Method for estimating the performance of an automated warehouse, device and program for estimating the performance of an automated warehouse

Abstract

To simplify the estimation of automated warehouse performance. [Solution] The method for estimating the performance of an automated warehouse involves acquiring customer information including the required specifications and floor plan of the automated warehouse, generating a calculation map for calculating the performance of the automated warehouse based on the customer information, calculating the processing time for orders to be handled by the automated warehouse using the customer information and the calculation map, calculating the number of orders that the automated warehouse can process per predetermined time based on the calculation results, and outputting the calculated number of orders that the automated warehouse can process per predetermined time.

Description

Technical Field

[0001] The present disclosure relates to a method for estimating the performance of an automated warehouse, an apparatus for estimating the performance of an automated warehouse, and a program.

Background Art

[0002] Patent Document 1 discloses a warehouse management system provided with a host controller for managing the operation of a warehouse and a warehouse design device for designing the layout of the warehouse and the operation line of incoming and outgoing operations. The warehouse design device simulates work instructions or incoming and outgoing instructions based on the designed layout and operation line, and when a satisfactory result is obtained, it sends the designed layout and operation line to the host controller.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When designing an automated warehouse or making a decision such as changing the layout of an automated warehouse, the decision may be made based on the performance of the automated warehouse calculated by simulation of the automated warehouse or past work results. However, various information is required for the simulation of an automated warehouse, and it may be difficult for the user making the decision to prepare such information. In addition, some users may not have past work results.

[0005] The present disclosure has been devised in view of the above-described conventional situation, and an object thereof is to simplify the estimation of the performance of an automated warehouse.

Means for Solving the Problems

[0006] This disclosure provides a method for estimating the performance of an automated warehouse, which includes acquiring customer information including the requirements specifications and floor plans of the automated warehouse, generating a calculation map for calculating the performance of the automated warehouse based on the customer information, calculating the processing time for orders to be processed by the automated warehouse using the customer information and the calculation map, calculating the number of orders that the automated warehouse can process per predetermined time based on the calculation results, and outputting the calculated number of orders that the automated warehouse can process per predetermined time.

[0007] Furthermore, this disclosure provides an automated warehouse performance estimation device comprising a processor and a memory, wherein the processor, in cooperation with the memory, acquires customer information including the requirements specifications and floor plans of the automated warehouse, generates a calculation map for calculating the performance of the automated warehouse based on the customer information, calculates the processing time for orders to be processed by the automated warehouse using the customer information and the calculation map, calculates the number of orders that the automated warehouse can process per predetermined time based on the calculation results, and outputs the calculated number of orders that can be processed per predetermined time.

[0008] Furthermore, this disclosure provides a program that causes a computing device to acquire customer information including the requirements specifications and floor plans of an automated warehouse, generates a calculation map for calculating the performance of the automated warehouse based on the customer information, calculates the processing time for orders to be processed by the automated warehouse using the customer information and the calculation map, calculates the number of orders that can be processed per predetermined time in the automated warehouse based on the calculation results, and outputs the calculated number of orders that can be processed per predetermined time.

[0009] Furthermore, any combination of the above components, as well as any conversion of the expressions of this disclosure between methods, apparatus, systems, storage media, computer programs, etc., are also valid as aspects of this disclosure. [Effects of the Invention]

[0010] According to this disclosure, it is possible to simplify the estimation of the performance of an automated warehouse. [Brief explanation of the drawing]

[0011] [Figure 1] Block diagram showing an example configuration of the estimation device according to Embodiment 1. [Figure 2] Flowchart showing the performance estimation process of an automated warehouse using the estimation device according to Embodiment 1. [Figure 3] Schematic diagram illustrating information for performance estimation processing of the automated warehouse according to Embodiment 1. [Figure 4] Schematic diagram illustrating the determination of the warehouse design of the automated warehouse according to Embodiment 1. [Figure 5] Schematic diagram illustrating the data structure of the calculation map according to Embodiment 1 [Figure 6] A flowchart showing a first example of the calculation process for warehouse performance using the estimation device according to Embodiment 1. [Figure 7] Schematic diagram illustrating the layout setting for calculating warehouse performance according to Embodiment 1. [Figure 8] Table diagram illustrating the order list for calculating warehouse performance according to Embodiment 1 [Figure 9] Table diagram illustrating the inventory bin list for calculating warehouse performance according to Embodiment 1. [Figure 10] Schematic diagram illustrating the tasks that constitute the order for calculating warehouse performance according to Embodiment 1. [Figure 11] Schematic diagram illustrating the task assignment to conveying equipment for calculating warehouse performance according to Embodiment 1. [Figure 12] Schematic diagram illustrating the processing results of all orders for calculating warehouse performance according to Embodiment 1. [Figure 13] A flowchart showing a second example of the calculation process for warehouse performance using the estimation device according to Embodiment 1. [Figure 14] A graph showing an example of the output of the estimation device according to Embodiment 1. [Figure 15]Table diagram showing an output example of the estimation device according to Embodiment 1

Mode for Carrying Out the Invention

[0012] Hereinafter, embodiments will be described in detail with reference to the drawings as appropriate. However, a more detailed description than necessary may be omitted. For example, a detailed description of well-known matters and redundant descriptions of substantially the same configurations may be omitted. This is to avoid making the following description unnecessarily redundant and to facilitate understanding by those skilled in the art. Note that the accompanying drawings and the following description are provided to enable those skilled in the art to fully understand the present disclosure, and are not intended to limit the subject matter described in the claims.

[0013] (Embodiment 1) [Device Configuration] FIG. 1 is a block diagram showing a configuration example of an estimation device 10 according to Embodiment 1. The estimation device 10 is a device that estimates the performance of an automated warehouse. The estimation device 10 may be a general-purpose computer device such as a Personal Computer (hereinafter referred to as "PC") or a server computer, or may be a mobile terminal such as a tablet terminal or a smartphone. Hereinafter, the estimation device 10 may be referred to as an arithmetic device. Note that the configuration shown in FIG. 1 is an example, and one part may be divided into a plurality of parts or a plurality of parts may be combined into one part.

[0014] The estimation device 10 includes a processor 11, a memory 12, a communication device 13, an input device 14, an external interface device 15, and a display device 16. Each part is configured to be communicable via an internal interface device 17.

[0015] The processor 11 may be configured using, for example, a Central Processing Unit (hereinafter referred to as "CPU"), a Graphical Processing Unit (hereinafter referred to as "GPU"), a Micro Processing Unit (hereinafter referred to as "MPU"), a Digital Signal Processor (hereinafter referred to as "DSE"), or a Field Programmable Gate Array (hereinafter referred to as "FPGA"). The processor 11 realizes various functions by reading and executing various data and programs held in the memory 12.

