CN111062552A - Scheduling method of intelligent scheduling system - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 36
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- 238000004364 calculation method Methods 0.000 description 8
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- 101100203600 Caenorhabditis elegans sor-1 gene Proteins 0.000 description 3
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- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention provides a scheduling method of an intelligent scheduling system, which comprises the steps of (a) receiving basic data provided by a user on a user interface, (b) establishing an initialization model based on the basic data, (c) carrying out simulation operation based on the initialization model, and (d) outputting a result of the simulation operation. Therefore, the intelligent dispatching system can establish a virtual factory model which accords with the real situation in a digital computer, solves the problem that the conventional dispatching system can not calculate the dispatching which completely accords with the actual situation, and assists a user to be used as decision support, and is quicker and more accurate compared with the conventional method.
Description
Technical Field
The present invention relates to a scheduling method, and more particularly, to a scheduling method of an intelligent scheduling system.
Background
Generally, a factory is faced with many unexpected emergency situations during practical operation, such as rapid order insertion, unexpected product yield, equipment breakdown, shortage of raw materials, etc., which are all related to the scheduling of factory production, however, in the past known production scheduling methods, because the factors of the real environment are too complex, it is often difficult to calculate by mathematical modeling, most of the available planning methods are calculation methods using capacity stack and adding constraints to calculate and solve, and the results obtained by such methods are far from the real situation and time consuming.
Disclosure of Invention
In view of the above, the present invention provides a scheduling method for an intelligent scheduling system, which can establish a virtual factory model conforming to the real situation in a digital computer, and solve the problem that the conventional scheduling system cannot calculate the scheduling completely conforming to the actual situation.
To achieve the above object, the present invention provides a scheduling method of an intelligent scheduling system, which comprises the following steps: the method comprises the steps of (a) receiving basic data provided by a user on a user interface, (b) establishing an initialization model based on the basic data, (c) performing simulation operation based on the initialization model, and (d) outputting a simulation operation result.
The scheduling method of the intelligent scheduling system belongs to an object-oriented modular development mode, can quickly establish a user-defined virtual factory model for a user, assists the user in decision support, and is quicker and more accurate compared with the conventional method.
The details of the present invention will be described in the following detailed description. However, it will be understood by those skilled in the art that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention as defined by the appended claims.
Drawings
FIG. 1 is a flow chart of a scheduling method of the intelligent scheduling system of the present invention;
FIG. 2 is a schematic flow chart illustrating a scheduling method of the intelligent scheduling system according to the present invention;
fig. 3 to fig. 10 are schematic views illustrating the operation of the interface of the intelligent scheduling system of the present invention.
[ Main element ]
S1-step (a); s2-step (b); s3-step (c); s4-step (d).
Detailed Description
To illustrate the implementation of the present invention in more detail, a semiconductor package and test example will be described, however, for other possible embodiments, the scheduling method of the intelligent scheduling system is also applicable to other industries, such as semiconductor manufacturing, led panel, metal processing assembly, etc. Referring to fig. 1, a flowchart of a scheduling method of an intelligent scheduling system according to a preferred embodiment of the present invention is shown, where the scheduling method of the intelligent scheduling system includes the following steps: (a) receiving basic data provided by a user in a user interface, establishing an initialization model based on the basic data in step (b), performing a simulation operation based on the initialization model in step (c), and outputting the result of the simulation operation in step (d).
Referring to fig. 2, in the present embodiment, the step (a) includes the step (a1) of inputting the basic data, the step (a2) of adjusting the basic data, and the step (a3) of collating the basic data. In more detail, the step (a1) is that the user inputs basic data in an interface of the intelligent scheduling system, the basic data includes equipment information as shown in tables 1 to 5, product route information as shown in tables 6 to 7, and order information as shown in table 8.
The following table 1 is a work center group table, and the required data is field settable parameters of machine name, processing time, maintenance time, work center name, replaceable work center name, interval upper limit, interval lower limit, key index assignment and priority index assignment, and the use priority of the machine is determined.
TABLE 1 work center group form
TABLE 1 work center group form (continuation)
| Upper limit of interval | Lower limit of interval | Key index dispatching | |
| 2 | 1 | ||
| 2 | 1 | ||
| 2 | 1 | ||
| 2 | 1 | ||
| 2 | 1 | ||
| 2 | 1 | ||
| 2 | 1 |
The following table 2 is a table of the internal structure of a machine, which can set the machine name, machine type, loading size, loading lock, manufacturing slot, and processing lot of the machine.
