Simulation device, simulation system, delivery system and simulation method

Abstract

The simulation device (800) of the present application includes a simulation unit (803) that performs simulation of movement of a plurality of moving bodies on a route; a storage unit (802) that stores a predicted reduction rate as an index of temporal change in the degree of congestion of the plurality of moving bodies on the route; a first calculation unit (805) that calculates the degree of congestion of the plurality of moving bodies on the route based on the simulation result; and a second calculation unit (806) that calculates an index of temporal change in the degree of congestion calculated by the first calculation unit. An appropriate reduction rate can be set so that the predicted reduction rate of the degree of congestion used as input for the simulation is equal to the reduction rate of the degree of congestion in the simulation result.

Description

[0001] This application claims priority to Japanese Application No. 2020-18888, filed February 6, 2020, the contents of which are incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to a control technology of a system and a moving method for simulating movement of a plurality of moving bodies. BACKGROUND

[0003] In logistics warehouses and factories, automation of equipment for conveying and unloading cargo operations is progressing, and development of control technology for improving the moving efficiency of an automated equipment group when performing a conveying or the like task is active. In logistics warehouses and factories, an operator collects articles stored in the ground according to a shipment order, and performs a sorting operation, that is, a picking operation, which classifies by a shipment destination. As an example of a picking operation using automated equipment, a picking system using an automated transport vehicle that transports a storage rack in which articles are stored to an operation station where an operator stays, and picks an article corresponding to a shipment order from the transported storage rack is being used.

[0004] In Patent Literature 1, a technology for implementing a picking system with an automated transport vehicle is disclosed. The automated transport vehicle penetrates into the lower side of the transported storage rack when transporting the storage rack to the place of the operator. Then, by lifting the lowest level of the storage rack from below by the automated transport vehicle, the entire storage rack is lifted and transported in a state where the storage rack is off the ground. The operator waits for the arrival of the storage rack in the operation station where the article is taken out. After the arrival of the storage rack at the operation station, the operator takes out the article of the item described in the shipment order, and puts it into a position corresponding to the shipment destination associated with the above shipment order in the rack unit or the small box divided by the shipment destination, thereby completing the picking operation corresponding to each shipment order. The storage rack for which the picking operation has been completed is carried out of the operation station by the automated transport vehicle. According to the embodiment of Patent Literature 1, it is disclosed that, when the automated transport vehicle selects the optimal path to the destination, the current congestion, the past traffic flow tendency, the task priority, and / or other appropriate knowledge are utilized.

[0005] In addition, with the development of automatic driving technology and railway networks in recent years, operation control technology of motor vehicles and vehicle groups for the purpose of suppressing or mitigating congestion has been attracting attention. Patent Literature 2 discloses an invention that performs mitigation assistance corresponding to the likelihood of congestion and the state of congestion. According to the embodiment of Patent Literature 2, when predicting the congestion scale, the future congestion peak probability is predicted based on the temporal change in the congestion peak probability of the prescribed time.

[0006] PRIOR ART DOCUMENTS

[0007] PATENT LITERATURE

[0008] Patent Literature 1: Japanese Patent Application Laid-Open No. 2017-30972

[0009] Patent Literature 2: Japanese Patent Application Laid-Open No. 2018-136781 SUMMARY

[0010] PROBLEMS TO BE SOLVED BY THE INVENTION

[0011] However, in Patent Literature 1 and Patent Literature 2, for example, there are problems as described below. In a case where an action in which a congestion condition is considered in a path search of a mobile body and a relaxation is simulated as time elapses and the congestion condition at the time of the search is eased, it is not easy to determine what value should be set as a reduction rate of a relaxation amount of a given congestion degree per unit time.

[0012] The control technology of the condition in which a plurality of mobile bodies exist is more often based on an effect verification study using simulation because a research cost of a system using a real object is high. In addition, in a real object action, there are cases where a congestion condition and a passing amount caused by movement of a plurality of mobile bodies are predicted and simulation based on modeling is applied. It is preferable to determine a calculation reduction rate of a relaxation condition of a congestion degree in an environment in which a plurality of mobile bodies move based on a prior test using simulation.

[0013] TECHNICAL SOLUTION TO THE PROBLEM

[0014] In order to solve at least one of the above problems, a simulation device of the present application is characterized by including: a simulation unit that performs simulation of movement of a plurality of mobile bodies on a path; a storage unit that stores a calculation reduction rate, which is an index of a temporal change in a congestion degree of the plurality of mobile bodies on the path; a first calculation unit that calculates the congestion degree of the plurality of mobile bodies on the path based on a result of the simulation; and a second calculation unit that calculates an index of a temporal change in the congestion degree calculated by the first calculation unit.

[0015] EFFECT OF THE INVENTION

[0016] According to one embodiment of the present application, an appropriate reduction rate can be set so that a calculation reduction rate of a congestion degree used as an input of simulation is equal to a reduction rate of a congestion degree in a simulation result. Thereby, an efficiency of a transport system that is a simulation object can be improved. The above problems, structures, and effects other than the above will be described through the following examples. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a schematic diagram showing an example of an action environment of a mobile body simulated by a simulation device of an embodiment of the present application.

[0018] Figure 2is a functional block diagram showing an example of the overall structure of a conveyance system simulated by the simulation device of the embodiment of the present application.

[0019] Figure 3 is a block diagram showing an example of the hardware structure of the operation management device and the order management device assumed in the simulation of the embodiment of the present application.

[0020] Figure 4 is an explanatory diagram showing an example of the picking instruction data of a picking operation simulated in the simulation of the embodiment of the present application.

[0021] Figure 5 is a flowchart showing an example of the control of the generation of a movement path of an automated guided vehicle by the operation management device and the instruction of movement in the simulation of the embodiment of the present application.