[0016] Memory 12 is a storage unit for storing various data and programs. Memory 12 may consist of volatile / non-volatile storage devices such as Random Access Memory (hereinafter referred to as "RAM"), Read Only Memory (hereinafter referred to as "ROM"), and Hard Disk Drive (hereinafter referred to as "HDD").

[0017] The communication device 13 is an interface for communicating with external devices via a network. The communication standards supported by the communication device 13 are not particularly limited and may support either wired or wireless communication standards. Furthermore, the communication device 13 may support multiple communication standards. Therefore, the network used by the communication device 13 may be composed of a combination of networks using multiple communication standards.

[0018] The input device 14 receives operations and instructions from a user, for example, who is selling goods in an automated warehouse. The input device 14 may consist of a mouse, keyboard, touch panel display, etc.

[0019] The external interface device 15 is an interface for sending and receiving data with an external device.

[0020] The display device 16 displays various user interfaces to the user. The display device 16 may consist of a liquid crystal display, a touch panel display, or the like.

[0021] For example, a user that sells automated warehouses can provide customers who are considering introducing or redesigning an automated warehouse with an estimated performance result from the estimation device 10. The user can, for example, interview customers about their requirements for the automated warehouse and use the information gathered to estimate the performance of the automated warehouse using the estimation device 10. The overall processing of the estimation device 10 will be explained with reference to Figure 2.

[0022] [flowchart] Figure 2 is a flowchart illustrating the performance estimation process of an automated warehouse by the estimation device 10 according to Embodiment 1. Each process in the flowchart shown in Figure 2 is executed by the processor 11 of the estimation device 10. It is assumed that at the start of the flowchart shown in Figure 2, the user has already heard the customer's requirements regarding the automated warehouse.

[0023] The processor 11 acquires customer information based on user operations (step St100). Customer information includes the requirements specifications and floor plans of the automated warehouse, which the user has gathered from the customer. Customer information also includes information used to estimate the performance of the automated warehouse. Customer information also includes the time required for work at the station. Specific examples of customer information will be described later with reference to Figure 3, but customer information includes at least the floor plan of the automated warehouse.

[0024] The processor 11 generates a calculation map to estimate the performance of the automated warehouse based on the customer information obtained in step St100 (step St101). Details of the calculation map will be described later with reference to Figures 4 and 5.

[0025] Processor 11 acquires information about the automated warehouse that is not included in the customer information, i.e., missing information, in order to estimate the performance of the automated warehouse (step St102). Processor 11 may acquire missing information based on user operations, or it may acquire it by virtually setting the missing information. Information not included in the customer information is, for example, information that the customer was not able to prepare at the time of the hearing regarding the requirements for the automated warehouse, or information that is difficult for the customer to prepare. Therefore, the information acquired in step St102 may be acquired in step St100 as customer information. Also, if the customer information acquired in step St100 contains sufficient information for estimating the performance of the automated warehouse, the processing in step St102 may be omitted. Specific examples of missing information will be described later with reference to Figure 3. Hereinafter, missing information may be referred to as detailed information.

[0026] The processor 11 sets calculation conditions for estimating the performance of the automated warehouse based on the customer information obtained in step St100 (step St103). The processor 11 may also set calculation conditions based on the detailed information obtained in step St102. Furthermore, the processor 11 may set the calculation conditions virtually. This is because the calculation conditions necessary for estimating the performance of the automated warehouse may not be set based on customer information or detailed information due to a lack of such information. Specific examples of calculation conditions will be described later with reference to Figure 3, but the calculation conditions include at least the number of stations in the automated warehouse and parameters related to the performance of the conveying equipment.

[0027] The conveying equipment is equipment that transports bins in which goods are stored. Examples of conveying equipment include Automatic Guided Vehicles (hereinafter referred to as "AGVs") and Autonomous Mobile Robots (hereinafter referred to as "AMRs"). In this specification, "goods" include merchandise and packages, and these terms may be used interchangeably. In this specification, bins in which goods are stored are distinguished into "inventory bins" for storing inventory, "empty bins" that do not store goods, and "outbound bins" for storing goods to be issued. When not specifically distinguished, they are simply referred to as "bins". At the station, a worker or work robot performs the task of moving goods to be issued from inventory bins to outbound bins, in other words, the picking operation. In the following description, "worker" means a worker or work robot.

[0028] The parameters of the conveying equipment include the maximum speed of the equipment, the acceleration of the equipment, the time required for the equipment to change direction, and the handling changeover time required for the equipment to lift and lower the bottles.

[0029] Processor 11 calculates the warehouse performance of the automated warehouse (step St104). Details of the warehouse performance calculation process will be described later with reference to Figures 6 to 13. Processor 11 may calculate the warehouse performance using the customer information obtained in step St100, the calculation map generated in step St101, the detailed information obtained in step St102, and the calculation conditions set in step St103. In this specification, the warehouse performance of the automated warehouse means the number of orders that the automated warehouse can process per predetermined time. An order means an operation such as the dispatch or receipt of goods in the automated warehouse. For the sake of simplicity, it will be assumed below that an order means a dispatch operation.

[0030] Here, the more information or conditions used in the calculation, the more detailed the calculation result of the warehouse performance can be. On the other hand, if there is less information or conditions used in the calculation, the calculation result of the warehouse performance can be simplified. For example, if the specifications for the automated warehouse to be introduced are vague, customer information may be scarce. If a user uses information obtained from the customer to have the estimation device 10 calculate the warehouse performance of the automated warehouse, the calculation result may be simplified. This is because the customer information used in the calculation is scarce. However, if the customer or user who has reviewed the calculation result adds more information or conditions and has the estimation device 10 calculate the warehouse performance, the calculation result may be more detailed than the previous calculation result.

[0031] The number of orders that an automated warehouse can process per unit of time is equal to the product of the number of orders that a station can process per unit of time and the number of stations.

[0032] Processor 11 calculates the number of conveying devices required for the automated warehouse (step St105). Using the calculation map generated in step St101 and the conveying device parameters set in step St103, Processor 11 can calculate the time it takes for the conveying devices to transport bins. From this calculation result, Processor 11 can calculate the number of bins that the conveying devices can transport per predetermined time. Based on the calculated number of bins that the conveying devices can transport per predetermined time and the warehouse performance calculated in step St104, that is, the number of orders that a station can process per predetermined time and the number of stations, Processor 11 can calculate the number of conveying devices required for the automated warehouse. A specific example will be described later with reference to Figure 14.

[0033] Processor 11 calculates the estimated cost of the automated warehouse (step St106). Processor 11 may calculate the estimated cost of the automated warehouse based on the size of the automated warehouse, the number of conveying equipment required for the automated warehouse calculated in step St105, the number of stations in the automated warehouse, etc.

