Scheduling apparatus and computer-readable recording medium

Abstract

A scheduling device (10A) creates scheduling data (41) for inputting a plurality of tasks to a processing machine (81), the scheduling device having: a schedule creating section (11) that creates the scheduling data (41) based on a processing estimated time obtained by estimating a time required for processing each task; a required maintenance period calculating section (13) that calculates, for the processing machine (81), a period during which maintenance is required, i.e., required maintenance period data (42), based on a consumable life prediction period (31) of a consumable used by the processing machine (81) and a component maintenance period (32) of a component possessed by the processing machine (81); a stop period calculating section (14) that calculates, based on the scheduling data (41) and the required maintenance period data (42), a period during which processing by the processing machine (81) is stopped due to maintenance in the execution of a task, i.e., a stop period; and a stop period output section (15) that outputs the stop period to an external device.

Description

TECHNICAL FIELD

[0001] The present application relates to a scheduling device and a learning device that create scheduling data for tasks in a machine tool. BACKGROUND

[0002] In recent years, in order to efficiently execute tasks in a machine tool, it is desirable to create scheduling data that can shorten the total execution time of tasks composed of a plurality of tasks.

[0003] The processing scheduling system described in Patent Literature 1 performs scheduling of processing by distinguishing between production preparation work for a workpiece in operation of a machine tool and production preparation work in a state in which the machine tool is stopped.

[0004] Patent Literature 1: Japanese Patent No. 5622483 SUMMARY

[0005] However, in the technology described in Patent Literature 1, scheduling is performed without taking into account maintenance work in the machine tool, and thus in the case where maintenance work is required, the total execution time of tasks composed of a plurality of tasks cannot be shortened in some cases.

[0006] The present application has been made in view of the above circumstances, and has an object to obtain a scheduling device that can shorten the total execution time of tasks composed of a plurality of tasks.

[0007] In order to solve the above problems and achieve the object, the present application is a scheduling device that creates scheduling data indicating scheduling for inputting a plurality of tasks to a wire electric discharge machine, the scheduling device including: a scheduling creation section that creates scheduling data based on processing estimation time obtained by estimating time required for processing of each task. In addition, the scheduling device of the present application includes a required maintenance period calculation section that calculates, for the wire electric discharge machine, a period in which maintenance is required, i.e., a required maintenance period, based on a period in which life is predicted, i.e., a life prediction period, of consumables consumed in processing and consumables consumed in processing and non-processing, and a period in which maintenance of components of the wire electric discharge machine is performed, i.e., a maintenance period. In addition, the scheduling device of the present application includes a stop period calculation section that calculates, based on the scheduling data and the required maintenance period, a period in which processing performed by the wire electric discharge machine is stopped due to maintenance, i.e., a stop period, in execution of a task; and an output section that outputs the stop period to an external device.

[0008] EFFECT OF THE INVENTION

[0009] The scheduling device of the present application has an effect that the total execution time of tasks composed of a plurality of tasks can be shortened. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 FIG. 1 is a diagram showing the structure of a scheduling device according to Embodiment 1.

[0011] Figure 2 FIG. 2 is a diagram showing the structure of scheduling data created by the scheduling device according to Embodiment 1.

[0012] Figure 3 FIG. 3 is a diagram showing the structure of period information created by the scheduling device according to Embodiment 1.

[0013] Figure 4 FIG. 4 is a diagram for explaining the consumable life prediction period calculated by the scheduling device according to Embodiment 1.

[0014] Figure 5 FIG. 5 is a diagram for explaining the component maintenance period calculated by the scheduling device according to Embodiment 1.

[0015] Figure 6 FIG. 6 is a diagram showing the structure of required maintenance period data created by the scheduling device according to Embodiment 1.

[0016] Figure 7 FIG. 7 is a diagram for explaining the required maintenance period of a consumable in the case where processing is performed, which is set by the scheduling device according to Embodiment 1.

[0017] Figure 8 FIG. 8 is a diagram for explaining the required maintenance period of a consumable in the case where processing is not performed, which is set by the scheduling device according to Embodiment 1.

[0018] Figure 9 FIG. 9 is a diagram for explaining the required maintenance period for maintenance, which is set by the scheduling device according to Embodiment 1.

[0019] Figure 10 FIG. 10 is a flowchart showing the calculation processing sequence of the required maintenance period implemented by the scheduling device according to Embodiment 1.

[0020] Figure 11 FIG. 11 is a flowchart showing the prompting processing sequence of the stop period implemented by the scheduling device according to Embodiment 1.

[0021] Figure 12 FIG. 12 is a diagram for explaining the calculation processing of the stop period implemented by the scheduling device according to Embodiment 1.

[0022] Figure 13 FIG. 13 is a diagram showing a hardware structure example of the scheduling device according to Embodiment 1.

[0023] Figure 14is a diagram showing the structure of the scheduling device according to Embodiment 2.

[0024] Figure 15 is a flowchart showing the processing sequence of the correction processing of the scheduling data performed by the scheduling device according to Embodiment 2.

[0025] Figure 16 is a diagram for explaining the correction processing of the scheduling data performed by the scheduling device according to Embodiment 2 and the correction processing of the scheduling data performed by the scheduling device according to the comparative example.

[0026] Figure 17 is a diagram showing the structure of the learning device according to Embodiment 3.

[0027] Figure 18 is a flowchart showing the processing sequence of the learning processing performed by the learning device according to Embodiment 3.

[0028] Figure 19 is a diagram showing the structure of the inference device according to Embodiment 3.

[0029] Figure 20 is a flowchart showing the processing sequence of the inference processing performed by the inference device according to Embodiment 3. DETAILED DESCRIPTION

[0030] Hereinafter, the scheduling device and the learning device according to Embodiment 1 will be described in detail based on the drawings.

[0031] Embodiment 1.

[0032] Figure 1 is a diagram showing the structure of the scheduling device according to Embodiment 1. The scheduling device 10A is a computer that creates scheduling data 41 of a task performed in a machine tool and calculates a period in which the machine tool is stopped based on the scheduling data 41. The scheduling device 10A has a scheduler that performs the creation of the scheduling data 41 and the calculation of a stop period in which the machine tool is stopped.

[0033] The scheduling device 10A is connected to a machining tool 81 and a display device 83. The machining tool 81 is a device arranged in a machine tool and performs machining of a workpiece. An example of the machining tool 81 is a working machine such as a wire electric discharge machine. In the following description, a case where the machining tool 81 is a wire electric discharge machine will be described.

[0034] The machining device 81 has a control device 82 that controls the machining device 81, and the control device 82 transmits operation history information indicating an operation history of the machining device 81 to the dispatch device 10A. In the operation history information, for example, a wire travel time, a pressure change of a deposit removal filter, i.e., a machining fluid filter, and a water supply time to the machining fluid filter are included. An example of the display device 83 is a liquid crystal monitor. The machining fluid filter is also called a machining chip removal filter.

[0035] The dispatch device 10A is a centralized dispatcher that creates a dispatch of various devices including an AMR (Autonomous Mobile Robot), an AGV (Automatic Guided Vehicle), and the like, for example.

[0036] The dispatch device 10A creates dispatch data 41 used when various workpieces are sequentially machined by the machining device 81 through various machining programs.

[0037] The dispatch device 10A has a dispatch creation section 11, a period information creation section 12, a required maintenance period calculation section 13, a stop period calculation section 14, and a stop period output section 15.

[0038] The dispatch creation section 11 creates the dispatch data 41 in which an execution order of a unit of machining, i.e., a task, executed by the machining device 81 is registered. Specifically, the dispatch creation section 11 creates the dispatch data 41 indicating an execution dispatch of a task composed of a plurality of units based on user specification information 20 input by a user and corresponding time information 24. The user specification information 20 includes machining program ID (Identification) information 23, workpiece ID information 22, and machining instruction information 21.

[0039] The dispatch creation section 11 receives the user specification information 20 including the machining program ID information 23, the workpiece ID information 22, and the machining instruction information 21. The dispatch creation section 11 receives a plurality of user specification information 20 from the user. One user specification information 20 corresponds to one task.

[0040] The machining program ID information 23 is information that identifies a machining program used in the execution of a task. The machining program is created for each task. The workpiece ID information 22 is information that identifies a workpiece. The machining instruction information 21 includes information that identifies the machining device 81, i.e., a machining device ID, a completion deadline of a task, i.e., a delivery period of a task, and the like. In addition, information of a priority level of an executed task can be included in the user specification information 20.

[0041] The corresponding time information 24 is information indicating an estimated time of processing corresponding to the combination of the processing program ID information 23, the workpiece ID information 22, and the processing machine ID. Information associating the processing program ID information 23, the workpiece ID information 22, the processing machine ID, and the estimated time of processing is stored in the corresponding time information 24.

[0042] The scheduling device 10A acquires the corresponding time information 24 from the external device in advance and stores it in a memory (not shown) or the like.

[0043] The schedule creation section 11 calculates an estimated time of processing corresponding to a task based on the combination of the processing program ID information 23, the workpiece ID information 22, and the processing machine ID included in the user specification information 20 and the corresponding time information 24 stored in the memory or the like.

[0044] Here, the structure of the schedule data 41 will be described. Figure 2 is a diagram indicating the structure of schedule data created by the scheduling device according to Embodiment 1. In the schedule data 41, a task ID, a priority level, a processing instruction, a workpiece ID, a processing program ID, and an estimated time are associated.

[0045] The task ID is information identifying a task. The task ID is set by the schedule creation section 11. The schedule creation section 11 registers a priority level for each task ID in the schedule data 41 based on the user specification information 20 received from the user. The schedule creation section 11 registers information of a processing instruction in the schedule data 41 based on the processing instruction information 21 received from the user. The processing machine ID and a delivery deadline of a task are included in the processing instruction of the schedule data 41.