[0022] Figure 6 is an explanatory diagram showing an example of the user interface for setting the estimated reduction rate in the embodiment of the present application.

[0023] Figure 7 is an explanatory diagram showing an example of the user interface for displaying the result at the time of execution of the simulation in the embodiment of the present application.

[0024] Figure 8 is an explanatory diagram showing an example of the functional structure and the flow of processing of the simulation device in the embodiment of the present application. DETAILED DESCRIPTION

[0025] Next, the embodiment of the present application will be described in detail with appropriate reference to the drawings.

[0026] Figure 1 is a schematic diagram showing an example of the action environment of a mobile body simulated by the simulation device of the embodiment of the present application.

[0027] The mobile body in the present application refers to, for example, an automated guided vehicle that conveys articles in a logistics warehouse or a factory. Figure 1 The article conveyance situation of the automated guided vehicle AC in the warehouse W is included in the simulation. The warehouse W has a work area Wl and an article storage area W2. A plurality of storage racks DS are arranged in the storage area W2. Each of the storage racks DS stores one or more kinds of articles. Then, a plurality of automated guided vehicles AC exist in the storage area W2. Here, the automated guided vehicle AC has a function of conveying the storage racks DS.

[0028] The floor of the storage area W2 is, for example, divided into a two-dimensional grid, and the picking operation described later is performed in the grid. Figure 2The WMS 201 and the operation management device 203 shown manage the positions of the automated transport vehicles AC and the storage racks DS using coordinate values of the centers of each grid (i.e., rectangular partition). In addition, the positions of the automated transport vehicles AC and the storage racks DS can also be managed using vertex coordinate values instead of grid center coordinate values. In addition, each grid has a coordinate marker that includes the coordinate values of the grid. The coordinate marker is, for example, a bar code (also including a two-dimensional code) affixed or painted on the grid. The bar code is information that includes the coordinate values of the grid.

[0029] In addition, in the work area W1, there are a plurality of work stations WSi indicated by symbols WS1, WS2. Since the order-picking work is performed in the work stations WSi in this embodiment, the work stations can also be referred to as order-picking stations. Here, i is the number of the work station WS, and is an integer that satisfies 1≤i≤n. n is an integer of two or more, and indicates the total number of work stations WS. In this example, it is assumed that n=2.

[0030] For example, in the case of a description common to any work station WSi, or in the case of not distinguishing between the work stations WSi, the work station WS is appropriately referred to. The work station WSi has a gate Gij, a terminal Ti, and a sorting shelf SSi. Here, i is the number of the work station WS. In addition, j of the gate Gij is an integer that satisfies 1≤j≤m, and is the number of the gate G provided in each work station WS. In this embodiment, m=2.

[0031] That is, in each work station WSi, one terminal Ti and one sorting shelf SSi, and m gates G are provided. In the case of not distinguishing between each gate Gij, terminal Ti, and sorting shelf SSi, the gate G, gate Gij, terminal T, terminal Ti, sorting shelf SS, or sorting shelf SSi is appropriately referred to. The gate Gij is a landing place of the storage rack DS. One gate Gij corresponds to one storage rack DS. On the terminal Ti, an overview of the sorting target of the article (information of the correspondence between the article and the sorting shelf partition of the sorting shelf SSi), and the like are displayed.

[0032] The sorting shelf SSi provided in the work station WSi is a shelf on which the article picked from the storage rack DS via the gate Gij is placed. Here, i of the worker Mi is the number of the work station WS, and is an integer that satisfies 1≤i≤n. n is an integer of two or more, and indicates the total number of work stations WS. In this example, it is assumed that n=2. In the case of not distinguishing between the workers Mi, the worker M or the worker Mi is appropriately referred to.

[0033] The automated transport vehicle AC transports the storage rack DS in the following flow. First, the automated transport vehicle AC moves to the position of the designated storage rack DS. The automated transport vehicle AC penetrates directly below the designated storage rack DS, and the storage rack DS is transported to the work station WSi. Figure 3The operation management device 203 shown lifts the storage rack DS upward in the vertical direction by a not-shown jack mechanism provided on the top surface of the automated guided vehicle AC when the lifting instruction information is accepted. Thereafter, the automated guided vehicle AC moves while holding the storage rack DS lifted to a designated work station WS within the work area Wl. The automated guided vehicle AC lowers the storage rack DS to the ground when it reaches the work station WS. The picker M performs a picking operation, and the automated guided vehicle AC lifts the storage rack DS again to return the storage rack DS to the original position.

[0034] Figure 2 is a functional block diagram showing an example of the overall configuration of a transport system simulated by a simulation device that simulates an embodiment of the present application.

[0035] The transport system 200 has a WMS (Warehouse Management System) 201, an order management device 202, an operation management device (control section) 203, an automated guided vehicle AC, terminals Ti, a gate control device (omitted from the drawing), and gates Gij. The WMS 201 is communicably connected to the order management device 202 and the operation management device 203. The order management device 202, the operation management device 203, the automated guided vehicle AC, the terminals Ti, and the gate control device Gc are communicably connected to each other via a network 210. At least the automated guided vehicle AC is wirelessly communicable with the operation management device 203 via the network 210.

[0036] The WMS 201 controls the order management device 202 and the operation management device 203. Specifically, the WMS 201 transmits an order and storage rack entry data to the order management device 202. The order refers to information including the name of an article, the number of articles, and the delivery destination of an article to be picked. The storage rack entry data refers to data related to a storage rack DS that stores an article. The specific storage rack entry data includes, for example, the name of an article, the number of articles, the identification information of the storage rack DS in which the article is stored, the position information of the storage section (shelf unit) in which the article is stored (for example, the identification information of the shelf face, the section layer, and the section column to which the storage section belongs), and the like of the article stored in each storage rack DS.