[0034] In this specification, the calculation of warehouse performance, the required number of conveying equipment units, or estimated costs will be collectively referred to as the "approximation of automated warehouse performance." Furthermore, the information including at least one of the warehouse performance, the required number of conveying equipment units, and estimated costs calculated by processor 11 in steps St104, St105, and St106 will be referred to as the "result of the approximation of automated warehouse performance." Note that processor 11 may also calculate items other than warehouse performance, the required number of conveying equipment units, and estimated costs as part of the approximation of automated warehouse performance. Specific examples will be described later with reference to Figure 15.

[0035] The processor 11 organizes and outputs various calculation results, in other words, approximate results (step St107). The processor 11 may output the approximate results to, for example, the display device 16, or to an external device not shown. Specific examples of the output approximate results will be described later with reference to Figures 14 and 15.

[0036] Processor 11 determines whether the estimated result output in step St107 is acceptable (step St108). For example, processor 11 may determine whether the estimated result is acceptable based on user input. For example, if a customer who has viewed the estimated result output on the display device 16 is negative about the estimated result, the user may input via the input device 14 that there is a problem with the estimated result. Alternatively, processor 11 may determine whether there is a problem with the estimated result based on customer information obtained in step St100, for example. For example, if processor 11 has obtained customer budget information in step St100 and determines that the estimated cost included in the estimated result exceeds the budget, it may further determine that there is a problem with the estimated result. In this case, processor 11 may also calculate how much the estimated cost exceeds the budget and output the calculation result.

[0037] If processor 11 determines that the estimated result is acceptable (step St108: YES), it terminates this processing flow.

[0038] If the processor 11 determines that there is a problem with the estimated result (step St108: NO), it adjusts or changes various information or conditions (step St109). For example, the processor 11 may display a user interface on the display device 16 for inputting new information related to the automated warehouse, such as the requirements specifications for the automated warehouse, or for changing customer information, detailed information, or calculation conditions that have already been acquired or set. Then, based on the user operation, it may adjust or change various information or conditions. After processing in step St109, the processor 11 returns to step St100, step St101, step St102, or step St103 and repeats the process. For example, if there is a change in customer information, the processor 11 may return to step St100. Or, for example, if there is a change in the floor plan, the processor 11 may return to step St101. Or, for example, if there is additional input of detailed information, the processor 11 may return to step St102. Or, for example, if there is a change in calculation conditions, the processor 11 may return to step St103. The processor 11 then estimates the performance of the automated warehouse based on the adjusted or modified information or conditions.

[0039] For example, even if a customer's requirements for an automated warehouse are vague, the process shown in Figure 2 can be repeated to obtain an estimated result that the customer can accept, as well as a rough specification of the automated warehouse at that time.

[0040] [Customer information and output information] Figure 3 is a schematic diagram illustrating information for the performance estimation process of the automated warehouse according to Embodiment 1.

[0041] As shown in Figure 3, customer information includes at least a floor plan, and may also include information such as warehouse performance, budget, and warehouse type. If the customer information includes warehouse performance, this is the warehouse performance required by the customer and differs from the warehouse performance calculated by the estimation device 10. In this case, for example, in step St108 of the flowchart shown in Figure 2, the warehouse performance required by the customer and the estimated warehouse performance can be compared. The floor plan included in the customer information is used to generate the calculation map.

[0042] Missing information, or in other words, detailed information, may include, for example, order lists and inventory bin lists. An order list is a list that summarizes orders and the items required for those orders. An inventory bin list is a list that summarizes inventory bins and the items stored in those inventory bins.

[0043] Calculation conditions may include, for example, the number of stations, parameters related to the performance of conveying equipment, parameters related to the performance of elevators, bin arrangement, and route information for conveying equipment. Elevator performance may include, for example, the elevator's speed, size, and the travel time between floors (in other words, between floors of the automated warehouse).

[0044] Detailed information may be acquired by the estimation device 10 as customer information. Similarly, calculation conditions may be acquired and set in the estimation device 10 as customer information, or as detailed information. For example, if a customer has already implemented an automated warehouse and is considering layout changes, etc., and it is easy for the customer to prepare various information, then various information and conditions may be provided to the user as customer information.

[0045] The estimation device 10 uses customer information, detailed information, calculation conditions, and a calculation map to estimate the performance of the automated warehouse. The estimated results output by the estimation device 10 may include, for example, warehouse performance, number of conveying equipment, estimated costs, utilization rate of conveying equipment, and station utilization rate.

[0046] [Calculation Map] Next, the data structure of the calculation map will be explained with reference to Figures 4 and 5. Figure 4 is a schematic diagram illustrating the determination of the warehouse design of the automated warehouse according to Embodiment 1. The warehouse design refers to the areas within the automated warehouse that cannot be changed.

[0047] The user receives a floor plan 20 of the automated warehouse from the customer. The floor plan 20 shows the rooms of the automated warehouse. Alternatively, the user may receive a drawing of the entire property owned by the customer. In this case, the user may input the floor plan showing the rooms of the automated warehouse from the overall drawing into the estimation device 10.

[0048] The processor 11 of the estimation device 10 extracts the area 21 that can be treated as an automated warehouse from the rooms of the automated warehouse shown in the floor plan 20. Area 21 does not include, for example, doors, pillars, etc., of the automated warehouse entrance. From area 21, the processor 11 extracts the plate area 22, which is the area where plates will be laid. The plates divide the floor area of ​​the automated warehouse. The size of the plates is not particularly limited. The processor 11 sets multiple plate centers (for example, plate centers 23) for the plate area 22. The processor 11 sets plates (for example, plate 24) so ​​that the center position of the plates coincides with the set plate centers. This determines the number and position of the plates.

[0049] As explained with reference to Figure 4, the processor 11 of the estimation device 10 determines the number and location of plates in the automated warehouse based on the floor plan. Subsequently, the processor 11 generates a calculation map. The data structure of the calculation map will be explained with reference to Figure 5. Figure 5 is a schematic diagram illustrating the data structure of the calculation map M1 according to Embodiment 1.

[0050] The calculation map M1 is composed of overlapping layers. Specifically, the calculation map M1 is composed of overlapping layers L0, L1, L2, and L3. The settings for each layer are performed by the processor 11 of the estimation device 10. Layer L0 is the layer for defining the area of ​​the automated warehouse in the calculation map M1. The area of ​​the automated warehouse in the calculation map M1 is defined based on the number and position of plates determined based on the floor plan. In the following description, it is assumed that the area of ​​the automated warehouse defined in layer L0 is divided into a mesh.

[0051] Layer L1 is a layer for setting the possible placement locations of objects in an automated warehouse. For example, Layer L1 sets the possible placement locations in the automated warehouse for objects with fixed positions, such as shelves, elevators, and garages where transport equipment is stored and waiting. Layer L1 also sets the possible placement locations in the automated warehouse for objects whose positions may change, such as transport equipment and bins. In Layer L1, for example, the possible placement location of one shelf may be set for one mesh, or the possible placement locations of one garage may be set for multiple meshes.