[0046] In addition, the schedule creation section 11 registers a workpiece ID in the schedule data 41 based on the workpiece ID information 22 received from the user. In addition, the schedule creation section 11 registers a processing program ID in the schedule data 41 based on the processing program ID information 23 received from the user.

[0047] In addition, the schedule creation section 11 registers an estimated time in the schedule data 41 based on the corresponding time information 24. An estimated time of processing processing, an estimated time of pre-processing, and an estimated time of post-processing are included in the estimated time of the schedule data 41. A workpiece in-feed time and a workpiece changeover adjustment time are included in the estimated time of pre-processing. The workpiece changeover adjustment is a parallel mounting processing of a workpiece, a horizontal mounting processing of a workpiece, a measurement processing of a reference position of a workpiece, or the like. A measurement processing of a workpiece and a workpiece out-feed time are included in the estimated time of post-processing.

[0048] The schedule creation section 11 creates the schedule data 41, for example, in a manner in which the tasks are executed in order from the task for which the delivery date is close. Further, the schedule creation section 11 can create the schedule data 41 in a manner in which the tasks are executed in order from the task for which the priority level is high. The schedule creation section 11 transmits the schedule data 41 created to the stop period calculation section 14.

[0049] The tasks executed in accordance with the schedule data 41 can be implemented in accordance with a user's instruction, or can be implemented by an automation system that automatically outputs an instruction to the workpiece transport device or the machining device 81, and the like.

[0050] The schedule creation section 11 can create the schedule data 41 for one machining device 81, or can create the schedule data 41 for a plurality of machining devices 81.

[0051] The schedule creation section 11 can automatically order the execution order of the tasks set in accordance with the priority level on the basis of the order of the delivery dates, and the like. Further, the schedule creation section 11 can automatically order the execution order of the tasks set on the basis of the order of the delivery dates, and the like, on the basis of the priority level.

[0052] The period information creation section 12 receives the operation history information transmitted from the control device 82 of the machining device 81. The period information creation section 12 calculates the consumable life prediction period 31 indicating the life prediction period of each consumable on the basis of the operation history information. Further, the period information creation section 12 extracts the component maintenance period 32 indicating the maintenance period of each component from the operation history information. The machining device ID is included in the consumable life prediction period 31 and the component maintenance period 32. The consumable life prediction period 31 can be a time until the life of the consumable, or can be a date and time at which the consumable reaches the life. The component maintenance period 32 is a date and time at which the maintenance of the component is scheduled to be performed.

[0053] Further, the period information creation section 12 can handle the component that is not managed by the maintenance cycle but by the use time, similarly to the life of the consumable.

[0054] Here, the structure of the period information including the consumable life prediction period 31 and the component maintenance period 32 will be described. Figure 3 is a diagram indicating the structure of the period information created by the schedule device according to Embodiment 1. In the period information 40, the information for identifying the machining device 81, that is, the machining device ID, the consumable life prediction period 31, and the component maintenance period 32 are associated with each other.

[0055] Among the consumables, there are consumables that are consumed only in the machining and consumables that are consumed also in the non-machining. Therefore, the period information creating section 12 calculates the consumable life prediction period 31 for each consumable in accordance with the consumption mode of each consumable.

[0056] For example, in the case of a wire electric discharge machine, the wire (wire electrode wire) is a consumable that is consumed only in the machining. Therefore, the period information creating section 12 calculates the consumption amount that represents the consumption amount of the wire based on the wire feeding speed in the machining, and calculates the consumable life prediction period 31 of the wire based on the consumption amount.

[0057] In addition, the machining liquid filter is a consumable that is consumed only in the machining. Therefore, the period information creating section 12 calculates the consumable life prediction period 31 of the machining liquid filter based on data such as the pressure change of the circuit (hereinafter, referred to as filter water passage circuit) through which water is passed in the machining liquid filter, the generation amount of machining chips generated by the machining, the material of the workpiece being machined, and the like.

[0058] In addition, as the ion exchange resin in the machining liquid, that is, the ion concentration adjustment resin, water is passed through the ion exchange resin also in the non-machining, and therefore it is a consumable that is consumed in both the machining and the non-machining. Therefore, the period information creating section 12 calculates the consumable life prediction period 31 based on the amount of water passage to the ion exchange resin that contains both the machining and the non-machining.

[0059] In the period information 40 of the machining liquid filter, the consumable life prediction period 31 of the machining liquid filter is calculated based on the pressure change of the filter water passage circuit, the generation amount of machining chips, the material of the workpiece being machined, and the like. Figure 3 In the period information 40 of the machining liquid filter, the consumable life prediction period 31 of the machining liquid filter is calculated based on the pressure change of the filter water passage circuit, the generation amount of machining chips, the material of the workpiece being machined, and the like.

[0060] For each component included in the machining machine 81, there are the maintenance periods required for each component, such as the daily inspection, the 1-week inspection, the 1-month inspection, the semi-annual inspection, and the like. Therefore, the period information creating section 12 sets the maintenance periods required for each component as the component maintenance periods 32 in the period information 40. In the period information 40 of the machining machine 81, the maintenance period required for each component is set as the component maintenance period 32. Figure 3 In the period information 40 of the machining machine 81, the most recent, that is, the first maintenance period is shown by the maintenance C1, and the second maintenance period is shown by the maintenance C2. The period information creating section 12 transmits the period information 40 to the required maintenance period calculating section 13.

[0061] In addition, the period information creating section 12 is not limited to the case where the period information 40 in which the consumable life prediction period 31 and the component maintenance period 32 are synthesized is created, and can create the consumable life prediction period 31 and the component maintenance period 32 as individual data.

[0062] In addition, the period information 40 can also be created by the control device 82. In this case, the control device 82 has the period information creation section 12. In addition, the period information 40 can also be created by an external computer or the like other than the control device 82. In this case, the external computer or the like has the period information creation section 12.

[0063] Here, the consumable life prediction period 31 and the component maintenance period 32 are explained. Figure 4 is a graph for explaining the consumable life prediction period calculated by the scheduling device related to Embodiment 1. Figure 5 is a graph for explaining the component maintenance period calculated by the scheduling device related to Embodiment 1.

[0064] Figure 4 The horizontal axis is time, and the vertical axis is the spool diameter. Figure 5 The horizontal axis is time, and the vertical axis is the pressure of the filter water passage of the machining fluid filter. In Figure 4 , the time and the changing spool diameter are shown as the spool diameter Pl, and in Figure 5 , the time and the changing pressure of the filter water passage are shown as the pressure P2.

[0065] In the wire electric discharge machine, the wire is fed from the spool on which the wire is wound, and the work is machined by the fed wire, and the wire used in the machining is recovered. Therefore, the spool diameter Pl of the spool on which the wire is wound becomes smaller as time passes as shown in Figure 4 . The period information creation section 12 calculates the time tl at which the spool diameter Pl becomes a certain diameter, i.e., the threshold value Ql, as the consumable life prediction period 31 of the wire.

[0066] In addition, in the wire electric discharge machine, as the machining progresses, machining chips are deposited, and therefore the pressure P2 of the filter water passage increases as time passes as shown in Figure 5 . The period information creation section 12 calculates the time t2 at which the pressure of the filter water passage becomes a certain pressure, i.e., the threshold value Q2, as the component maintenance period 32 of the machining fluid filter.

[0067] The required maintenance period calculation section 13 calculates the required maintenance period data 42 based on the period information 40 including the consumable life prediction period 31 and the component maintenance period 32. The required maintenance period data 42 is data indicating the period in which the maintenance work is required for the life of the consumable or the maintenance of the component. The required maintenance period data 42 includes information of the start time of the maintenance and the end time of the maintenance.

[0068] The required maintenance period data 42 includes data of the earliest replacement of the consumables or the required maintenance among the life prediction periods of the consumables and the maintenance periods of the components, i.e., the nearest period (hereinafter, sometimes referred to as the nearest maintenance period) in which the maintenance work is required. The required maintenance period data 42 can be the time until the maintenance work is required, or the date and time in which the maintenance work is required. At the time of the maintenance work, the processing machine 81 is stopped.

[0069] The scheduling device 10A calculates the required maintenance period data 42 by the required maintenance period calculating section 13 in order to prompt the stop period of the processing machine 81, for example, in correspondence with the scheduling data 41. The degree of consumption of the consumables varies depending on the operating conditions such as in processing and out of processing, and the life prediction period also varies. Therefore, the required maintenance period calculating section 13 calculates the life prediction period for each consumable, distinguishing between in processing and out of processing.

[0070] The required maintenance period calculating section 13 calculates the required maintenance period of the required maintenance period data 42 based on the life of all the consumables, for example, in the case of performing processing, and calculates the required maintenance period of the required maintenance period data 42 based on the life of the consumables also consumed out of processing, in the case of not performing processing. That is, the required maintenance period calculating section 13 calculates the progress degree of consumption, distinguishing between the period of performing processing and the period of not performing processing, and calculates the required maintenance period based on the progress degree of consumption. The required maintenance period calculating section 13 registers the calculated required maintenance period in the required maintenance period data 42.

[0071] In addition, the required maintenance period calculating section 13 registers the required maintenance period corresponding to the component maintenance period 32 in the required maintenance period data 42, regardless of the operating conditions of the processing machine 81. In addition, the required maintenance period calculating section 13 calculates the nearest maintenance period based on the progress degree of consumption and the component maintenance period 32, distinguishing between the period of performing processing and the period of not performing processing. The required maintenance period calculating section 13 registers the nearest maintenance period in the required maintenance period data 42.