[0037] In addition, the WMS 201 cooperates the processing in the order management device 202 with the processing in the operation management device 203. For example, the WMS 201 instructs the operation management device 203 to return the storage rack DS to the original position when the order management device 202 accepts a notification of the end of the picking operation of an article performed by the picker M (refer to Figure 1 ).

[0038] The operation management unit 203 manages the operation of the automated guided vehicles (AGVs) (e.g., the transport of storage racks DS by the AGVs). Each AGV has a reading device (not shown) such as a visible light camera or infrared camera on its underside, which scans the ground while moving. For example, if the coordinate markers on the ground are barcodes, the reading device is a barcode reader. Then, when passing through a grid with attached coordinate markers, the AGV obtains its coordinate value by scanning the barcode representing that coordinate value with the reading device. The AGVs then send the obtained coordinate values ​​to the operation management unit 203. Thus, the operation management unit 203 manages the current position of each AGV.

[0039] When the operation management device 203 receives the delivery instruction information from the storage rack DS via the WMS 201 from the order management device 202, it determines the work station WSi where the storage rack DS, which holds the items to be delivered, and the sorting rack SSi, which has the delivery destination of the items to be delivered, are located. Then, it obtains the location of the determined storage rack DS and generates path information from that location to the location of the determined work station WSi. At this time, the operation management device 203 sends the path information to a certain automated guided vehicle AC, for example, the automated guided vehicle AC closest to the determined storage rack DS, instructing it to move according to the path information.

[0040] Figure 3 (a) is a block diagram illustrating an example of the hardware structure of the operation management device 203 envisioned in a simulation of an embodiment of the present invention. Figure 3 (b) is a block diagram illustrating a hardware structure example of an order management device 202 conceived in a simulation of an embodiment of the present invention.

[0041] The operation management device 203 of this embodiment can be used Figure 3 The hardware implementation of computer 300 is shown in (a). Computer 300 has a processor 301, a storage device 302, an input device 303, an output device 304, and a communication interface (communication IF) 305. The processor 301, storage device 302, input device 303, output device 304, and communication IF 305 are connected by a bus 306.

[0042] Processor 301 controls computer 300. Storage device 302 is the operating area of ​​processor 301. Furthermore, storage device 302 is a non-transitory or temporary storage medium for storing various programs and data. Examples of storage devices 302 include ROM (Read Only Memory), RAM (Random Access Memory), HDD (Hard Disk Drive), and flash memory.

[0043] In the storage device 302 of the computer 300 used as the operation management device 203 in this embodiment, the operation management program 307, layout information 308, simulation program 309, and simulation information 310 are stored. In this embodiment, the processing performed by the operation management device 203 is actually executed by the processor 301 according to the operation management program 307 or the simulation program 309, and controls the input device 303, output device 304, and communication interface 305 as needed.

[0044] The layout information 308 includes at least information about the configuration of various objects within the storage area W2. For example, the layout information 308 may include the location of each storage rack DS within the storage area W2, the orientation of each storage rack DS (i.e., which shelf faces which direction), the items stored in each storage section of each storage rack DS, the location of each automated guided vehicle AC, the location of the door G, the orientation of the door G, and the path that the automated guided vehicle AC can move when lifting the storage rack DS.

[0045] The simulation information 510 includes information referenced in the simulation executed by the processor 301 according to the simulation program 309 and information generated as a result of the simulation. The information referenced in the simulation, as described later, may include layout information, the initial position of each conveyor AC, the initial value of the estimated reduction rate, and indication data, etc. The layout information included in the simulation information 510 may be a copy of at least a portion of the layout information 308. The estimated reduction rate and indication data are described later. Additionally, the information generated as a result of the simulation may, for example, include the position of each conveyor AC at each time point, the congestion level at each location within the warehouse W calculated based on the position of each conveyor AC at each time point, and the estimated reduction rate calculated based on the congestion level at each time point.

[0046] Input device 303 inputs data. Examples of input devices 303 include a keyboard, mouse, touch panel, numeric keypad, and scanner. Output device 304 outputs data. Examples of output devices 304 include a monitor and printer. Communication IF 305 connects to network 210 to send and receive data.

[0047] The order management device 202 in this embodiment can be used Figure 3 The hardware implementation of computer 350 is shown in (b). Computer 350 has a processor 351, a storage device 352, an input device 353, an output device 354, and a communication interface (communication IF) 355. The processor 351, storage device 352, input device 353, output device 354, and communication IF 355 are connected by a bus 356.

[0048] The processor 351 controls the computer 350. The storage device 352 is a work area of the processor 351. In addition, the storage device 352 is a non-transitory or transitory storage medium that stores various programs and data. As the storage device 352, for example, there are a ROM (Read Only Memory), a RAM (Random Access Memory), an HDD (Hard Disk Drive), a flash memory, and the like.

[0049] In the storage device 352 of the computer 350 used as the order management apparatus 202 of the present embodiment, an order management program 357, order information 358, and storage article information 359 are stored. The processing performed by the order management apparatus 202 of the present embodiment is actually performed by the processor 351 in accordance with the order management program 357, controlling the input device 353, the output device 354, the communication interface 355, and the like as necessary.

[0050] The order information 358 includes at least information on the article, the storage, the delivery destination, and the like of the various articles. For example, the order information 358 can include information on the kind and the number of articles that have completed the picking and the delivery from the articles stored in the system, the article name, the store name and the address of the delivery destination, and the like, information on the kind, the number, and the article name of the articles that have completed the storage into the storage rack of the system, and the like, and information on the shelf ID, the shelf face, and the shelf unit of the storage rack that has completed the picking in the delivery, and the like.