[0052] Layer L2 is the layer for configuring connections between nodes. A node refers to a specific location on the computation map M1, and each node corresponds to each mesh that demarcates the area of ​​the automated warehouse as defined in Layer L0. Nodes can connect to adjacent nodes to set the paths of transport equipment. In other words, Layer L2 is the layer for setting the paths of transport equipment. The connections between nodes indicate the possibility of movement between nodes. For example, consider nodes adjacent to each other horizontally. These adjacent nodes may have a path set such that, for example, transport equipment can travel from the left node to the right node, but not from the right node to the left node. Note that this is not the only example of setting a travel path in the horizontal direction using adjacent nodes. Also, adjacent nodes vertically can set a travel path for transport equipment vertically. Examples of travel path settings may include dead ends, impassable paths, etc.

[0053] Furthermore, routing may be performed not only based on connections between adjacent nodes, but also based on connections between nodes that span one or more nodes.

[0054] Furthermore, the calculation of bin transport paths based on the connections between nodes may utilize known techniques for solving the all-point-to-shortest-path problem.

[0055] Layer L3 is a layer for setting the initial positions of objects whose positions may change, such as conveying equipment or bins. For example, in Layer L3, the initial position of conveying equipment is set to one of the possible placement locations for conveying equipment set in Layer L1. Similarly, in Layer L3, the initial position of bins is set to one of the possible placement locations for bins set in Layer L1.

[0056] The computational map M1 is formed by the overlapping of these layers L0, L1, L2, and L3. Note that the computational map M1 shown in Figure 5 is just one example, and the computational map is not limited to this.

[0057] Furthermore, each of the layers L1, L2, and L3 that constitute the calculation map may be set based on customer information, or the processor 11 may set them automatically. For example, if the customer has specified the possible shelf placement locations in advance, layer L1 may be set based on the possible shelf placement locations obtained from the customer. Alternatively, for example, if the customer has not specified the possible shelf placement locations in advance, the processor 11 may determine the possible shelf placement locations and set layer L1 accordingly.

[0058] [Detailed calculation example of warehouse performance] Next, referring to Figures 6 to 12, we will explain the first example of the warehouse performance calculation process in step St104 of the flowchart shown in Figure 2. In the explanation of the first example, it is assumed that the estimation device 10 has obtained the order list and the inventory bin list as detailed information. This is because the first example assumes a stage where negotiations between the user and the customer have progressed, and the user has obtained more detailed information from the customer. In the first example, more accurate calculations are possible using detailed information. On the other hand, referring to Figure 13, in the second example of the warehouse performance calculation process described later, the estimation device 10 has not obtained the order list and the inventory bin list. This is because the second example described later assumes a stage where the user proposes an automated warehouse to the customer, that is, a stage where the customer's image of the automated warehouse requirements is vague. In the second example described later, the estimation device 10 performs a simplified calculation by using approximate values ​​such as average values ​​instead of detailed data. Therefore, the first example is an example that calculates warehouse performance in more detail compared to the second example described later.

[0059] Figure 6 is a flowchart showing a first example of the calculation process for warehouse performance by the estimation device according to Embodiment 1. Each process in the flowchart shown in Figure 6 is executed by the processor 11 of the estimation device 10.

[0060] First, the processor 11 sets the layout of the automated warehouse using a calculation map (step St200). The layout setting will be explained with reference to Figure 7. Figure 7 is a schematic diagram illustrating the layout setting for calculating warehouse performance according to Embodiment 1.

[0061] Processor 11 uses the calculation map M2 to calculate warehouse performance as shown in the flowchart of Figure 6. Processor 11 uses the calculation map M2 to set the layout of Garage G1, Garage G2, Station ST, Empty Bin Area OE, Outbound Area SE, and Backyard BY in the automated warehouse. One transport device is stationed in each of Garage G1 and Garage G2. A worker is stationed at Station ST. Empty bins are stored in Empty Bin Area OE. Outbound Area SE is the area where outbound bins are processed, and outbound bins are transported to Outbound Area SE. Backyard BY stores numerous inventory bins Z, including inventory bins Z1, Z2, and Z3. For simplicity of explanation, it is assumed that there are two transport devices in the automated warehouse, and that there is one station, one empty bin area, and one outbound area.

[0062] Returning to the flowchart in Figure 6, the processor 11 sets the speed of the conveying equipment and the worker's processing speed (step St201). Here, the processor 11 may set the speed of the conveying equipment and the worker's processing speed based on information obtained from the customer (e.g., customer information, detailed information, or calculation conditions). The worker's processing speed is the speed at which the worker performs the picking work. The processor may also use the calculation map M1 to determine the path of the conveying equipment and calculate the time it takes for the conveying equipment to move based on that path (straight, corners, etc.).

[0063] Processor 11 sets the order list (step St202). An example of an order list is shown in Figure 8. Figure 8 is a table diagram illustrating the order list for calculating warehouse performance according to Embodiment 1.

[0064] The order number is a number used to identify an order, and the item number is a number used to identify an item. For example, an order with order number 1 instructs the dispatch of 2 items of item number 1, 3 items of item number 2, 1 item of item number 3, ... 3 items of item number 100.

[0065] Returning to the flowchart in Figure 6, the processor 11 sets the inventory bin list (step St203). An example of the inventory bin list is shown in Figure 9. Figure 9 is a table diagram illustrating the inventory bin list for calculating warehouse performance according to Embodiment 1.

[0066] The inventory bin number is a number used to identify an inventory bin. For example, inventory bin number 1 stores 30 items with item number 1.

[0067] Returning to the flowchart in Figure 6, the processor 11 sets the order and inventory bin combinations based on the order list set in step St202 and the inventory bin list set in step St203 (step St204). The processor 11 optimizes the inventory bin combinations necessary to process the orders. Specific examples of optimization include minimizing the number of inventory bins used and minimizing the travel distance of the conveying equipment by combining orders and inventory bins.

[0068] Returning to the flowchart in Figure 6, the processor 11 assigns tasks to the conveying equipment based on the combination of order and inventory bin set in step St204 (step St205). The tasks will be described with reference to Figure 10. Figure 10 is a schematic diagram illustrating the tasks that constitute the order for calculating warehouse performance according to Embodiment 1.

[0069] Figure 10 illustrates the case where, in step St204, a combination of an order and inventory bins Z1, Z2, and Z3 is set. In this case, the order consists of task OS, task BR1, task PK1, task RT1, task BR2, task PK2, task RT2, task BR3, task PK3, task RT3, and task SP.

