mode without performing one or more of the aforementioned jobs. The control unit acquires information about the time the workstation is in the standby state during each of the multiple periods of different pre-set lengths, and calculates the predicted standby time for the workstation to enter the standby state based on the information.

16. The information processing system as described in claim 15, characterized in that: The one or more operations include at least: sorting operations performed on the storage section; sorting operations performed on the items picked out from the storage section; and dispatching operations performed by the transport device to start transporting the storage section after the sorting operations performed on the storage section. The storage department has work schedule information regarding one or both of the inbound and outbound operations to be performed on a specified workday. The control unit calculates the predicted date and time, i.e. the end prediction time, of the completion of one or both of the inbound and outbound operations on the specified work day, based on the predicted work time, the predicted standby time, and the work reservation information for each type of the above-mentioned operations.

17. The information processing system as described in claim 16, characterized in that: An output device is provided that can visually display the predicted end time of the specified workday at the current moment.

18. A warehouse management method, the warehouse comprising a warehouse control device, a conveying device, and a terminal, wherein the warehouse control device predicts the time for operations, wherein, The warehouse control device has a processor and a memory. The conveying device is capable of conveying a storage unit for storing goods according to conveying instructions from the warehouse control device. The terminal is connected to the warehouse control device to send and receive work station information. In the work station, one or more operations related to the storage unit's inbound and outbound operations are performed. The warehouse management method is characterized by including: In the storage step, the warehouse control device obtains information about the actual result of the operation from the operation information of at least one of the inbound and outbound operations of the workstation from the terminal, and stores it as log information in the storage unit; In the actual result data generation step, the warehouse control device generates multiple actual result data points, each containing pre-set information about the operation time for each of the above-mentioned operation types, for each of multiple periods of different lengths. The warehouse control device uses these multiple actual result data to calculate the predicted operation time of the corresponding operation and control steps.

19. A warehouse control device having a processor and memory for predicting the time of operations, characterized in that, have: The storage unit acquires information about the actual results of at least one of the work information (inbound and outbound) of the work station from a terminal that transmits and receives work information at the work station, and stores this information as log information. This is done while at the work station, one or more operations are performed regarding the inbound and outbound of items in the storage unit used for storing items. The control unit, based on the log information, generates multiple actual result data, each with a pre-set duration of different lengths for each of the above-mentioned types of tasks, and calculates the predicted task time for the corresponding task based on the current multiple actual result data.

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