[0051] Figure 4 is a diagram that illustrates an example of the picking instruction data that simulates the picking operation in the simulation in the present embodiment.

[0052] The picking instruction data 400 includes in the simulation information 310 information that uniquely aggregates the ID 401 of the instruction data, the name 402 of the article that needs to be delivered to the delivery destination, the number 403 of the article, the ID 404 of the storage rack DS that has the article, the shelf unit layer 405 that stores the article, the shelf unit column 406, and the ID 410 of the delivery box corresponding to the delivery destination, the ID 407 of the sorting rack SS that carries the delivery box, the shelf unit layer 408, and the shelf unit column 409. Thus, in the present embodiment, the picking operation between which storage rack DS and which sorting rack SS is associated in advance, and the transport is instructed in the simulation after the selection of the automatic transport vehicle AC that transports the storage rack DS.

[0053] In addition, the picking instruction data 400 can be generated based on the actual order and the actual article arrangement determined by the order management device 202 from the held order information 358 and the storage article information 359, or can be generated for simulation regardless of the actual order and the like. The order management device 202 gives such picking instruction data 400 to the transport task pair management device 203. Alternatively, the transport task pair management device 203 can generate such a transport task.

[0054] In addition, Figure 4 The picking instruction data 400 is for simulation, but picking instruction data for actual warehouse operation is also generated in the same form. For example, the order management device 202 generates picking instruction data based on the held order information 358 and the storage article information 359, and gives an actual transport task to the automated transport vehicle AC based on it. Figure 4

[0055] Figure 5 is a flowchart showing an example of the control of the movement of the automated transport vehicle AC generated by the transport task pair management device 203 in the simulation of the embodiment of the present application.

[0056] In the case where the order management device 202 gives a transport task of a shelf resulting from the instruction of the storage and retrieval of the automated transport vehicle AC to the order management device 202, the transport task pair management device 203 receives the transport task (S501), and selects the automated transport vehicle AC that transports the storage shelf DS corresponding to the transport task (S502). Then, the transport task pair management device 203 generates a movement path from the current position of the automated transport vehicle AC to the destination (S503), and gives a movement instruction to the automated transport vehicle. The reduction rate is used in the search process in the path generation, and simulates the effect of the reduction of the passage time of the calculated path based on the recent congestion situation, which is required for the automated transport vehicle AC to travel on the assumed path.

[0057] For example, the transport task pair management device 203 can calculate the degree of congestion of the path that is reduced by the passage of time using the reduction rate set at that time, and search for a path with the minimum cost using the cost based on the calculated degree of congestion (for example, set to be higher as the degree of congestion is higher). Details of the calculation of the degree of congestion based on the reduction rate are described later (Formula (1) and the like).

[0058] ​In a case where the simulation based on the present embodiment determines the reduction rate, in a particularly transport situation where an extreme imbalance occurs in the start point of movement, the movement destination, and the movement path of the automated guided vehicle AC in the movement environment (hereinafter referred to as the layout) of the automated guided vehicle AC, the traffic situation of the entire layout is not preferable. As an example of the transport task of the rack given to the automated guided vehicle AC from the order management device 202, a randomly selected rack in the layout is transported to a randomly selected work station WS, whereby the congestion degree considering the traffic situation of the entire layout can be evaluated, and thus an appropriate reduction rate can be derived.

[0059] In addition, in a case where the number of components of the rack, the work station WS, and the automated guided vehicle AC used in actual use is less than the number of components actually provided within the layout, an appropriate reduction rate in a restricted layout can be derived by using a random transport task generated after excluding the components not actually used in the simulation.

[0060] As another example, in a case where the inventory status of the storage rack DS and the picking work in the work station WS corresponding thereto are known before the physical use, by setting the transport of the storage rack DS required for the known work as the transport task, the reduction rate adjusted for each use can be determined.

[0061] Next, the operation management device 203 determines whether the path searched for the automated guided vehicle AC is prohibited from use due to the scheduled passage of another automated guided vehicle AC (S504). The operation management device 203 prohibits the passage of automated guided vehicles other than the automated guided vehicle for which the passage of the path is reserved, in order to avoid a collision between the automated guided vehicles, by reserving the passage of the path by the automated guided vehicle. The reservation is released after the passage of the reserved automated guided vehicle. In S504, it is determined whether the path searched for the automated guided vehicle is reserved for the passage of another automated guided vehicle.

[0062] In a case where the searched path is prohibited from use (S504: Yes), the operation management device 203 causes the automated guided vehicle AC to stand by until the searched path becomes available (S505). Thereafter, the operation management device 203 determines whether the searched path becomes available, that is, whether the reservation is released (S506). In a case where the searched path has not become available (S506: No), the operation management device 203 determines whether a predetermined time has elapsed from the start of the stand by (S507). In a case where the predetermined time has not elapsed from the start of the stand by (S507: No), the operation management device 203 returns to S506 to determine whether the searched path becomes available. In a case where the predetermined time has elapsed from the start of the stand by (S507: Yes), the operation management device 203 returns to S503 to search for another path for the automated guided vehicle AC.

[0063] When the searched path is available (S504: No, or S506: Yes), the operation management device 203 instructs the automated guided vehicle AC to move on the searched path (S508). Then, the operation management device 203 updates the movement performance of the automated guided vehicle AC (S509). For example, the operation management device 203 can calculate the position of the automated guided vehicle AC at each time from the instruction to move on the path to the instruction of the end of the movement, or based on the time of the instruction of the start of the movement and the time of the instruction of the end of the movement, and so on, and add the calculation result to the simulation information 310, according to the movement speed of the automated guided vehicle AC, and so on. As described later, the degree of congestion in the simulation can be calculated from the movement performance. The above process ends (S510).