[0072] Here, the required maintenance period data 42 will be described. Figure 6 is a diagram showing the structure of the required maintenance period data created by the scheduling device according to Embodiment 1. In the required maintenance period data 42, the processing machine ID, the required maintenance period, the consumable life prediction period 31, and the component maintenance period 32 are associated. In other words, in the required maintenance period data 42, the required maintenance period, the consumable life prediction period 31, and the component maintenance period 32 are registered for each processing machine ID. In addition, in the required maintenance period data 42, the nearest maintenance period of each processing machine ID is registered.

[0073] Figure 6 The consumable life prediction period 31 and the component maintenance period 32 shown in the drawing correspond to the period information 40. Figure 3 The consumable life prediction period 31 and the component maintenance period 32 shown in the period information 40 correspond to each other.

[0074] The required maintenance period calculation section 13 calculates the required maintenance period based on the consumable life prediction period 31 and the component maintenance period 32. The required maintenance period includes the processing time until the most recent maintenance, the processing time + non-processing time, and the scheduled date and time of the most recent maintenance. The required maintenance period can be shown by the time until the period when maintenance is required, or by the date and time when maintenance is required.

[0075] In a case where the consumable B11 that is consumed only in processing reaches the life earlier than the consumable B12 that is consumed in processing and non-processing, and the life is close to the component maintenance period 32, the required maintenance period calculation section 13 sets the timing of the life of the consumable B11 that is consumed only in processing as the most recent maintenance period. In this case, if it becomes the most recent maintenance period, replacement of the consumable B11 that is consumed only in processing is performed.

[0076] In a case where the consumable B12 that is consumed in processing and non-processing reaches the life earlier than the consumable B11 that is consumed only in processing, and the life is close to the component maintenance period 32, the required maintenance period calculation section 13 sets the timing of the life of the consumable B12 that is consumed in processing and non-processing as the most recent maintenance period. In this case, if it becomes the most recent maintenance period, replacement of the consumable B12 that is consumed in processing and non-processing is performed.

[0077] In a case where the component maintenance period 32 is earlier than the period of the life of the consumables B11, B12, the required maintenance period calculation section 13 sets the component maintenance period 32 as the most recent maintenance period. In this case, if it becomes the most recent maintenance period, maintenance of the component is performed.

[0078] In Figure 6 In the drawing, the most recent maintenance period in each processing machine 81 is shown by diagonal hatching. The processing time + non-processing time included in the required maintenance period becomes the same time as the life of the consumable that is consumed in processing and non-processing in the consumable life prediction period 31. In addition, the period of maintenance included in the required maintenance period is the same as the period of the most recent maintenance in the component maintenance period 32. In addition, the processing time until the most recent maintenance included in the required maintenance period becomes a time that is equal to or less than the time until the most recent required maintenance period.

[0079] Figure 7This diagram illustrates the maintenance period required for consumables during processing, as set by the scheduling device according to Embodiment 1. Figure 7 In the diagram, consumables consumed only during processing are shown under consumable B11, while consumables consumed both during and outside of processing are shown under consumable B12 and B13.

[0080] During processing, all consumables B11 to B13 are consumed. Therefore, the maintenance period calculation unit 13 sets the lifespan of the consumable that has reached its shortest lifespan from among consumables B11 to B13 as the maintenance period TM1. Here, consumable B11 has the shortest remaining lifespan, so the maintenance period calculation unit 13 sets the lifespan of consumable B11 as the maintenance period TM1.

[0081] Figure 8 This diagram illustrates the maintenance period required for consumables when no processing is performed, as set by the scheduling device according to Embodiment 1. Figure 8 In the diagram, consumables consumed only during processing are shown under consumable B11, while consumables consumed both during and outside of processing are shown under consumable B12 and B13.

[0082] Consumables B12 and B13 are consumed when no processing is performed, i.e., during non-processing, but consumable B11 is not consumed. Therefore, the maintenance period calculation unit 13 sets the lifespan of the consumable that has reached its shortest lifespan from consumables B12 and B13 as the maintenance period TM2. Here, consumable B13 has the shortest remaining lifespan, so the maintenance period calculation unit 13 sets the lifespan of consumable B13 as the maintenance period TM2.

[0083] Figure 9 This diagram illustrates the maintenance-required periods for setting the scheduling device according to Embodiment 1. Figure 9 In the diagram, the first maintenance is shown as maintenance C1, and the second maintenance is shown as maintenance C2. Maintenance C1 will take place at 9:00 AM on May 30th, and maintenance C2 will take place at 9:00 AM on September 29th.

[0084] The maintenance period calculation unit 13 sets the most recent maintenance among maintenance C1 and C2 as the maintenance period TM3. Here, the maintenance period of maintenance C1 is the most recent period, so the maintenance period calculation unit 13 sets the timing of maintenance C1 as the maintenance period TM3.

[0085] The required maintenance period calculating section 13 registers the required maintenance periods TM1 to TM3 and the like in the required maintenance period data 42. The most recent required maintenance period among the required maintenance periods TM1 to TM3 is the most recent maintenance period. The required maintenance period calculating section 13 can register the period of the second maintenance C2 as the required maintenance period data 42. The required maintenance period calculating section 13 transmits the required maintenance period data 42 to the stop period calculating section 14.

[0086] The stop period calculating section 14 judges whether maintenance is required until the completion of the task registered in the schedule data 41 on the basis of the schedule data 41 and the required maintenance period data 42. The stop period calculating section 14 calculates the period in which maintenance is required, that is, the stop period on the basis of the schedule data 41 and the required maintenance period data 42 in the case where it is judged that maintenance is required. The stop period is the period in which the processing machine 81 is stopped for maintenance. That is, the stop period is the period in which the execution of the schedule corresponding to the schedule data 41 is stopped at a certain period and maintenance is required. An example of the stop period is the most recent maintenance period.

[0087] The stop period calculating section 14 transmits the calculated stop period to the stop period output section 15. In addition, the stop period calculating section 14 transmits the schedule data 41 and the required maintenance period data 42 to the stop period output section 15.

[0088] The stop period output section 15 outputs the stop period, the schedule data 41 and the required maintenance period data 42 to the display device 83.

[0089] Thus, the display device 83 displays the stop period, the schedule data 41 and the required maintenance period data 42. In addition, the stop period output section 15 can output the stop period, the schedule data 41 and the required maintenance period data 42 to an external device other than the display device 83. In this case, the stop period output section 15 transmits the stop period, the schedule data 41 and the required maintenance period data 42 to an external computer or the like via a communication line or the like.

[0090] As described above, the schedule device 10A calculates the stop period and prompts in the case where it is judged that maintenance is required until the completion of the task registered in the schedule data 41.

[0091] Next, the calculation processing procedure of the required maintenance period and the prompting processing procedure of the stop period will be described. Figure 10 is a flowchart showing the calculation processing procedure of the required maintenance period by the schedule device according to Embodiment 1.

[0092] The required maintenance period calculation section 13 of the dispatch device 10A collects the consumable life prediction period 31 of each consumable from the period information creation section 12 (step S10). The required maintenance period calculation section 13 extracts the timing of the life of the consumable that reaches the shortest life based on the consumable life prediction period 31 (step S20).

[0093] In addition, the required maintenance period calculation section 13 collects the component maintenance period 32 of each component from the period information creation section 12 (step S30). The required maintenance period calculation section 13 extracts the period of the required maintenance that is the shortest based on the component maintenance period 32 (step S40).

[0094] Further, the required maintenance period calculation section 13 can execute the process of step S10 before the process of step S20, and can execute the process of step S30 before the process of step S40, and can execute the processes of steps S10 to S40 in an arbitrary order.

[0095] The required maintenance period calculation section 13 calculates the required maintenance period based on the timing of the life of the consumable that reaches the shortest life and the period of the required maintenance that is the shortest, and creates the required maintenance period data 42 after executing the processes of steps S10 to S40 (step S50). Specifically, the required maintenance period calculation section 13 creates the required maintenance period data 42 based on the consumable life prediction period 31 and the component maintenance period 32.

[0096] In addition, the required maintenance period calculation section 13 calculates the recent maintenance period based on the timing of the life of the consumable that reaches the shortest life and the period of the required maintenance that is the shortest (step S60). Further, the required maintenance period calculation section 13 can execute the process of step S50 at an arbitrary timing if it is after steps S10 and S30. In addition, the required maintenance period calculation section 13 can execute the process of step S60 at an arbitrary timing if it is after steps S20 and S40.

[0097] Figure 11 is a flowchart showing the order of the process of the stop period by the dispatch device according to Embodiment 1. The stop period calculation section 14 of the dispatch device 10A acquires the dispatch data 41 and the required maintenance period data 42 (step S110).

[0098] The stop period calculating section 14 extracts the estimated time of the Nth (N is a natural number) task, task (N), from the schedule data 41 (step S120). The 1st task (1) is a task executed first by the processing machine 81, and the Nth task (N) is a task executed Nth by the processing machine 81. The stop period calculating section 14 extracts the estimated time of the 1st task, task (1), from the schedule data 41. The stop period calculating section 14 extracts the required maintenance period of the processing machine 81 to which the tasks (1) to (N) are input, from the required maintenance period data 42 (step S130).

[0099] The stop period calculating section 14 calculates the period until maintenance based on the required maintenance period. The stop period calculating section 14 subtracts the estimated time of the task (N) from the period until maintenance. The stop period calculating section 14 subtracts the estimated time of the task (1) from the period until maintenance (step S140).

[0100] The stop period calculating section 14 judges whether the subtraction result is less than or equal to 0 (step S150). That is, the stop period calculating section 14 judges whether the period until maintenance is longer than the estimated time of the task (1).

[0101] In the case where the subtraction result is greater than 0 (step S150, No), the stop period calculating section 14 sets the subtraction result as the period until maintenance (step S170). That is, the stop period calculating section 14 updates the period until maintenance by the subtraction result.

[0102] The stop period calculating section 14 judges whether the estimated time is subtracted from the period until maintenance for all the tasks (step S180).