[0070] Task OS is a task that sets one of the empty bottles stored in the empty bottle area OE as the outgoing bottle and instructs the transport equipment to transport that outgoing bottle from the empty bottle area OE to station ST.

[0071] Task BR1 is a task to have the conveying equipment transport inventory bin Z1 from backyard BY to station ST.

[0072] Task PK1 is a task in which the worker is instructed to transfer items to be issued from inventory bin Z1 to the issue bin, in other words, to perform a picking operation.

[0073] Task RT1 is the task of having the transport equipment return the stock bin Z1, where the picking process has been completed, to the backyard BY.

[0074] Tasks BR1, PK1, and RT1 are tasks for inventory bin Z1, tasks BR2, PK2, and RT2 are tasks for inventory bin Z2, and tasks BR3, PK3, and RT3 are tasks for inventory bin Z3. Since tasks BR2, PK2, RT2, BR3, PK3, and RT3 overlap with the explanations for tasks BR1, PK1, and RT1, differing only in the target inventory bin, their explanations are omitted.

[0075] Task SP is a task that instructs a transport device to transport the bins containing the completed items for tasks PK1, PK2, and PK3 from station ST to the bin area SE. As a result, the bins containing all the items to be shipped according to the order are released from the automated warehouse.

[0076] Thus, each task constituting an order is performed by a transport device or a worker. Here, it is assumed that there is one transport device waiting in each of Garage G1 and Garage G2. Referring to Figure 11, an example of assigning tasks other than picking to the two transport devices will be explained. Figure 11 is a schematic diagram illustrating the assignment of tasks to transport devices for calculating warehouse performance according to Embodiment 1.

[0077] The processor 11 may, for example, assign each task constituting an order to each transport device in such a way that the processing time for the order is minimized. For illustrative purposes, the two transport devices will be referred to as transport device a and transport device b. For example, the initial position of transport device a may be garage G1, and the initial position of transport device b may be garage G2. In the example in Figure 11, tasks OS, BR3, RT1, RT2, and SP are assigned to transport device a, and tasks BR1, BR2, and RT3 are assigned to transport device b. Tasks PK1 and PK2 are performed on the inventory bins that transport device b has delivered to station ST, and task PK3 is performed on the inventory bins that transport device a has delivered to station ST. The movements of transport devices a and b in this manner will be described in chronological order.

[0078] First, transporter a starts task BR3, and transporter b starts task BR1. This transports inventory bins Z3 and Z1 from backyard BY to station ST. Transporter a completes task BR3 before transporter b completes task BR1, and starts task OS. This transports empty bins in empty bin area OE to station ST as outbound bins.

[0079] At station ST, tasks PK1 and PK3 are started. This moves the items to be issued from stock bins Z1 and Z3, respectively, to the issuing bins. Task PK1 is completed before task PK3 is completed. While task PK3 is running, transporter b starts task BR2. This transports stock bin Z2 from backyard BY to station ST. Once task PK3 is complete, transporter a starts task RT1. This returns stock bin Z1 from station ST to backyard BY. While task RT1 is running, transporter b completes task BR2. Then, task PK2 is started at station ST.

[0080] When task PK2 is completed, that is, when all items to be shipped have been moved from the stock bin to the shipping bin, conveyor a starts task SP and conveyor b starts task RT3. This transports the shipping bin from station ST to shipping area SE, and the shipping bin is shipped out of the automated warehouse. Also, stock bin Z3 is returned from station ST to backyard BY. After completing task SP, conveyor a starts task RT2. This returns stock bin Z2 from station ST to backyard BY. The processor may assign each task to two conveyors so that orders consisting of the tasks shown in Figure 10 are processed in this manner.

[0081] Returning to the explanation of Figure 6, the processor 11 calculates the total processing time for all orders (step St206). Referring to Figure 12, the total processing time when orders are executed continuously will be explained. Figure 12 is a schematic diagram illustrating the processing results for all orders for calculating warehouse performance according to Embodiment 1.

[0082] The assignment of multiple tasks to a conveying device, as explained with reference to Figure 11, pertains to a single order composed of those multiple tasks. In an actual automated warehouse, there are often multiple orders to be processed; for example, as shown in Figure 8, there may be cases where 100 orders need to be processed. Figure 12 shows the processing result when 10 orders are executed consecutively. In the example in Figure 12, similar to the example in Figure 11, the tasks constituting each of the 10 orders are assigned to two conveying devices. Furthermore, Figure 12 shows the processing result when 100 orders are executed consecutively. The time taken from the start to the completion of the 100 orders is the total processing time for all orders. Note that the numbers showing seconds in Figure 12 are merely illustrative examples.

[0083] Returning to the explanation of Figure 6, the processor 11 calculates the warehouse performance (step St207). Warehouse performance is obtained by dividing the total number of orders by the total processing time calculated in step St206. This allows the processor 11 to calculate the number of orders that the automated warehouse can process per predetermined time. Then, the processor 11 terminates this processing flow.

[0084] In this way, the estimation device 10 can calculate warehouse performance with high accuracy by using detailed information such as an order list or an inventory bin list to optimize the combination of orders and inventory bins, or the assignment of orders to transport equipment. In the first example explained with reference to Figures 6 to 12, the detailed information was given as an order list and an inventory bin list, but the estimation device 10 may also calculate warehouse performance based on detailed information different from the order list and inventory bin list obtained by the user from the customer. The detailed information different from the order list and inventory bin list may be, for example, the detailed waiting locations of transport equipment in an automated warehouse, or the detailed placement locations of inventory bins in an automated warehouse. In other words, the first example explained with reference to Figures 6 to 12 is merely one example of the warehouse performance calculation process in step St104 of the flowchart in Figure 2. Depending on the information acquired, the estimation device 10 can calculate warehouse performance in more detail than in the first example, or in a simplified manner as in the second example described later.

[0085] The first example of the calculation process for warehouse performance in step St104 of the flowchart shown in Figure 2 has been explained with reference to Figures 6 to 12. In the explanation with reference to Figures 6 to 12, it was assumed that there are two transport machines, one station, one empty bin area, and one outbound area, and two garages. These are examples to simplify the calculation, and for example, there may be more than two stations. If there is one station in the automated warehouse, the number of orders that the station can process per predetermined time is the number of orders that the automated warehouse can process per predetermined time, i.e., the warehouse performance. In the first example explained with reference to Figures 6 to 12, the total processing time shown in Figure 12 is the total processing time for all orders at one station ST, so by dividing the total number of orders by this total processing time, the number of orders that station ST can process per predetermined time can be obtained. In the first example explained with reference to Figures 6 to 12, this calculation result is the number of orders that the automated warehouse can process per predetermined time, i.e., the warehouse performance.

[0086] For example, if there are two stations, the warehouse performance can be determined by calculating the number of orders that each of the two stations can process per predetermined time, and then summing the number of orders that each station can process per predetermined time.