[0064] The above is the process of generating and instructing movement of the movement path of the automated guided vehicle AC in the simulation, but the generation and instruction of movement of the movement path of the actual automated guided vehicle AC for performing the transport task based on the actual order can also be performed similarly to the above. In this case, the update of the movement performance (S509) can be performed based on the position of the actual automated guided vehicle AC.

[0065] Figure 6 FIG. 1 is an explanatory diagram showing an example of a user interface for setting the estimated reduction rate in the embodiment of the present application.

[0066] The user using the simulation device based on the present embodiment can input the estimated reduction rate as a real number value through the screen on the electronic terminal. The estimated reduction rate can be input as a real number value as shown in (a) of FIG. 1, or as shown in (b) of FIG. 1, can be input in a separate window started when a certain setting button on the UI relating to the simulation setting is clicked on the terminal. In addition, as another embodiment, it can also be a method of delivering the setting value given by the user to the simulator by reading a text file including the setting value of the estimated reduction rate or other variables used in the simulation. Figure 6 Figure 6 As shown in (a) of FIG. 1, it is one of the setting items set in the same window as other setting variables (for example, the performance update rate) used in the simulation of the present embodiment, or as shown in (b) of FIG. 1, can be set in a separate window started when a certain setting button on the UI relating to the simulation setting is clicked on the terminal. In addition, as another embodiment, it can also be a method of delivering the setting value given by the user to the simulator by reading a text file including the setting value of the estimated reduction rate or other variables used in the simulation.

[0067] As for the method of giving the estimated reduction rate to the simulator by the user, it can not be a setting of a fixed real number value, but a setting of a certain real number range and a scale in the above real number range as additional information, whereby the simulation can also be repeatedly performed with different estimated reduction rates, and be performed in detail. In addition, in the case where the estimated reduction rate is defined in the simulator with a certain linear / non-linear function, the coefficients of the function can also be given. Furthermore, in the case where there are places where the automated guided vehicle is easily congested depending on the layout, the estimated reduction rate can also be set to different values for each area or path. As for the method of giving these estimated reduction rates, the enumerated methods can also be set in any combination. ​

[0068] As an example of the route search method of the automated guided vehicle AC calculated by the operation management device 203, there can be mentioned a method of selecting a place via which the moving time to the destination is the shortest among the movable candidate places. At this time, let the coordinates of the present place be o, the coordinates of the candidate place be v, and the coordinates of the destination be d, and let the time to reach the destination d from the present place o via the candidate place v be Qo(v, d), and the Qo(v, d) of all routes can be managed as a table. As the candidate places via which the place v can be reached from the present place o, there can be mentioned a plurality of candidates such as, in the case of expressing the orthogonal coordinate system with (X, Y) in a two-dimensional grid shape, the coordinate group via which the 1st linear movement can be reached with the value of X or Y of v being set to be equal to that of o. However, when the automated guided vehicle AC moves linearly, the rotation of the vehicle body for moving in the direction of v from the orientation of the automated guided vehicle AC at the present place o can also be attached.

[0069] Qo(v, d) is, for example, the shortest time calculated based on the running performance of the automated guided vehicle AC such as the acceleration, the maximum speed, and the like, and this value is updated as the moving performance, whereby the traffic state in the layout in the operation of the transport system simulated in the simulation is simulated, and thus the congestion situation can be reflected in the simulation. As an example of reflecting the degree of reduction of congestion in Qo(v, d) using the estimation reduction rate, there can be mentioned a method of correcting the moving time with the following formula (1).

[0070] Q'o(v, d) = max(Qo(v, d) - β · (t - t0), Qo(v, d)min) (1)

[0071] Here, the variable β represents the estimation reduction rate given by a real value, for example, the variable t represents the time at which the operation management device 203 performs the route search, the variable t0 represents the final time at which Qo(v, d) is updated by the moving performance of the automated guided vehicle AC, and Qo(v, d)min represents the shortest moving time in the case where the moving performance of the automated guided vehicle is not affected by congestion and moves without stopping. By subtracting the long moving time in the case where congestion occurs with the estimation reduction rate β from the estimated time required to reach the destination d from the present place o via the place v based on the moving performance of the automated guided vehicle AC, the place via which the congestion is alleviated can be considered and the place via which the destination is reached can be determined based on Q'o(v, d). The estimation reduction rate can also not be a scalar value β, but can be defined as a table Bo(v, d) corresponding to the movement schedule of the present place o, the candidate place v, and the destination d.

[0072] As other path search methods, even in the case where the search method based on Dijkstra method, A* method or dynamic programming method is used, which regards the coordinate points in the layout as a graph, the path that alleviates congestion can be calculated using the estimated reduction rate. In this case, the estimated reduction rate is introduced for the connection cost related to the path movement between the coordinates, the cost accompanying the movement between the coordinates is calculated in the same manner as in Equation (1), and thus the optimal path in the case where the congestion is alleviated in the future from the operation time when the path search is performed by the operation management device 203 can be calculated as the path of the minimum cost.

[0073] In addition, the above is a description regarding simulation, but the same path search as described above is performed when the movement path of the actual automated guided vehicle AC for performing the delivery task based on actual orders is generated. The path search algorithm used in the simulation is preferably the same as the path search algorithm used for the movement path generation of the actual automated guided vehicle AC.

[0074] Figure 7 is a diagram illustrating an example of a user interface that displays the result when the simulation is performed in the embodiment of the present application.