[0103] In the case where the estimated time is not subtracted from the period until maintenance for all the tasks (step S180, No), the stop period calculating section 14 returns to the process of step S120. The stop period calculating section 14 extracts the estimated time of the next task (N) as the process of step S120. The stop period calculating section 14 extracts the estimated time of the 2nd task, task (2), from the schedule data 41. Then, the stop period calculating section 14 executes the processes of steps S130 to S150.

[0104] The stop period calculating section 14 repeats the processes of steps S120 to S170 until the period until maintenance is subtracted from the estimated time for all the tasks in the case where the subtraction result is greater than 0 in the process of step S150. In the case where the estimated time is subtracted from the period until maintenance for all the tasks (step S180, Yes), the stop period calculating section 14 ends the prompting process of the stop period. That is, in the case where the subtraction result is not less than or equal to 0 even if the estimated time is subtracted from the period until maintenance for all the tasks, maintenance work is not required during the period until all the tasks are completed, and thus the stop of the processing machine 81 is not required.

[0105] In addition, in the case where the subtraction result is less than or equal to 0 in the process of step S150 (step S150, Yes), the stop period calculating section 14 determines that the maintenance required period is reached in the processing of the task for which the subtraction result is less than or equal to 0. The stop period calculating section 14 transmits the processing in which the subtraction result is less than or equal to 0 as the stop period to the stop period output section 15.

[0106] The stop period output section 15 outputs the stop period to the display device 83. Thereby, the display device 83 displays the stop period. As described above, the scheduling device 10A prompts the user of the calculated stop period (step S160).

[0107] Figure 12 is a diagram for explaining the calculation process of the stop period performed by the scheduling device according to Embodiment 1. Here, the process of calculating the stop period by the stop period calculating section 14 for the consumables B11 to B13 explained in Figure 7 and Figure 8 is explained.

[0108] As explained in Figure 7 and Figure 8 , in the case where processing is performed, that is, in the case of processing, the maintenance required period calculating section 13 sets the timing of the life of the consumable B11, which is the consumable that reaches the shortest life, as the maintenance required period TM1 from among the consumables B11 to B13. In addition, in the case where processing is not performed, that is, in the case of non-processing, the timing of the life of the consumable B13, which is the consumable that reaches the shortest life, is set as the maintenance required period TM2 from among the consumables B12 and B13.

[0109] The stop period calculating section 14 determines the nearest maintenance required period, that is, the nearest maintenance period, from among the maintenance required periods TM1 and TM2. The stop period calculating section 14 determines the maintenance required period TM1 as the nearest maintenance period. The stop period calculating section 14 determines whether the maintenance required period TM1 determined as the nearest maintenance period is the timing of the execution of the task.

[0110] In Figure 12 , it is shown that the tasks registered in the schedule are tasks (1), (2). The timing at which the task (1) is completed is a completion timing ST1, and the timing at which the task (2) is completed is a completion timing ST2.

[0111] In a case where the maintenance-required time period TM1 is a timing at which the execution of the task (1) or the task (2) is in progress, the stop time period calculation section 14 calculates the stop time period by the process explained in Figure 11 . For example, in a case where the maintenance-required time period TM1 is a timing at which the execution of the task (2) is in progress, the stop time period calculation section 14 determines that the execution of the task (2) is the stop time period.

[0112] The stop time period calculation section 14 creates information that prompts a change of the maintenance-required time period TM1 before the execution of the task (2). Specifically, the stop time period calculation section 14 creates information that prompts the setting of the completion timing ST1 at which the task (1) is completed as the start timing of the maintenance Mx as the change prompting information. The stop time period calculation section 14 transmits the created change prompting information to the stop time period output section 15.

[0113] The stop time period output section 15 outputs the change prompting information to the display device 83. Thereby, the display device 83 displays the change prompting information. Thereby, the change prompting information is displayed on the display device 83, and thus the schedule device 10A can prompt the user with an appropriate stop time period.

[0114] As described above, the schedule device 10A can notify the user of a case where maintenance is required in the execution of a task in a case where maintenance is required in the execution of a task. Thereby, the schedule device 10A can cause the user to recognize a case where processing is stopped due to maintenance in the execution of a task, and thus the user can correct the schedule so that the maintenance work is performed before the execution of the task in which processing is stopped.

[0115] Thereby, it is possible to avoid a case where a task is interrupted halfway due to maintenance. In a case where each task is continuously executed without stopping halfway, the processing is completed in a shorter time than in a case where it is stopped halfway. The schedule device 10A can prompt the user with an appropriate stop time period in which a task is not stopped halfway due to maintenance, and thus it is possible to prevent the execution time of a task from becoming long.

[0116] In addition, the user can recognize in advance that maintenance of the processing machine 81 is awaited in the execution of a task, and thus can give an instruction to a maintenance worker so that maintenance is performed at a predicted time of maintenance. Thereby, the maintenance worker can perform maintenance before the execution of a task in which maintenance is required in the middle of the task. Thus, the schedule device 10A can reduce a wasted time from when the processing machine 81 is stopped until the maintenance is started.

[0117] Here, the hardware structure of the scheduling device 10A will be described. Figure 13 is a view showing a hardware structure example of the scheduling device according to Embodiment 1.

[0118] The scheduling device 10A can be implemented by the input device 300, the processor 100, the memory 200, and the output device 400. An example of the processor 100 is a CPU (also referred to as Central Processing Unit, central processing device, processing device, arithmetic device, microprocessor, microcomputer, DSP (Digital Signal Processor)), or a system LSI (Large Scale Integration). An example of the memory 200 is a RAM (Random Access Memory), or a ROM (Read Only Memory).

[0119] The scheduling device 10A is implemented by the processor 100 reading out and executing a computer executable program, i.e., a scheduler, for performing the operation of the scheduling device 10A stored in the memory 200. The program for performing the operation of the scheduling device 10A can be said to be a sequence or a method for causing a computer to perform the scheduling device 10A.

[0120] The scheduler executed by the scheduling device 10A becomes a module structure including the schedule creation section 11, the period information creation section 12, the required maintenance period calculation section 13, and the stop period calculation section 14, which are downloaded onto the main storage device, and which are generated on the main storage device.

[0121] The input device 300 receives the user designation information 20 and the operation history information and sends them to the processor 100. The memory 200 is used as a temporary memory when various processes are performed by the processor 100. The memory 200 stores the schedule data 41, the required maintenance period data 42, the stop period, the change prompt information, and the like. The output device 400 outputs the schedule data 41, the required maintenance period data 42, the stop period, the change prompt information, and the like to the display device 83.

[0122] The scheduler can be provided as a computer program product by storing it in a computer-readable storage medium in a file in an installable form or an executable form. In addition, the scheduler can be provided to the scheduling device 10A via a network such as the Internet. Furthermore, as for the functions of the scheduling device 10A, a part thereof can be implemented by a dedicated hardware such as a dedicated circuit, and a part thereof can be implemented by software or firmware. In addition, as for the scheduling device 10B, the learning device 50, and the inference device 60 described in Embodiment 2 and onwards, they can also be implemented by the same hardware structure as the scheduling device 10A.

[0123] Further, the processing machine 81 is not limited to a wire electric discharge machine, and can be a machining center, an NC (Numerical Control) milling machine, or an NC lathe. These machining centers, NC milling machines, and NC lathes also use tools as consumables, and thus require maintenance work for tool replacement. In addition, these machining centers, NC milling machines, and NC lathes also use NC programs for performing processing, and thus can predict the life of a tool based on a path length, a feed speed, or a rotational speed of a shaft set in the NC program. Therefore, the scheduling device 10A can also be applied to machining centers, NC milling machines, and NC lathes.

[0124] As described above, in Embodiment 1, the scheduling device 10A calculates a required maintenance period based on the life prediction period and the maintenance period, and calculates a stop period of the processing machine 81 based on the scheduling data 41 and the required maintenance period. Thereby, it is possible to prevent maintenance during execution of a task performed by the processing machine 81, and thus it is possible to shorten the total execution time of a task composed of a plurality of tasks.

[0125] Embodiment 2.

[0126] Next, the use of the scheduling device 10B in Embodiment 2 will be described. Figure 14 to Figure 16 Embodiment 2 will be described. In Embodiment 2, the scheduling device corrects the scheduling data 41 in a case where maintenance is to be performed during execution of a task if the task is executed in the order registered in the scheduling data 41.

[0127] Figure 14 is a view showing the structure of the scheduling device according to Embodiment 2. The structures of the scheduling device 10B will be described with reference to Figure 14 The same reference numerals are attached to the structure elements of the scheduling device 10B that have the same functions as the structure elements of the scheduling device 10A shown in Figure 1

[0128] The scheduling device 10B further has a scheduling correction section 16 in addition to the structure elements of the scheduling device 10A. The scheduling correction section 16 is connected to the stop period calculation section 14.

[0129] In Embodiment 2, the required maintenance period calculation section 13 calculates the required maintenance period data 42 including a maintenance time, which is a time taken for maintenance. That is, the required maintenance period data 42 of Embodiment 2 includes information of a start time of maintenance, an end time of maintenance, and a time required for maintenance.

[0130] ​The stop period calculating section 14 judges whether the tasks registered in the schedule data 41 can be executed without being stopped midway due to maintenance, based on the schedule data 41 and the required maintenance period data 42. In the case where the tasks registered in the schedule data 41 will be stopped midway due to maintenance, the stop period calculating section 14 calculates the stop period based on the schedule data 41 and the required maintenance period data 42. In this case, the stop period calculating section 14 transmits the stop period, the schedule data 41 and the required maintenance period data 42 to the schedule revision section 16.