[0087] [Simplified calculation example of warehouse performance] Next, referring to Figure 13, a second example of the warehouse performance calculation process in step St104 of the flowchart shown in Figure 2 will be explained. In the explanation of the second example, unlike the first example, it is assumed that the estimation device 10 has not obtained the order list and the inventory bin list. This is because the second example assumes a stage in which the user proposes an automated warehouse to the customer, and it is assumed that at this stage the customer's image of the requirements specifications for the automated warehouse is vague. In the second example, the estimation device calculates the warehouse performance more simply than in the first example described above, using approximate values ​​such as average values ​​instead of detailed data.

[0088] Figure 13 is a flowchart showing a second example of the warehouse performance calculation process by the estimation device 10 according to Embodiment 1. Each process in the flowchart shown in Figure 13 is executed by the processor 11 of the estimation device 10.

[0089] First, the processor 11 sets the layout of the automated warehouse using a calculation map (step St300). For an explanation of the automated warehouse layout, see Figure 7. The processor 11 uses the calculation map M2 to set the placement of garages G1, G2, station ST, empty bin area OE, outbound area SE, and backyard BY in the automated warehouse. The processor 11 further sets representative points for the placement of the conveying equipment. In the first example described above, the conveying equipment was placed in both garage G1 and garage G2. However, in the second example, to simplify calculations, the conveying equipment is placed at the set representative points. For example, the processor 11 sets garage G2 as the representative point for the placement of the conveying equipment. The processor 11 may set the representative points for the placement of the conveying equipment based, for example, on user operation.

[0090] The processor 11 sets the speed of the conveying equipment and the worker's processing time (step St301). The worker's processing time is the time required for the worker to pick the items. The processor 11 may set the speed of the conveying equipment and the worker's processing time based, for example, on user operation. Alternatively, if the average value of the conveying equipment speed is stored in memory 12, the processor 11 may use that average value. Similarly, if the average value of the worker's processing time is stored in memory 12, the processor 11 may use that average value.

[0091] Processor 11 calculates the average travel distance of the conveying equipment (step St302). The average travel distance is the sum of the travel distance for transporting empty bottles, twice the average travel distance for transporting stock bottles, and the travel distance for transporting out-of-stock bottles.

[0092] The distance traveled for transporting empty bottles is the sum of the distance from the representative point to the empty bottle area OE, the distance from the empty bottle area OE to station ST, and the distance from station ST to the representative point.

[0093] The total distance traveled for transporting inventory bins is the sum of the distance from the representative point to the location of the inventory bin, the distance from the location of the inventory bin to station ST, and the distance from station ST to the representative point. The average distance traveled for transporting inventory bins is calculated by determining the distance traveled for each inventory bin, summing these distances, and dividing the sum by the number of inventory bins. The reason for doubling the average distance traveled for transporting inventory bins when calculating the average distance traveled by the conveying equipment is to take into account both the distance traveled when transporting inventory bins from backyard BY to station ST and the distance traveled when returning inventory bins from station ST to backyard BY.

[0094] The total distance traveled for transporting the bins out of storage is the sum of the distance from the representative point to station ST, the distance from station ST to the out-of-store area SE, and the distance from the out-of-store area SE to the representative point.

[0095] Processor 11 sets an average number of inventory bins per order (step St303). If Processor 11 has obtained the order list and the inventory bin list, it can calculate, for example, that one order requires 3 inventory bins and another order requires 2 inventory bins. However, if Processor 11 has not obtained the order list and the inventory bin list, it sets an average number of inventory bins per order and uses this average to calculate warehouse performance. For example, Processor 11 may set an average of 3.5 inventory bins required per order.

[0096] Processor 11 calculates the total processing time for all orders (step St304). First, processor 11 calculates the processing time for one order. Processor 11 calculates the travel time of the conveying equipment based on the speed of the conveying equipment set in step St301 and the average travel distance of the conveying equipment calculated in step St302. Specifically, processor 11 calculates the travel time of the conveying equipment by dividing the average travel distance of the conveying equipment by the speed of the conveying equipment. Then, processor 11 adds the travel time of the conveying equipment to the processing time of the worker set in step St301. This gives the processing time for one inventory bin. Processor 11 calculates the processing time for one order by multiplying the processing time for one inventory bin by the average number of inventory bins set in step St303. Processor 11 calculates the total processing time for all orders by multiplying the processing time for one order by the total number of orders. Note that processor 11 may pre-set the total number of orders. Processor 11 may set the total number of orders, for example, based on user operations.

[0097] Processor 11 calculates warehouse performance (step St305). Warehouse performance is obtained by dividing the total number of orders by the total processing time calculated in step St304. This allows processor 11 to calculate the number of orders that the automated warehouse can process per predetermined time. Then, processor 11 terminates this processing flow.

[0098] Thus, at a stage where the user has not yet obtained detailed information such as an order list or inventory bin list from the customer, the estimation device 10 can calculate warehouse performance by setting the average number of inventory bins required for an order, a representative point for the placement of conveying equipment, etc. Even when there is little information obtained from the customer, the estimation device 10 can easily calculate warehouse performance by making several assumptions, without having to set detailed conditions (for example, setting combinations of individual orders and inventory bins).

[0099] The above describes two examples of the warehouse performance calculation process in step St104 of the flowchart shown in Figure 2, with reference to Figures 6 to 13. The estimation device 10 can calculate warehouse performance with higher accuracy or more simply depending on the amount and type of information obtained from the customer. As the negotiation stage between the user and the customer progresses, that is, as specific information can be obtained from the customer, detailed calculation results can be obtained, for example, as in the first example described with reference to Figures 6 to 12. For example, depending on the amount and type of information provided by the customer to the user, calculations may be performed at a granularity between that of the first example described with reference to Figures 6 to 12 and that of the second example described with reference to Figure 13.

[0100] [Example of estimated performance results for an automated warehouse] Next, with reference to Figures 14 and 15, an example of the estimated performance results of the automated warehouse will be explained. Figure 14 is a graph showing an example of the output of the estimation device 10 according to Embodiment 1.

[0101] The graph in Figure 14 shows the station performance and the conveying equipment performance. Station performance refers to the number of orders that a station can process per predetermined time, or in other words, the number of outbound bins that a station can process per predetermined time. Conveying equipment performance refers to the number of bins that the conveying equipment can transport per predetermined time. In the example in Figure 14, one station can process 120 outbound bins (orders) per hour. The estimation device 10 calculates warehouse performance by multiplying the station performance by the number of stations. The estimation device 10 may also output warehouse performance separately from the graph in Figure 14 (see, for example, Figure 15).