[0075] Specifically, Figure 7 An application screen displayed on the electronic terminal including the simulation device of the present embodiment is illustrated as an example in the above. However, the result of the simulation of the present embodiment can be held as an output file or the like, and the above output file or the like can be read and displayed by an application prepared in another terminal.

[0076] Figure 7 is a bird's-eye view in the case where the layout is viewed from the top of the land, and illustrates an example of the automated guided vehicle AC, the shelf DS placed in the shelf storage area SA or being delivered by the automated guided vehicle AC, the work station WS (omitted in the above), and the passageway existing in the layout. Figure 7 The above is not limited to the example of Figure 7 For example, the layout can be expressed by a three-dimensional CG. In addition, as components of the displayed layout, the charging place of the automated guided vehicle AC, the order picker, the work place of the order picking, the area of the post-process to which the article is delivered after the order picking is completed, and the area of the pre-process of the article included in the layout can be included.

[0077] In order to inform the user of the congestion situation of the automated guided vehicle AC, for example, the congestion situation of the automated guided vehicle AC can be displayed on the display of the electronic terminal. Figure 7The congestion degree of the automated guided vehicles AC is displayed as a heat map CHM overlaid with the layout in the illustrated user interface. Here, the congestion degree can be calculated based on the number of automated guided vehicles AC that pass between the coordinates of interest per unit time, or the movement time required for one automated guided vehicle AC, and the like. For example, it can be calculated in a manner such that the smaller the number of automated guided vehicles AC that pass between the coordinates of interest per unit time, the greater the congestion degree, or in a manner such that the longer the movement time required for one automated guided vehicle AC, the greater the congestion degree, or based on a combination thereof.

[0078] In addition to the congestion degree based on the movement performance within the simulation (hereinafter referred to as "actual congestion degree"), a calculated congestion degree calculated based on the estimated reduction rate at the time of the route search of the automated guided vehicles during the simulation execution can also be displayed as a result of the simulation. At this time, the calculated congestion degree can be displayed as a heat map as with the actual congestion degree, and for example, by using a color map of a different color from the heat map CHM representing the actual congestion degree, the two can be compared in terms of the degree of deviation between the calculated and actual congestion degrees during the simulation.

[0079] The user of the simulation device of the present embodiment compares the two congestion degrees described above while adjusting the estimated reduction rate as an input to the simulation, whereby an appropriate value at which congestion of the automated guided vehicles AC is less likely to occur can be set as the estimated reduction rate. As a result, the simulation can achieve a transport system of the automated guided vehicles AC that can transport articles in a short time, and can achieve an effect of improving the work efficiency of the picking system.

[0080] In addition, in order to reduce visual clutter caused by simultaneously displaying two heat maps, the difference between the calculated congestion degree and the actual congestion degree obtained as a result of the simulation can also be displayed as a heat map. As a result, a portion in which the two deviate greatly can be identified from one heat map.

[0081] In addition, the heat map is illustrated in the above example as an example of a method of displaying the distribution of congestion in the space in which the automated guided vehicles AC travel, i.e., the simulation target space, but the distribution of congestion can also be displayed in a method other than this.

[0082] Figure 8 is an explanatory diagram illustrating an example of the functional configuration and the flow of the process of the simulation device 800 in the embodiment of the present application.

[0083] In addition, in the present embodiment, as Figure 3As shown in (a), the storage device 302 of the computer 300 that implements the operation management device 203 retains the simulation program 309 and the simulation information 310. That is, the simulation device 800 of the present embodiment is implemented by the computer 300. Specifically, the input unit 801, the simulation unit 803, the congestion degree calculation unit 805, the congestion degree reduction rate calculation unit 806, and the evaluation unit 807 described later are functions implemented by the processor 301 controlling the input device 303, the output device 304, the communication IF 305, and the like as necessary in accordance with the simulation program 309. In addition, the input parameter storage unit 802 and the simulation result storage unit 804 are provided as storage areas of the storage device 302, and the information held therein is included in the simulation information 310.

[0084] However, the structure described above is only an example, and the simulation device 800 of a structure other than the above can also be implemented. For example, the computer 350 that implements the order management device 202 can also implement the simulation device 800 by retaining the simulation program 309 and the simulation information 310 in the storage device 352 of the computer 350. Alternatively, the simulation device 800 can also be implemented by a computer (omitted from the drawing) different from the computer 300 and the computer 350. In addition, the functions of the simulation device 800 can also be implemented by processing distributed among a plurality of computers (omitted from the drawing). The simulation device 800 can also be changed to a simulation system, and the functions of the simulation system can be implemented by one or more computers as described above.

[0085] The input unit 801 saves the parameters input by the user interface or the like shown in (a) or the related implementation method in the input parameter storage unit 802. The parameters input here can include, for example, the initial value of the estimated reduction rate, the layout of the warehouse in which the simulation is performed, the initial positions of the automated guided vehicles AC in the simulation, and the transport tasks used in the simulation. Figure 6

[0086] The simulation unit 803 performs simulation of the transport and picking work of the storage racks DS using the automated guided vehicles AC in the present embodiment with appropriate reference to the parameters stored in the input storage unit 602, and passes the execution result to the simulation result storage unit 804. The simulation of the transport of the storage racks DS at this time is performed, for example, as shown in (b). Figure 5

[0087] The congestion degree calculation unit 805 calculates the congestion degree of the path by referring to the movement history of the automated guided vehicles AC accumulated in the simulation result storage unit 804, and causes the simulation result storage unit 804 to store the congestion degree. The congestion degree reduction rate calculation unit 806 calculates the congestion degree reduction rate by referring to the congestion degree and the time information from the simulation result storage unit 804, and causes the simulation result storage unit 804 to store the congestion degree reduction rate.