[0131] The schedule revision section 16 revises the schedule data 41 based on the stop period, the schedule data 41 and the required maintenance period data 42. The schedule revision section 16 revises the schedule data 41 so that the tasks registered in the schedule data 41 will not be stopped midway due to maintenance. At this time, the schedule revision section 16 revises the execution order of the tasks within the schedule data 41, and revises the start timing of the maintenance.

[0132] The schedule device 10B can update the schedule data 41 in real time in the execution of the tasks. In this case, the schedule creating section 11 updates the processing estimated time of the tasks in execution in real time in the execution of the tasks. In addition, the required maintenance period calculating section 13 updates the required maintenance period data 42 in real time in the execution of the tasks. Furthermore, the stop period calculating section 14 updates the stop period in real time in the execution of the tasks, and the schedule revision section 16 updates by revising the schedule data 41 in real time in the execution of the tasks.

[0133] In the case where the number of the processing machines 81 is one, the schedule revision section 16 judges whether there is a task which will come to the timing of the maintenance midway in the execution if the task is executed in the order registered in the schedule data 41. Hereinafter, the task which will come to the timing of the maintenance midway in the execution will be sometimes referred to as a midway maintenance task. The midway maintenance task is a task which is in execution at the stop period of the processing machine 81.

[0134] In the case where there is a midway maintenance task (M) (M is any natural number from 1 to N-1), the schedule revision section 16 judges whether the maintenance will come to the timing midway in the execution of the next task, i.e., a task (M+1), if the task (M+1) is executed before the task (M) compared to the task (M).

[0135] In the case where the maintenance will not come to the timing midway in the execution of the task (M+1), the schedule revision section 16 revises the schedule data 41 so as to execute the task (M+1) before the task (M) by skipping the task (M). That is, the schedule revision section 16 switches the order of the task (M) and the task (M+1). The task switched with the midway maintenance task is a replacement task.

[0136] In a case where the timing of the maintenance comes in the middle of the execution of the task (M+1), the schedule correction section 16 determines whether the timing of the maintenance comes in the middle of the execution of the next task, i.e., the task (M+2), if the task (M+1) is executed first compared to the task (M) and the task (M+1).

[0137] In a case where the timing of the maintenance does not come in the middle of the execution of the task (M+2), the schedule correction section 16 corrects the schedule data 41 so that the task (M+2) is executed first by skipping the tasks (M) and (M+1). That is, the schedule correction section 16 swaps the task whose execution timing does not come in the middle and the task whose execution timing comes in the middle. Further, the schedule correction section 16 can move the task whose execution timing does not come in the middle to before the task determined as the task whose execution timing comes in the middle.

[0138] As described above, the schedule correction section 16 performs the search of the tasks until the task whose execution timing does not come in the middle is found. The schedule correction section 16 performs the search of the task whose execution timing does not come in the middle sequentially from the task (M+1) to the last task (N).

[0139] The schedule correction section 16 repeatedly performs the process of correcting the schedule data 41 so that the task whose execution timing does not come in the middle is executed first compared to the task whose execution timing comes in the middle.

[0140] The schedule correction section 16 transmits the schedule data 41 after the correction to the stop period output section 15. Thereby, the stop period output section 15 outputs the stop period, the required maintenance period data 42, and the schedule data 41 after the correction to the display device 83.

[0141] Further, in a case where the task whose execution timing comes in the middle exists, the stop period output section 15 can output the stop period to the display device 83 without correction of the schedule data 41 by the schedule correction section 16. That is, the schedule correction section 16 can transmit the stop period directly to the stop period output section 15. In this case, the stop period output section 15 outputs the stop period from the schedule correction section 16 to the display device 83. Further, the stop period output section 15 can receive the stop period from the required maintenance period calculation section 13 and output to the display device 83.

[0142] The schedule device 10B can select whether to output the stop period to the display device 83 without correction of the schedule data 41 or to change the execution order of the tasks in a case where the task whose execution timing comes in the middle exists. Whether to correct the schedule data 41 is set in the schedule device 10B in advance according to an instruction from the user.

[0143] In the case where the plurality of processing machines 81 are provided, the schedule modification section 16 can change the processing machine 81 that executes the task on the basis of the change of the execution order of the task in the same processing machine 81 as described above, and change the processing machine 81 that executes the task according to the priority level of the task.

[0144] In addition, the schedule modification section 16 compares the date and time at which the task ends last, i.e., the first date and time, in the schedule data 41 before the modification of each processing machine 81, and the date and time at which the task ends last, i.e., the second date and time, in the schedule data 41 after the modification of each processing machine 81, in the case where the plurality of processing machines 81 are provided. In this case, the result of the comparison by the schedule modification section 16 is the improvement degree of the schedule data 41. The schedule modification section 16 can calculate the improvement degree of the schedule data 41 by calculating the difference between the first date and time and the second date and time. The improvement degree of the schedule data 41 can be the difference between the first date and time and the second date and time, or a value obtained by dividing the difference by the first date and time.

[0145] The schedule modification section 16 transmits the calculated improvement degree to the stop period output section 15, and the stop period output section 15 causes the improvement degree to be displayed on the display device 83. Thus, the user can recognize the improvement degree of the schedule data 41.

[0146] Figure 15 is a flowchart showing the modification processing order of the schedule data performed by the schedule device according to Embodiment 2. The schedule modification section 16 of the schedule device 10B acquires the stop period, the schedule data 41, and the required maintenance period data 42 (step S210).

[0147] The schedule modification section 16 determines whether there is a maintenance task in the middle (step S220). In the case where there is no maintenance task in the middle (step S220, No), the schedule modification section 16 ends the processing without modifying the schedule data 41.

[0148] In the case where there is a maintenance task in the middle (step S220, Yes), the schedule modification section 16 determines whether the modification of the schedule data 41 is set (step S230).

[0149] In the case where the modification of the schedule data 41 is not set (step S230, No), the schedule modification section 16 directly outputs the acquired stop period to the stop period output section 15 (step S260), and ends the modification processing of the schedule data 41.

[0150] In a case where the modification of the schedule data 41 is set (step S230, Yes), the schedule modification section 16 determines whether the execution order of the tasks is changed in the same processing machine 81 (step S240). In a case where the processing machine 81 is one, the schedule modification section 16 determines that the execution order of the tasks is changed in the same processing machine 81. In addition, in a case where the change of the execution order of the tasks in the same processing machine 81 is set by the user in the schedule device 10B, the schedule modification section 16 determines that the execution order of the tasks is changed in the same processing machine 81.

[0151] The schedule modification section 16 determines whether the next task becomes the interim maintenance task in a case where the next task is executed first in a case where it is determined that the execution order of the tasks is changed in the same processing machine 81 (step S240, Yes) (step S250).

[0152] The schedule modification section 16 exchanges the task which does not become the interim maintenance task, that is, the next task and the interim maintenance task in a case where it is determined that the next task does not become the interim maintenance task even if the next task is executed first (step S250, No) (step S270).

[0153] As described above, the schedule modification section 16 exchanges the task which does not reach the required maintenance period (the next task) and the interim maintenance task so that the task is executed before the maintenance in a case where the task which does not reach the required maintenance period can be extracted from the input predetermined tasks with respect to all the processing machines 81. Thus, the schedule modification section 16 can reduce the number of maintenances, and thus can improve the operation rate of the processing machines 81 by the reduction of the number of maintenances, and can achieve cost reduction by using the consumable until reaching the life. The schedule modification section 16 outputs the schedule data 41 after the exchange of the tasks to the stop period output section 15, and ends the modification process of the schedule data 41.

[0154] The schedule modification section 16 determines whether the next task which becomes the interim maintenance task is the last task in a case where it is determined that the next task also becomes the interim maintenance task if the next task is executed first (step S250, Yes) (step S280). That is, the schedule modification section 16 determines whether the next task determined through step S250 is the last executed task.

[0155] The schedule modification section 16 returns to the process of step S250 if it is determined that the next task which becomes the interim maintenance task is not the last task (step S280, No).

[0156] The schedule correction section 16 searches for the task that does not become the interim maintenance task in the order of the execution order of the tasks until the task that does not become the interim maintenance task is found. That is, the schedule correction section 16 repeats the processes of steps S250 and S280.

[0157] If the schedule correction section 16 determines that the next task that becomes the interim maintenance task is the last task (step S280, Yes), the schedule correction section 16 outputs the acquired stop period directly to the stop period output section 15 (step S290), and ends the correction process of the schedule data 41.

[0158] If the schedule correction section 16 determines that the execution order of the tasks is not changed in the same processing machine 81 in the process of step S240 (step S240, No), the schedule correction section 16 determines whether there is a task that has a higher priority level than the interim maintenance task (hereinafter, referred to as a priority higher task) among the tasks that are executed before the interim maintenance task. That is, if the schedule correction section 16 determines that the execution order of the tasks is changed in the plurality of processing machines 81, the schedule correction section 16 determines whether there is a priority higher task among the tasks that are executed before the interim maintenance task (step S300).

[0159] If the schedule correction section 16 determines that there is a priority higher task (step S300, Yes), the schedule correction section 16 determines whether the priority higher task can be executed in the other processing machine 81 (step S310).

[0160] If the schedule correction section 16 determines that the priority higher task can be executed in the other processing machine 81 (step S310, Yes), the schedule correction section 16 swaps the interim maintenance task and the priority higher task (step S320). In this case, the schedule correction section 16 selects the priority higher task that does not become a new interim maintenance task in the case where the priority higher task is swapped with the current interim maintenance task among the priority higher tasks. The schedule correction section 16 does not swap the current interim maintenance task and the priority higher task in the case where the priority higher task becomes a new interim maintenance task in the case where the priority higher task is swapped with the current interim maintenance task. The schedule correction section 16 outputs the schedule data 41 after the tasks are swapped to the stop period output section 15, and ends the correction process of the schedule data 41.