[0102] Furthermore, the estimation device 10 can calculate the number of transport equipment units required for the automated warehouse. First, the estimation device 10 determines the number of transport equipment units required for one station. The estimation device 10 takes the value obtained by dividing the station performance by the transport equipment performance as the number of transport equipment units required for one station. Here, if the value obtained by dividing the station performance by the transport equipment performance includes a decimal part, the estimation device 10 rounds up the decimal part to determine the number of transport equipment units required for one station. In the example in Figure 14, one station can process 120 outbound bins per hour, and one transport equipment unit can transport 34.3 bins per hour. In this case, the estimation device 10 rounds up the decimal part of the value obtained by dividing 120 by 34.3 (approximately 3.5) to determine that 4 transport equipment units are required for one station. The estimation device 10 can determine that 12 transport devices are needed for the automated warehouse by multiplying the number of transport devices needed for one station (4) by the number of stations (3). In this way, the estimation device 10 can calculate the number of transport devices needed to stably achieve the warehouse performance of the automated warehouse by determining the number of transport devices needed for each station.

[0103] Figure 15 is a table diagram showing an example of the output of the estimation device 10 according to Embodiment 1. As shown in Figure 15, the estimation device 10 may calculate and output items such as "storage size," "warehouse size," "bin size," "storage capacity," "number of shelves," "number of stations," "number of conveying devices," "number of chargers," "warehouse performance," "load capacity," and "power supply capacity." Among these items, there may be items that are not only calculated by the estimation device 10, but also items that directly reflect the requirements specifications for the automated warehouse obtained from the customer.

[0104] Note that the output examples shown in Figures 14 and 15 are just examples, and the estimation device 10 may output the estimation results in a format other than graph or table format, or it may output information other than the information shown in Figures 14 or 15. Furthermore, the type of information and output format output by the estimation device 10 may be set in advance by the user.

[0105] (modified version) In the above embodiment 1, an example was shown in which the estimation device 10 calculates warehouse performance at a fine or coarse level of detail depending on the number or type of information obtained from the customer. In these examples, the estimation device 10 calculates warehouse performance using a calculation map generated based on floor plans, so there is no need to perform discrete simulations that require complex condition settings. However, the estimation device 10 may also determine warehouse performance by performing discrete simulations based on customer information, detailed information, and calculation conditions. This allows the estimation device 10 to determine the warehouse performance of the automated warehouse with higher accuracy.

[0106] (Summary of the embodiments) The following technologies are disclosed based on the above description of the embodiments. Note that the components etc. in the above embodiments are examples, but are not limited to these.

[0107] (Technology 1) The method for estimating the performance of an automated warehouse involves obtaining customer information, including the requirements specifications and floor plan (e.g., floor plan 20) of the automated warehouse; generating calculation maps (e.g., calculation map M1, calculation map M2) for calculating the performance of the automated warehouse based on the customer information; calculating the processing time for orders to be handled by the automated warehouse using the customer information and calculation maps; calculating the number of orders that the automated warehouse can process per predetermined time based on the calculation results; and outputting the calculated number of orders that the automated warehouse can process per predetermined time.

[0108] This allows for the calculation of automated warehouse performance based on customer information, including floor plans. This makes it possible to estimate automated warehouse performance even when the customer has a vague understanding of the required specifications, such as when a user is proposing the introduction of an automated warehouse.

[0109] (Technology 2) The method for estimating the performance of an automated warehouse described in Technology 1 may generate a calculation map by setting, based on customer information, a layer for defining the area of ​​the automated warehouse (e.g., Layer L0), a layer for defining the fixed and placeable positions of objects in the automated warehouse (e.g., Layer L1), a layer for defining the routes of transport equipment that transport bins storing goods in the automated warehouse (e.g., Layer L2), and a layer for defining the initial positions of the transport equipment and bins in the automated warehouse (e.g., Layer L3), and combining each of these layers.

[0110] This allows the automated warehouse performance estimation method to define the area of ​​the automated warehouse in one of the multiple layers that make up the calculation map for calculating the automated warehouse's performance. Furthermore, the automated warehouse performance estimation method can define the fixed and possible placement locations of objects in one layer. It can also define the routes of the transport equipment in one layer. Finally, it can define the initial positions of the transport equipment and bins in one layer. By combining these layers, the automated warehouse performance estimation method can generate a calculation map.

[0111] (Technology 3) The method for estimating the performance of an automated warehouse described in Technology 1 or 2 may involve setting calculation conditions based on customer information, including parameters related to the number of stations for processing orders in the automated warehouse and the performance of conveying equipment for transporting bins containing goods, and then using a calculation map and calculation conditions to calculate the processing time for orders handled by the automated warehouse.

[0112] This allows the automated warehouse performance estimation method to set calculation conditions that include parameters related to the number of stations in the automated warehouse and the performance of the conveying equipment, and to calculate the processing time of orders that the automated warehouse will handle based on the set calculation conditions. For example, if a user has obtained information from a customer about the number of stations to be installed in the automated warehouse, or if the specifications such as the speed of the conveying equipment are known, they can reflect this information in the estimation of the automated warehouse's performance.

[0113] (Technology 4) The method for estimating the performance of an automated warehouse described in Technical 3 involves using a calculation map and calculation conditions to calculate the transport time of bins by the transport equipment and the processing time of orders handled by the station. Based on these calculation results, the number of bins that the transport equipment can transport per predetermined time and the number of orders that the station can process per predetermined time are calculated. Based on these calculation results and the number of stations, the required number of transport equipment units in the automated warehouse may be calculated.

[0114] This allows the automated warehouse performance estimation method to calculate the bin transport time of the conveying equipment. Furthermore, it can calculate the order processing time for each station. Additionally, based on the bin transport time of the conveying equipment, the method can calculate the number of bins that the conveying equipment can transport per predetermined time. Moreover, based on the order processing time for each station, the method can calculate the number of orders that a station can process per predetermined time. Finally, based on these calculation results and the number of stations, the method can calculate the required number of conveying equipment units for the automated warehouse.

[0115] (Technology 5) In the automated warehouse performance estimation method described in Technical 3 or 4, if the calculation conditions set based on customer information do not include specific conditions, specific conditions may be hypothetically set.

[0116] This allows the automated warehouse performance estimation method to virtually set calculation conditions when, for example, the customer information does not contain the necessary information and it is not possible to set the calculation conditions required to estimate the automated warehouse performance.

[0117] (Technology 6) In the automated warehouse performance estimation method described in Technical 4, the estimated cost of the automated warehouse may be calculated based on customer information, the number of orders that the automated warehouse can process per predetermined hour, and the required number of conveying equipment units, and the estimated cost of the automated warehouse may be output.

[0118] This allows the automated warehouse performance estimation method to calculate the estimated cost of the automated warehouse. Furthermore, the automated warehouse performance estimation method can output not only the number of orders that the automated warehouse can process per given time, but also the number of conveying devices required in the automated warehouse, as well as the estimated cost of the automated warehouse.