[0088] ​​The evaluation section 807 calculates the estimated reduction rate as a new input parameter from the congestion degree reduction rate referred to from the simulation result storage section 804 and stores it in the input parameter storage section 802, whereby the estimated reduction rate can be adaptively brought close to an appropriate value. For example, the evaluation section 807 can cause the congestion degree reduction rate referred to to be reflected in the estimated reduction rate as a new input parameter in accordance with the performance update rate input. In this case, the evaluation section 807 can set, for example, a value obtained by weighted averaging the congestion degree reduction rate referred to and the estimated reduction rate at that time on the basis of the performance update rate as the estimated reduction rate as a new input parameter.

[0089] In addition, the estimated reduction rate as a new input parameter thus calculated can be stored in the input parameter storage section 802 in a manner that covers the estimated reduction rate used as an input parameter in the last simulation, or can be stored in the input parameter storage section 802 in a manner that is added while leaving the last estimated reduction rate. In the simulation of time, the estimated reduction rate as a new input parameter is used.

[0090] The simulation device 800 repeats the above-described processing, and in a case where a prescribed condition is satisfied (for example, in a case where it is determined that the value of the estimated reduction rate converges, or in a case where the number of repetitions or the calculation time reaches a prescribed upper limit, or the like), ends the above-described processing, and acquires a final estimated reduction rate. The operation management device 203 uses the estimated reduction rate thus acquired to search for a path of an automated guided vehicle AC for a transport task generated on the basis of an actual order, and controls an actual automated guided vehicle AC in a manner that travels on the path.

[0091] In addition, the effects of the present application are also effective for embodiments other than the above-described embodiments. As an example, movement of an automated guided vehicle in a logistics warehouse or a factory is assumed in the above-described embodiments, but as other embodiments, the above-described simulation can also be applied in a case where a forklift truck capable of automatic movement, a bucket conveyance device in an automated warehouse, or the like is simulated. Alternatively, the above-described simulation can also be applied to a traffic system in which the travel of each vehicle on a road is centrally controlled.

[0092] The above-described embodiments of the present application can include the following examples.

[0093] (1) For example, a simulation device of the present application can have a simulation section (for example, the simulation section 803) that performs simulation of a plurality of moving bodies passing on a path; a storage section (for example, the storage device 302) that holds an estimated reduction rate as an index of a temporal change in congestion degree of the plurality of moving bodies on the path; a first arithmetic section (for example, the congestion degree arithmetic section 805) that calculates the congestion degree of the plurality of moving bodies on the path on the basis of a result of the simulation; and a second arithmetic section (for example, the congestion degree reduction rate arithmetic section 806) that calculates an index of a temporal change in the congestion degree calculated by the first arithmetic section.

[0094] Thus, an appropriate reduction rate can be set so that the estimated reduction rate of the congestion level used as an input of the simulation is equal to the reduction rate of the congestion level in the simulation result. Thus, the efficiency of the transport system that is the object of the simulation can be improved. For example, a decrease in efficiency caused by setting a path that passes through a region in which congestion has not been eliminated or setting a path that avoids a region in which congestion has been eliminated can be prevented.

[0095] (2) In the above (1), the simulation unit can perform the simulation of the travel of the plurality of mobile bodies on the searched path using a cost based on the congestion level (for example, S503) to search for the path of the plurality of mobile bodies (for example, S502), and Figure 5 where the congestion level is calculated based on the estimated reduction rate read from the storage unit,

[0096] The simulation device can further have an evaluation unit (for example, evaluation unit 807) that calculates the estimated reduction rate based on an index of the temporal change in the congestion level calculated by the second calculation unit and stores the calculated estimated reduction rate in the storage unit.

[0097] Thus, an appropriate estimated reduction rate can be set, and the efficiency of the transport system that is the object of the simulation can be improved.

[0098] (3) In the above (2), the simulation device can further have an output unit (for example, output device 304) that displays a distribution of at least one of the congestion level calculated based on the simulation result and the congestion level calculated based on the estimated reduction rate in the object space of the simulation (for example, the heat map shown in Figure 7 ).

[0099] Thus, the estimated congestion level and the actual congestion level in the simulation can be easily compared in terms of how much the estimated congestion level deviates from the actual congestion level.

[0100] (4) In the above (1), the congestion procedure of the path can be defined based on at least one of the time required for the mobile body to move on the path and the number of mobile bodies that move on the path per unit time.

[0101] Thus, the congestion level suitable for the cost used in the path search can be calculated.

[0102] (5) In the above (1), the simulation unit can perform the simulation of the travel of a plurality of mobile bodies in the object space of the simulation from a randomly specified movement origin (for example, the position of a rack that holds a transport object) to a movement destination (for example, the position of a work station).

[0103] Thus, an appropriate estimated reduction rate can be derived without extreme imbalance in the movement path.

[0104] (6) In the above (1), the plurality of mobile bodies can be automated transport vehicles that transport racks in which articles are stored.

[0105] Thus, the present application can be applied to the conveyance of articles in, for example, a warehouse or a factory.

[0106] (7) A conveyance system (for example, conveyance system 200) can be configured to have the simulation device of (1) above or a simulation system equivalent thereto; an order management device (for example, order management device 202) that manages order information including the delivery destination of an article and the quantity to be delivered; and a movement management device (for example, movement management device 203) that controls the movement of an automated transport vehicle in order to convey a shelf in which an article is stored in accordance with the order information, by performing a path search based on a congestion degree calculated using the estimated reduction rate calculated by the simulation system.

[0107] Thus, the present application can be applied to the conveyance of articles in, for example, a warehouse or a factory.