[0161] The schedule correction section 16 judges whether there is the last judged machining device 81 among the machining devices 81 that are the judgment targets in the case where it is judged that the priority upper task cannot be executed in the other machining device 81 (step S330). That is, the schedule correction section 16 judges whether there remains the machining device 81 for which the judgment of whether the priority upper task can be executed is not performed, in addition to the other machining device 81 in the case where it is judged that the priority upper task cannot be executed in the other machining device 81.

[0162] The schedule correction section 16 returns to the process of step S310 in the case where it is judged that the other machining device 81 for which the judgment of whether the priority upper task is executed is not the last judged machining device 81 among the machining devices 81 that are the judgment targets (step S330, No). The last judged machining device 81 is the machining device 81 for which the judgment of whether the priority upper task is executed is last judged among the other machining devices 81 that are the judgment targets of whether the priority upper task is executed.

[0163] The schedule correction section 16 searches for the machining device 81 in which the priority upper task is executed until the machining device 81 in which the priority upper task is executed is found. That is, the schedule correction section 16 repeats the processes of steps S310 and S330.

[0164] The schedule correction section 16 outputs the acquired stop period directly to the stop period output section 15 (step S340) in the case where it is judged that the other machining device 81 for which the judgment of whether the priority upper task is executed is the last judged machining device 81 among the machining devices 81 that are the judgment targets (step S330, Yes), and ends the correction process of the schedule data 41.

[0165] The schedule correction section 16 outputs the acquired stop period directly to the stop period output section 15 in the case where it is judged that there is no priority upper task in the process of step S300 (step S300, No) (steps S350), and ends the correction process of the schedule data 41.

[0166] Further, the case where the acquired stop period is output directly to the stop period output section 15 in the processes of steps S290, S340, and S350 is described, but the schedule correction section 16 can set the execution of the task that is initially judged to be the interim maintenance task as the stop period. In this case, the schedule correction section 16 sets the timing between the task that is initially judged to be the interim maintenance task and the task immediately before the task as the stop period, and sets the task that is initially judged to be the interim maintenance task as the end of the stop period. In this case, the schedule correction section 16 also outputs the set stop period to the stop period output section 15.

[0167] As described above, the schedule correction section 16 extracts a task that is not in execution at the stop period, that is, a replacement task, from the tasks of all the processing machines 81, instead of the in-process task, that is, the in-process maintenance task, that is in execution at the stop period. The schedule correction section 16 corrects the schedule data 41 by exchanging the replacement task and the in-process maintenance task when the replacement task can be extracted. In addition, the schedule correction section 16 corrects the schedule data 41 by setting the stop period for maintenance to before the in-process task and setting the in-process task after the maintenance when the replacement task cannot be extracted.

[0168] Figure 16 is a view for explaining a correction process of the schedule data performed by the schedule device according to Embodiment 2 and a correction process of the schedule data performed by the schedule device according to the comparative example. In the correction process of the schedule data 41 performed by the schedule device 10B, a process of exchanging the tasks between the processing machines 81 by the schedule correction section 16 is explained in a case where the processing machines 81 are plural.

[0169] On the right side of Figure 16 , the execution plan of the tasks to the processing machines 81 and the execution results of the tasks are shown in a case where the schedule device 10B corrects the schedule data 41. On the left side of Figure 16 , the execution plan of the tasks to the processing machines 81 and the execution results of the tasks are shown in a case where the schedule device according to the comparative example does not correct the schedule data 41.

[0170] The schedule device according to the comparative example and the schedule device 10B sometimes execute the tasks using the processing machine 81 of the No. 1 machine and the processing machine 81 of the No. 2 machine. In this case, in the schedule data 41, it is set that the tasks are executed by the No. 1 machine in the order of the tasks (1), (3) and the tasks are executed by the No. 2 machine in the order of the tasks (2), (4).

[0171] In Figure 16 , the scheduled completion date and time of the task (1) is shown by the date and time T1 and the scheduled completion date and time of the task (3) is shown by the date and time T3. In addition, the scheduled completion date and time of the task (2) is shown by the date and time T2 and the scheduled completion date and time of the task (4) is shown by the date and time T4.

[0172] The execution time of the total of the tasks (1), (3) is set to be longer than the execution time of the total of the tasks (2), (4). In addition, the required maintenance period is shown by the required maintenance period TM21. The required maintenance period TM21 is between the date and time T1 and the date and time T3. That is, the required maintenance period TM21 becomes in the execution of the task (3). The required maintenance period TM21 is, for example, a replacement timing of the wire.

[0173] The scheduling device of the comparative example does not change the scheduling data 41. In this case, the No. 1 machine executes the task (1), and then, starts the execution of the task (3). In the case where the scheduling data 41 is not changed, the maintenance required period TM21 occurs in the execution of the task (3), and therefore, if the maintenance required period TM21 occurs, the No. 1 machine stops, and the task (3) is interrupted. Further, the maintenance instruction to the No. 1 machine is notified to the maintenance worker. After the No. 1 machine stops, the start of the maintenance performed by the maintenance worker is waited for. After the start of the maintenance is waited for, the maintenance Mx is performed. Figure 16 In the comparative example, the state of waiting for the start of the maintenance is shown by the waiting state Wx. After the waiting state Wx, the maintenance performed by the maintenance worker is performed. In the comparative example, the maintenance is shown by the maintenance Mx. Figure 16 In the comparative example, the scheduled completion date and time of the maintenance Mx set by the scheduling device of the comparative example is shown by the date and time T5. Figure 16

[0174] The No. 1 machine resumes the task (3) after the time of the waiting state Wx and the time of the maintenance Mx elapse. As described above, the scheduling device of the comparative example does not change the scheduling data 41, and therefore, the task (3) is interrupted by the maintenance Mx. Further, during the waiting state Wx, the maintenance Mx needs to be waited for.

[0175] On the other hand, the scheduling device 10B changes the scheduling data 41 as necessary. Specifically, the scheduling correction section 16 sets the timing at which a specific task is completed as the start timing of the maintenance Mx in the case where the maintenance Mx is required in the execution of the task, and thereby does not perform the maintenance Mx in the execution of the task. Further, the scheduling correction section 16 switches the execution order of the tasks in such a manner that the completion time of the tasks by both the No. 1 machine and the No. 2 machine is earlier than the completion time achieved by the scheduling data 41 before the change.

[0176] The scheduling correction section 16 here moves the maintenance Mx which becomes the maintenance required period TM21 in the execution of the task (3) to after the completion of the task (1). In the comparative example, the scheduled completion date and time of the maintenance Mx set by the scheduling device 10B is shown by the date and time T6. Figure 16

[0177] Further, the scheduling correction section 16 shortens the completion time of the entire tasks (1) to (4) by switching the task (3) which overlaps the maintenance required period TM21 in the No. 1 machine and the predetermined task (4) which is executed in the No. 2 machine.

[0178] The scheduling correction section 16 changes the timing of the maintenance Mx, and updates the scheduling data 41 with the new scheduling after the task (3) and the task (4) are switched.

[0179] ​​Thus, in the updated schedule data 41, the order of execution of the processes by the task (1), the maintenance Mx, and the task (4) is set for the No. 1 machine. In addition, the order of execution of the processes by the tasks (2) and (3) is set for the No. 2 machine. In Figure 16 In the example, the scheduled completion date and time of the task (4) set by the scheduling device 10B is shown by the date and time T7, and the scheduled completion date and time of the task (3) is shown by the date and time T8.

[0180] In the case where the updated schedule data 41 is used, after the task (1) is executed by the No. 1 machine, the maintenance Mx of the No. 1 machine is performed, and then the task (4) is executed by the No. 1 machine. In addition, the tasks are executed by the No. 2 machine in the order of the tasks (2) and (3). In Figure 16 In the example, the delay time from the plan in the case where the scheduling device of the comparative example executes the tasks (1) to (4) without correcting the schedule data 41 is shown by the time D1. The delay time from the plan is the delay time from the plan in the case where the maintenance Mx is added to the plan in the case where there is no maintenance Mx. In addition, in Figure 16 In the example, the delay time from the plan in the case where the scheduling device 10B executes the tasks (1) to (4) by correcting the schedule data 41 is shown by the time D2.

[0181] As described above, the schedule correction section 16 corrects the schedule data 41 to schedule data 41 in which the task does not stop due to the maintenance Mx in the execution of the task in the case where it is predicted that the task stops due to the maintenance Mx in the execution of the task.

[0182] In the embodiment 1, the maintenance operator needs to consider and execute a countermeasure against the task stop. In the embodiment 2, the scheduling device 10B corrects the schedule data 41 as a countermeasure against the task stop, and thus the maintenance operator does not need to consider the countermeasure against the task stop.

[0183] In addition, the scheduling device 10B feeds back the execution state of the processing, can automatically update the schedule data 41, and thus can always provide appropriate schedule data 41. For example, in an automated system configured to enable the robot to execute the schedule such as the changeover adjustment of the processing machine 81 even without the maintenance operator, the effect of the automatic update of the schedule data 41 by the scheduling device 10B is great.

[0184] In addition, the scheduling device of the comparative example does not have feedback of the actual performance in the execution of the schedule, and thus in a case where a discrepancy arises between the changeover adjustment information database in which information of changeover adjustment is stored and the actual performance, there is no method of improving the discrepancy. Thus, in a case where the scheduling device of the comparative example is applied, productivity deteriorates. On the other hand, in a case where the scheduling device 10B is applied, there is feedback of the actual performance and automatic reorganization of the schedule, and thus even in a case where a discrepancy arises between changeover adjustment and the actual performance, the discrepancy can be improved. Thus, the scheduling device 10B can prevent deterioration of productivity.