[0119] (Technology 7) The automated warehouse performance estimation method described in Technology 3 may involve obtaining detailed information, including a list of orders and a list of bins, and using the calculation conditions, detailed information, and calculation map to calculate the order processing time.

[0120] This allows for a more detailed calculation of warehouse performance based on order lists and inventory bin lists. For example, it becomes possible to calculate warehouse performance with greater accuracy when specific information is available from the customer.

[0121] (Technology 8) The method for estimating the performance of an automated warehouse described in Technical 7 may involve setting one or more bins required for order processing so as to minimize the travel distance of the conveying equipment, and assigning tasks that constitute an order to one or more conveying equipment so as to shorten the order processing time.

[0122] This allows the automated warehouse performance estimation method to optimize the combination of orders and inventory bins. Furthermore, it allows the automated warehouse performance estimation method to optimize how tasks constituting orders are assigned to transport equipment. This enables more accurate calculations of warehouse performance.

[0123] (Technology 9) The automated warehouse performance estimation device (e.g., estimation device 10) comprises a processor (e.g., processor 11) and memory (e.g., memory 12). The processor, in cooperation with the memory, acquires customer information including the requirements specifications and floor plans of the automated warehouse. Based on the customer information, it generates a calculation map for calculating the performance of the automated warehouse. Using the customer information and the calculation map, it calculates the processing time for orders to be handled by the automated warehouse. Based on the calculation results, it calculates the number of orders that can be processed per predetermined time in the automated warehouse and outputs the calculated number of orders that can be processed per predetermined time.

[0124] As a result, the automated warehouse performance estimation device can achieve the same effect as Technology 1.

[0125] (Technology 10) The program causes a calculation unit (e.g., an estimation unit 10) to acquire customer information including the requirements specifications and floor plans of the automated warehouse, generates a calculation map for calculating the performance of the automated warehouse based on the customer information, uses the customer information and the calculation map to calculate the processing time for orders to be handled by the automated warehouse, calculates the number of orders that can be processed per predetermined time based on the calculation results, and outputs the calculated number of orders that can be processed per predetermined time.

[0126] This allows the program to achieve the same effect as Technique 1.

[0127] While embodiments have been described above with reference to the attached drawings, this disclosure is not limited to such examples. It is clear to those skilled in the art that various modifications, alterations, substitutions, additions, deletions, and equivalents can be conceived within the scope of the claims, and these are also understood to fall within the technical scope of this disclosure. Furthermore, the components of the embodiments described above can be combined in any way without departing from the spirit of the invention. [Industrial applicability]

[0128] The technology disclosed herein is useful as a method for estimating the performance of an automated warehouse, an automated warehouse performance estimation device, and a program. [Explanation of Symbols]

[0129] 10 Estimation device 11 processors 12 memory 13. Communication equipment 14 Input devices 15 External Interface Device 16 Display device 17 Internal Interface Device 20 Floor Plans L0, L1, L2, L3 layers M1, M2 calculation map G1, G2 Garage ST Station OE Empty Bottle Area SE Departure Area BY Backyard OS, BR1, BR2, BR3, PK1, PK2, PK3, RT1, RT2, RT3, SP Task

Claims

1. We obtain customer information, including the requirements specifications and floor plans for the automated warehouse. Based on the customer information, a calculation map is generated for calculating the performance of the automated warehouse. Using the customer information and the calculation map, the processing time for orders to be processed by the automated warehouse is calculated. Based on the calculation results, the number of orders that the automated warehouse can process per predetermined time is calculated. The calculated number of orders that the automated warehouse can process per predetermined time is output. A method for estimating the performance of an automated warehouse.

2. Based on the customer information, a layer for defining the area of ​​the automated warehouse, a layer for defining the fixed position and placement position of objects in the automated warehouse, a layer for defining the route of the transport equipment that transports the bins storing articles in the automated warehouse, and a layer for defining the initial positions of the transport equipment and the bins in the automated warehouse are set. By combining each of the aforementioned layers, the calculation map is generated. A method for estimating the performance of an automated warehouse according to claim 1.

3. Based on the customer information, calculation conditions are set, including parameters related to the number of stations for processing the order in the automated warehouse and the performance of the conveying equipment for transporting the bins in which the goods are stored. Using the calculation map and the calculation conditions, the processing time for orders to be processed by the automated warehouse is calculated. A method for estimating the performance of an automated warehouse according to claim 1.

4. Using the calculation map and the calculation conditions, the transport time of the bins by the transport equipment and the processing time of the orders processed by the station are calculated. Based on the calculation results, the number of bins that the transport equipment can transport per predetermined time and the number of orders that the station can process per predetermined time are calculated. Based on the calculation result and the number of stations, the required number of conveying devices in the automated warehouse is calculated. A method for estimating the performance of an automated warehouse as described in claim 3.

5. If the calculation conditions set based on the customer information do not include a specific condition, the specific condition is set virtually. A method for estimating the performance of an automated warehouse as described in claim 3.

6. Based on the customer information, the number of orders that the automated warehouse can process per predetermined hour, and the required number of conveying devices, the estimated cost of the automated warehouse is calculated. The automated warehouse outputs the number of orders it can process per predetermined hour, the number of transport devices required, and the estimated cost of the automated warehouse. A method for estimating the performance of an automated warehouse as described in claim 4.

7. Obtain detailed information including the list of orders and the list of bins, Using the calculation conditions, the detailed information, and the calculation map, the processing time for the order is calculated. A method for estimating the performance of an automated warehouse as described in claim 3.

8. To minimize the travel distance of the transport equipment, set one or more bins required for processing the order, To shorten the processing time of the order, assign the tasks constituting the order to one or more of the transport devices. A method for estimating the performance of an automated warehouse according to claim 7.

9. Equipped with a processor and memory, The aforementioned processor, in cooperation with the memory, We obtain customer information, including the requirements specifications and floor plans for the automated warehouse. Based on the customer information, a calculation map is generated for calculating the performance of the automated warehouse. Using the customer information and the calculation map, the processing time for orders to be processed by the automated warehouse is calculated. Based on the calculation results, the number of orders that can be processed per predetermined time in the automated warehouse is calculated. Output the number of orders that can be processed per the calculated predetermined time. A device for estimating the performance of an automated warehouse.

10. In the computing unit, We will obtain customer information including the requirements specifications and floor plans for the automated warehouse. Based on the customer information, a calculation map is generated to calculate the performance of the automated warehouse. Using the customer information and the calculation map, the automated warehouse calculates the processing time for the orders it will process. Based on the calculation results, the automated warehouse calculates the number of orders that can be processed per predetermined time. To output the number of orders that can be processed per predetermined time, program.

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