[0108] In addition, the present application is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments are described in detail in order to better understand the present application and are not limited to necessarily having all of the structures described. In addition, a part of the structure of one embodiment can be replaced with the structure of another embodiment, and the structure of another embodiment can be added to the structure of one embodiment. In addition, other structures can be added, deleted, or replaced for a part of the structure of each embodiment.

[0109] For each of the above-described structures, functions, processing sections, processing units, and the like, a part or all thereof can be implemented in hardware, for example, by designing in an integrated circuit. In addition, each of the above-described structures, functions, and the like can be implemented in software by a processor interpreting and executing a program that implements each function. The program, table, file, and the like that implement each function can be stored in a nonvolatile memory, a hard disk drive, an SSD (Solid State Drive), or the like, a storage device, or an IC card, an SD card, a DVD, or the like, a non-transitory data storage medium that is readable by a computer.

[0110] In addition, the control lines and information lines are shown as necessary for explanation and do not necessarily show all of the control lines and information lines on the product. In fact, it can be considered that almost all of the structures are connected to each other.

Claims

1. A simulation device comprising: a simulation section that performs simulation of movement of a plurality of moving bodies on a path; a storage section that stores a calculated decrease rate, which is an index of temporal change in congestion degree of the plurality of moving bodies on a path; a first calculation section that calculates a congestion degree of the plurality of moving bodies on a path based on a result of the simulation; and a second calculation section that calculates an index of temporal change in the congestion degree calculated by the first calculation section, the simulation device being characterized in that: the simulation section performs simulation of movement on a searched path using a cost based on a congestion degree calculated based on the calculated decrease rate read from the storage section, the simulation device further has an evaluation section that calculates a new calculated decrease rate by performing weighted average based on a performance update rate of the index of temporal change in the congestion degree calculated by the second calculation section and the calculated decrease rate stored in the storage section, and stores the calculated new calculated decrease rate in the storage section.

2. The simulation device according to claim 1, characterized in that: it further has an output section that displays a distribution of at least one of a congestion degree calculated based on a result of the simulation and a congestion degree calculated based on the calculated decrease rate in an object space of the simulation.

3. The simulation device according to claim 1, characterized in that: the congestion degree of the path is defined based on at least one of a time required for the moving bodies to move on the path and a number of the moving bodies moving on the path per unit time.

4. The simulation device according to claim 1, characterized in that: the simulation section performs simulation of movement of the plurality of moving bodies from a randomly specified movement origin to a movement destination in an object space of the simulation.

5. The simulation device according to claim 1, characterized in that: the plurality of moving bodies are automated transport vehicles that transport racks in which articles are stored.

6. A simulation system comprising: a simulation section that performs simulation of movement of a plurality of moving bodies on a path; a storage section that stores a calculated decrease rate, which is an index of temporal change in congestion degree of the plurality of moving bodies on a path; a first calculation section that calculates a congestion degree of the plurality of moving bodies on a path based on a result of the simulation; and a second calculation section that calculates an index of temporal change in the congestion degree calculated by the first calculation section, the simulation system being characterized in that: the simulation section performs simulation of movement on a searched path using a cost based on a congestion degree calculated based on the calculated decrease rate read from the storage section, the simulation system further has an evaluation section that calculates a new calculated decrease rate by performing weighted average based on a performance update rate of the index of temporal change in the congestion degree calculated by the second calculation section and the calculated decrease rate stored in the storage section, and stores the calculated new calculated decrease rate in the storage section. ​ The simulation system further has an evaluation section that performs an operation of weighted average based on a performance update rate on an index of temporal change in the congestion degree calculated by the second operation section and the estimated reduction rate stored in the storage section, to calculate a new estimated reduction rate, and stores the calculated new estimated reduction rate in the storage section.

7. The simulation system according to claim 6, characterized in that: Further having an output section that displays a distribution of at least one of a congestion degree calculated based on a result of the simulation and a congestion degree calculated based on the estimated reduction rate in the object space of the simulation.

8. The simulation system according to claim 6, characterized in that: The congestion degree of the path is defined based on at least one of a time required for the mobile body to move on the path and a number of the mobile bodies moving on the path per unit time.

9. The simulation system according to claim 6, characterized in that: The simulation section performs simulation of movement of the plurality of mobile bodies from a randomly specified movement origin to a movement destination in the object space of the simulation.

10. The simulation system according to claim 6, characterized in that: The plurality of mobile bodies are automated transport vehicles that transport racks on which articles are stored.

11. A delivery system characterized by, including: The simulation system according to claim 10; An order management device that manages order information including a delivery destination of the articles and a quantity to be delivered; and A movement management device that controls movement of the automated transport vehicles by performing a congestion degree-based path search in order to transport the racks on which the articles are stored in accordance with the order information, wherein the congestion degree is calculated using the estimated reduction rate calculated by the simulation system.

12. A simulation method performed by a simulation system having a processor and a storage section, characterized by: The storage section stores an estimated reduction rate that is an index of temporal change in a congestion degree of a plurality of mobile bodies on a path, The simulation method includes: A first step in which the processor performs simulation of movement of the plurality of mobile bodies on a path; A second step in which the processor calculates a congestion degree of the plurality of mobile bodies on a path based on a result of the simulation; and A third step in which the processor calculates an index of temporal change in the congestion degree calculated in the second step, In the first step, the processor searches for a path of the plurality of mobile bodies using a congestion degree-based cost, to perform simulation of movement on the searched path, wherein the congestion degree is calculated based on the estimated reduction rate read from the storage section, The simulation method further includes a fourth step in which the processor performs an operation of weighted average based on a performance update rate on the index of temporal change in the congestion degree calculated in the third step and the estimated reduction rate stored in the storage section, to calculate a new estimated reduction rate, and stores the calculated new estimated reduction rate in the storage section.

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