[0185] In addition, the scheduling device 10B advances the completion time at which the last task among the tasks executed by the No. 1 machine and the No. 2 machine is completed by swapping the execution order of the tasks, and thus can shorten the delay from the plan compared to the schedule data 41 created by the scheduling device of the comparative example. In addition, the scheduling device 10B moves the timing of maintenance Mx in such a manner that the timing of maintenance Mx does not overlap with the tasks, and thus can avoid interruption of the tasks.

[0186] In a case where the processing machine 81 is a wire electric discharge machine, there are a wire, a processing liquid filter, ion exchange resin, and the like as consumables. These consumables reach the life span at a certain timing, and thus are replaced at a certain timing. The schedule correction section 16 can change the replacement timing of the consumable to the period in which maintenance Mx is performed in a case where the remaining life span of the consumable is shorter than a certain period. The certain period is set in the scheduling device 10B by the user in advance. Thus, the replacement of the consumable with a short remaining life span is performed at the time of maintenance Mx. For example, in a case where the remaining life span of the wire at the time of the date and time T1 at which maintenance Mx is started is shorter than a certain period, the schedule correction section 16 can set the date and time T1 as the replacement timing of the wire.

[0187] As described above, in Embodiment 2, the scheduling device 10B corrects the schedule data 41 so that the stop period is not in the execution of the task on the basis of the schedule data 41, the period in which maintenance is required, and the stop period in a case where the stop period is in the execution of the task. Thus, it is possible to prevent maintenance from being performed in the execution of the task performed by the processing machine 81, and thus it is possible to shorten the total execution time of the task composed of a plurality of tasks.

[0188] Embodiment 3.

[0189] Next, the use of the learning device and the inference device will be described. Figure 16 Embodiment 3 will be described. In Embodiment 3, the learning device learns the schedule data 41 that shortens the time until the completion of the task, and the inference device infers the schedule data 41 that shortens the time until the completion of the task.

[0190] <Learning Phase>

[0191] Figure 17 to Figure 20 is a diagram showing a structure of a learning device according to Embodiment 3. The learning device 50 is a computer that learns input schedule data 41 of a task so that a completion time of all tasks is shortened. The learning device 50 can be provided in the scheduling device 10A or the scheduling device 10B, or outside the scheduling devices 10A and 10B.

[0192] The learning device 50 includes a data acquisition unit 51 and a model generation unit 52. The learning device 50 is connected to the trained model storage unit 70.

[0193] The data acquisition unit 51 acquires, as learning data, the required maintenance period data 42 of the processing machine 81 calculated based on the life prediction period of each consumable or the maintenance period of each component of the processing machine 81. In addition, the data acquisition unit 51 acquires, as learning data, the schedule data 41 created based on the estimated time of processing of each task. In addition, the data acquisition unit 51 acquires, as learning data, the operation performance data 45 of the processing machine 81 in the case where the task of the processing machine 81 and the maintenance are performed based on the required maintenance period data 42 and the schedule data 41.

[0194] That is, the data acquisition unit 51 acquires, as learning data, the required maintenance period data 42, the schedule data 41, and the schedule operation performance data 45. The data acquisition unit 51 transmits the learning data to the model generation unit 52.

[0195] The model generation unit 52 learns the schedule data 41 based on the learning data so that the completion date and time of the last task to be completed among the schedule data 41 of all the processing machines 81 is shortened. In other words, the model generation unit 52 learns the schedule data 41 based on the learning data including the required maintenance period data 42, the schedule data 41, and the schedule operation performance data 45. That is, the model generation unit 52 generates, from the required maintenance period data 42 and the schedule data 41, a trained model 71 for inferring the schedule data 41 that can shorten the completion date and time of all the tasks.

[0196] The learning algorithm used by the model generation unit 52 can use known algorithms such as teacher-aided learning, teacherless learning, reinforcement learning, and the like. As one example, a case in which reinforcement learning is applied will be described. In reinforcement learning, an agent (action subject) within a certain environment observes a current state (parameter of the environment) and decides on an action that should be taken. The environment dynamically changes through the action of the agent, and a reward is given to the agent in correspondence with the change in the environment. The agent repeats this action, and through a series of actions, the action policy that gives the most reward is learned. As representative methods of reinforcement learning, Q-learning and TD-learning are known. For example, in the case of Q-learning, a general update formula of an action value function Q(s, a) is expressed by the following equation (1).

[0197] [Equation 1]

[0198]

[0199] In equation (1), s t represents the environment at time t, a t represents an action at time t. Through the action a t , the state (environment) becomes s t+1 . r t+1 represents a reward resulting from the change in state, γ represents a discount rate, and α represents a learning coefficient. Furthermore, γ is in the range of 0 < γ ≤ 1, and α is in the range of 0 < α ≤ 1. The schedule operation execution result data 45 becomes the action a t , the maintenance period data 42 and the schedule data 41 become the state s t , and the learning device 50 learns the best action a t in the state st at time t. The schedule operation execution result data 45 corresponds to the corrected schedule data 41. Therefore, the learning device 50 learns the best corrected schedule data 41. In other words, the learning device 50 learns the corrected schedule data 41 in such a way that the entire execution time of the task can be shortened.

[0200] The update formula expressed by equation (1) is such that if the action value Q of the action a that gives the highest Q value at time t + 1 is greater than the action value Q of the action a performed at time t, the action value Q is increased, and in the opposite case, the action value Q is decreased. In other words, the learning device 50 updates the action value function Q(s, a) in such a way that the action value Q of the action a at time t approaches the best action value Q at time t + 1. As a result, the best action value Q in a certain environment is continuously and sequentially propagated to the action value Q in the previous environment.

[0201] As shown above, in a case where the model generation section 52 generates the trained model 71 through reinforcement learning, the model generation section 52 has a reward calculation section 53 and a function update section 54.

[0202] The reward calculation section 53 calculates the reward r based on the completion date and time of the last task calculated from the scheduled operation execution result data 45. For example, the reward calculation section 53 increases the reward r (for example, gives a reward of "1") in a case where the completion date and time of the last task is early, and on the other hand, decreases the reward r (for example, gives a reward of "-1") in a case where the completion date and time of the last task is late.

[0203] The function update section 54 updates the function for deciding the corrected schedule data 41 corresponding to the scheduled operation execution result data 45 in accordance with the reward calculated by the reward calculation section 53, and outputs to the trained model storage section 70. For example, in a case of Q-learning, the function update section 54 uses the action value function Q(s t , a t ) represented by Expression (1) as a function for calculating the schedule data 41.

[0204] The learning device 50 repeatedly performs the above learning. The trained model storage section 70 stores the action value function Q(s t , a t ) updated by the function update section 54, that is, the trained model 71.

[0205] Next, the learning processing procedure of the schedule data 41 performed by the learning device 50 will be described using Figure 17 , which is a flowchart showing the processing procedure of the learning processing performed by the learning device according to Embodiment 3. Figure 18

[0206] The data acquisition section 51 acquires the required maintenance period data 42, the schedule data 41, and the scheduled operation execution result data 45 as learning data (Step S410).

[0207] The model generation section 52 calculates the reward based on the required maintenance period data 42, the schedule data 41, and the scheduled operation execution result data 45 (Step S420). Specifically, the reward calculation section 53 acquires the required maintenance period data 42, the schedule data 41, and the scheduled operation execution result data 45, and judges whether to increase the reward or decrease the reward based on a predetermined reward reference, that is, the completion date and time of the last task.

[0208] ​If the return calculation unit 53 determines that the return should be increased (step S420, return increase benchmark), it increases the return (step S430). On the other hand, if the return calculation unit 53 determines that the return should be decreased (step S420, return decrease benchmark), it decreases the return (step S440).

[0209] Based on the reward calculated by the reward calculation unit 53, the function update unit 54 updates the action value function Q(s) represented by equation (1) stored in the trained model storage unit 70. t a t Update (step S450).

[0210] The learning device 50 repeats the steps from S410 to S450 above, so that the generated action value function Q(s) is... t a t It is stored in the trained model storage unit 70 as a trained model 71.

[0211] Furthermore, in Embodiment 3, the learning device 50 stores the trained model 71 in a trained model storage unit 70 provided externally to the learning device 50, but the trained model storage unit 70 may also be configured inside the learning device 50.

[0212] <Effective Use Phase>

[0213] Figure 18 This is a diagram showing the structure of the inference device according to Embodiment 3. The inference device 60 is a computer that uses a trained model 71 to infer the corrected scheduling data 41 based on the period data 42 and scheduling data 41 maintained as needed.

[0214] The inference device 60 infers the revised scheduling data 41, which shows a shortened completion time for the final task. The inference device 60 can be located within the scheduling device 10A or 10B, or external to either scheduling device 10A or 10B. In Embodiment 3, the case where the inference device 60 is located within the scheduling device 10A will be described.

[0215] The inference device 60 has a data acquisition section 61 and an inference section 62. The data acquisition section 61 acquires the required maintenance period data 42 and the schedule data 41. The inference section 62 performs inference on the corrected schedule data, that is, the corrected schedule data 41X, using the trained model 71, and outputs the corrected schedule data 41X to the stop period output section 15. That is, the inference section 62 inputs the required maintenance period data 42 and the schedule data 41 acquired by the data acquisition section 61 to the trained model 71, and thereby can perform inference on the corrected schedule data 41X in which the completion time of the last task is shortened. The stop period output section 15 causes the corrected schedule data 41X to be displayed on the display device 83.

[0216] Further, in Embodiment 3, the case where the inference device 60 performs inference on the corrected schedule data 41X using the trained model 71 trained by the model generation section 52 is described, but the inference device 60 can acquire the trained model 71 from a learning device other than the learning device 50, and perform inference on the corrected schedule data 41X based on the trained model 71.

[0217] Next, the processing procedure of the processing performed by the inference device 60 to perform inference on the corrected schedule data 41X will be described. Figure 19 Figure 20 Figure 20 is a flowchart showing the processing procedure of the inference processing performed by the inference device according to Embodiment 3.

[0218] The data acquisition section 61 acquires the required maintenance period data 42 and the schedule data 41 as inference data (Step S510). The inference section 62 inputs the inference data, that is, the required maintenance period data 42 and the schedule data 41, to the trained model 71 stored in the trained model storage section 70 (Step S520), and obtains the corrected schedule data 41X. The inference section 62 outputs the obtained data, that is, the corrected schedule data 41X, to the stop period output section 15 (Step S530). The stop period output section 15 causes the corrected schedule data 41X to be displayed on the display device 83.

[0219] The control device 82 of the processing machine 81 performs tasks in accordance with the corrected schedule data 41X. In addition, the maintenance worker performs maintenance in accordance with the corrected schedule data 41X. Thereby, the inference device 60 can shorten the time until all the tasks are completed.

[0220] Further, in Embodiment 3, the case where reinforcement learning is applied to the learning algorithm used in the inference section 62 is described, but the present embodiment is not limited thereto. As for the learning algorithm, in addition to reinforcement learning, teacher learning, unsupervised learning, or semi-supervised learning, or the like can be applied.

[0221] ​Further, as the learning algorithm used in the model generation section 52, Deep Learning that learns extraction of the feature amount itself can also be used, and the model generation section 52 can also perform machine learning in accordance with other publicly known methods, such as a neural network, genetic programming, functional logic programming, a support vector machine, and the like.

[0222] Further, the learning device 50 and the inference device 60 can also be devices that are independent of the dispatch device 10A and are connected to the dispatch device 10A via a network, for example. Further, the learning device 50 and the inference device 60 can also exist on a cloud server.

[0223] Further, the model generation section 52 can also learn the revised dispatch data 41X using learning data acquired from a plurality of control devices 82 and a plurality of dispatch devices 10A. Further, the model generation section 52 can also learn the revised dispatch data 41X using learning data collected from a plurality of control devices 82 and a plurality of dispatch devices 10A that independently operate in different regions. Further, the learning device 50 can also add or remove a control device 82 or a dispatch device 10A that collects learning data from the middle of the object. Further, the learning device 50 that has learned the revised dispatch data 41X with respect to a certain control device 82 or dispatch device 10A can be applied to other control devices 82 or dispatch devices 10A that are different from the control device 82 or the dispatch device 10A, and the trained model 71 can be updated by relearning the revised dispatch data 41X with respect to the other control devices 82 or dispatch devices 10A.

[0224] Further, the learning device 50 can generate a trained model 71 for inferring the revised dispatch data 41X in which the proportion of the total task execution time of all of the processing machines 81 in a specified period specified by a user is increased. In other words, the learning device 50 can generate a trained model 71 for inferring the revised dispatch data 41X that can shorten the total maintenance time of all of the processing machines 81 in the specified period specified by the user.

[0225] In this case, the inference device 60 infers the revised dispatch data 41X in which the proportion of the total task execution time of all of the processing machines 81 in a specified period specified by a user is increased, using the trained model 71 generated by the learning device 50.

[0226] The trained model 71, in a case where the trained model 71 is a model capable of shortening the total of the maintenance times of all the processing machines 81 within the specified period designated by the user, the learning device 50 and the inference device 60 are capable of increasing the operation rate of the entire processing machines 81 within the specified period designated by the user.

[0227] In addition, the learning device 50 can also generate a trained model 71 that ensures the delivery date of each task and infers correction schedule data 41X in which the number of tasks performed by all the processing machines 81 within the specified period designated by the user is increased. Thus, the learning device 50 is capable of increasing the number of tasks performed within the specified period designated by the user.

[0228] In this case, the inference device 60 infers correction schedule data 41X in which the number of tasks performed by all the processing machines 81 within the specified period designated by the user is increased, using the trained model 71 generated by the learning device 50.

[0229] As described above, according to Embodiment 3, the trained model 71 infers correction schedule data 41X that shortens the time until completion of the last task execution in the correction schedule data 41X, and thus is capable of shortening the time until completion of the last task execution. Thus, the processing machines 81 are capable of shortening the total execution time of tasks.

[0230] In addition, the trained model 71 infers correction schedule data 41X in which the proportion of the task execution time of all the processing machines 81 within the specified period is increased, and thus is capable of increasing the proportion of the task execution time of all the processing machines 81 within the specified period. Thus, the processing machines 81 are capable of efficiently performing tasks.

[0231] In addition, the trained model 71 infers correction schedule data 41X that ensures the delivery date of each task and increases the number of tasks performed by all the processing machines 81 within the specified period, and thus is capable of increasing the number of tasks performed by all the processing machines 81 within the specified period. Thus, the processing machines 81 are capable of efficiently performing tasks.

[0232] The structures shown in the above embodiments represent one example, and can also be combined with other known technologies, can also be combined with each other, and can omit or change a part of the structures without departing from the gist.

[0233] Explanation of Reference Signs

[0234] 10A, 10B scheduling device, 11 schedule creation unit, 12 period information creation unit, 13 required maintenance period calculation unit, 14 stop period calculation unit, 15 stop period output unit, 16 schedule correction unit, 20 user specification information, 21 processing instruction information, 22 workpiece ID information, 23 processing program ID information, 24 corresponding time information, 31 consumable life prediction period, 32 component maintenance period, 40 period information, 41 schedule data, 41X corrected schedule data, 42 required maintenance period data, 45 schedule operation execution result data, 50 learning device, 51, 61 data acquisition unit, 52 model generation unit, 53 reward calculation unit, 54 function update unit, 60 inference device, 62 inference unit, 70 trained model storage unit, 71 trained model, 81 processing machine, 82 control device, 83 display device, 100 processor, 200 memory, 300 input device, 400 output device.

Claims

1. A scheduling device that creates scheduling data representing a schedule for inputting a plurality of tasks to a wire electric discharge machine, the scheduling device characterized by having: a schedule creating section that creates the scheduling data based on a processing estimation time obtained by estimating a time required for processing of each task; a required maintenance period calculating section that calculates, for the wire electric discharge machine, a period during which maintenance is required, i.e., a required maintenance period, based on a life prediction period, i.e., a period predicted from a life of a consumable used in processing by the wire electric discharge machine and a consumable consumed in processing and non-processing, and a maintenance period, i.e., a period of maintenance of a component possessed by the wire electric discharge machine; a stop period calculating section that calculates, based on the scheduling data and the required maintenance period, a period during which processing by the wire electric discharge machine is stopped due to the maintenance in execution of the tasks, i.e., a stop period; and an output section that outputs the stop period to an external device.

2. A scheduling device that creates scheduling data representing a schedule for inputting a plurality of tasks to a wire electric discharge machine, the scheduling device characterized by having: a schedule creating section that creates the scheduling data based on a processing estimation time obtained by estimating a time required for processing of each task; a required maintenance period calculating section that calculates, for the wire electric discharge machine, a period during which maintenance is required, i.e., a required maintenance period, based on a life prediction period, i.e., a period predicted from a life of a consumable used in processing by the wire electric discharge machine and a consumable consumed in processing and non-processing, and a maintenance period, i.e., a period of maintenance of a component possessed by the wire electric discharge machine; a stop period calculating section that calculates, based on the scheduling data and the required maintenance period, a period during which processing by the wire electric discharge machine is stopped due to the maintenance in execution of the tasks, i.e., a stop period; and a schedule correcting section that, in a case where the stop period is in execution of an arbitrary task among the tasks, corrects the scheduling data so that the stop period is not in execution of the task based on the scheduling data, the required maintenance period, and the stop period.

3. The scheduling device according to claim 2, characterized in that: the schedule creating section creates the scheduling data for the wire electric discharge machine respectively, the schedule correcting section corrects the scheduling data by exchanging a task in which the stop period is not in execution, i.e., a replacement task, and a task in which the stop period is in execution, i.e., an execution-in-task, in a case where the replacement task can be extracted from all the tasks of the wire electric discharge machine, or corrects the scheduling data by setting the maintenance to before the execution-in-task and setting the execution-in-task after the maintenance in a case where the replacement task cannot be extracted.

4. The scheduling device according to claim 3, characterized in that: The schedule correction section corrects the schedule data so that the date and time at which the task ends last among all the schedule data is brought forward.

5. The scheduling device according to any one of claims 2 to 4, wherein The schedule correction section corrects the schedule data based on a priority level set in the task.

6. The scheduling device according to any one of claims 2 to 4, wherein The schedule creation section updates the processing estimated time of the task in progress in real time in execution of the schedule corresponding to the schedule data, The required maintenance period calculation section updates the required maintenance period in real time in execution of the schedule corresponding to the schedule data, The schedule correction section corrects the schedule data based on the updated processing estimated time and the updated required maintenance period.

7. The scheduling device according to any one of claims 2 to 4, wherein The schedule correction section compares a first date and time at which the task ends last in the schedule data before correction and a second date and time at which the task ends last in the schedule data after correction, thereby calculating an improvement degree of the schedule data achieved by correction, The improvement degree is output to an external device.

8. The scheduling device according to any one of claims 1 to 4, wherein The required maintenance period calculation section calculates the required maintenance period while distinguishing between processing and non-processing for each consumable.

9. The scheduling device according to claim 8, wherein The required maintenance period calculation section calculates the required maintenance period based on the life of all consumables in the case of performing processing and based on the life of consumables also consumed in non-processing in the case of not performing processing.

10. The scheduling device according to claim 3, wherein The schedule correction section exchanges the alternative task extracted from the schedule data created for a first wire electric discharge machine among the wire electric discharge machines and the in-progress task extracted from the schedule data created for a second wire electric discharge machine among the wire electric discharge machines, thereby correcting the schedule data.

11. A computer-readable recording medium storing a program for executing the operation of the scheduling device according to claim 1 or 2.

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