TABLE 2 Table of the internal structure of the machine
| Name of machine | Class of machine | Carrying size | Load lock | Manufacturing tank | Processing batches |
| SOR_1 | Limited | 3 | 2 | 6 | 1 |
| SOR_2 | Limited | 3 | 2 | 4 | 1 |
| SOR_3 | Limited | 3 | 3 | 1 | |
| STH_1 | Limited | 3 | 3 | 1 | |
| STH_2 | Limited | 2 | 3 | 1 | |
| SCRA_1 | Limited | 3 | 3 | 1 | |
| SCRA_2 | Limited | 3 | 3 | 1 | |
| MIC_1 | Limited | 3 | 3 | 1 | |
| MIC_2 | Limited | 3 | 3 | 1 | |
| DESD_1 | Limited | 2 | 2 | 4 | |
| DESD_2 | Limited | 2 | 2 | 4 |
The following table 3 is a table of machine production time, which can set the machine number, production item name, processing stage, processing technology, processing parameter, production rate, production line classification, production lot, production line personnel group, production line personnel number, production line personnel man-hour, LP _ man-hour calculation mode, PT _ man-hour calculation mode, DL _ man-hour calculation mode, LP _ production time, PT _ production time, DL _ production time, and other relevant settings for each machine. Taking FV-4 in Table 3 as an example, when the item PD _1 is produced to the processing technology of the serial number 125, it takes 36 seconds to process one PD _ 1; in the production of the item PD _1 to the processing of the reference numeral 130, it takes 60 seconds to process one PD _ 1.
TABLE 3 production time table
| Machine station number | Name of production item | Stage of processing | Processing technology | Processing parameters | Production rate |
| FV_4 | PD_1 | P11 | 125 | |
1 |
| FV_4 | PD_1 | P11 | 130 | |
1 |
| FV_4 | PD_1 | BPFV | 590 | Recipe 60 | 1 |
| FV_4 | PD_10 | BPFV | 540 | Recipe 124 | 1 |
| FV_4 | PD_11 | BPFV | 540 | Recipe 188 | 1 |
| FV_4 | PD_12 | P11 | 115 | Recipe 208 | 1 |
TABLE 3 production time table of machine (continue)
| Production line classification | Mass production | Production line personnel group | Number of production line personnel |
| F | 1 | ||
| F | 1 | ||
| F | 1 | ||
| F | 1 | ||
| F | 1 | ||
| F | 1 |
TABLE 3 production time table of machine (continue)
TABLE 3 production time table of machine (continue)
The following table 4 is a table of the production time of the machine, which can set the related settings of the machine name, the cycle start time, the cycle shutdown time, the absolute start time, the absolute shutdown time, etc. for each machine. Taking the machine SOR-1 in table 4 as an example, the machine SOR-1 will automatically start up at 8 am 00 min 05 sec every day, and the machine can perform production until it automatically shuts down at 5 pm 00 min, and the machine cannot perform production.
TABLE 4 production time chart of machine
The following table 5 is a table of machine tool crashes, which may set the machine name, routine, interval, In _ P1, In _ P2, upper limit of crashes, lower limit of crashes, duration, upper limit of crashes, lower limit of crashes, etc.
TABLE 5 Table of machine crash
| Name of machine | Routine nature | Crash interval | In_P1 | In_P2 |
| ACE01 | TRUE | normal | 2:00:39:30.0300 | 1:09:31:31.1400 |
| ACE01-PM1 | TRUE | normal | 51:13.4 | 06:37.1 |
| ACE01-PM2 | TRUE | normal | 5731.8 | 29:28.2 |
| ACE01-PM3 | TRUE | normal | 13:39.9 | 13:53.0 |
TABLE 5 Table crash form (continue)
| Upper limit of crash | Lower limit of crash | Duration of time | P1_ duration |
| 09:01.0 | 12:07:14:09.0000 | normal | 28:47.6 |
| 19:16.0 | 8:04:07:23.0000 | normal | 26:53.4 |
| 08:34.0 | 4:03:46:13.0000 | normal | 34:02.0 |
| 19:53.0 | 3:23:21:39.0000 | normal | 27:24.1 |
TABLE 5 Table crash form (continue)
| P2_ duration | Upper limit of crash | Lower limit of crash |
| 53:35.4 | 06:51.0 | 24:24.0 |
| 13:35.2 | 06:30.0 | 20:21.0 |
| 41:21.0 | 07:15.0 | 13:38.0 |
| 13:34.3 | 06:30.0 | 03:19.0 |
Because the scheduling method of the intelligent scheduling system is an object-oriented modular development mode, when the machine table form data of tables 1 to 5 is filled, the machine object is also built in the model, and then the machine parameters are included in the production decision when the user performs the scheduling simulation.
The data required by the item route form shown in the following table 6 is information such as item name, process serial number, work center, processing stage, process name, rework rate, workpiece retention time, sampling rate, material quantity per minute, transfer lot, waiting quantity (>), waiting quantity (<), waiting time (seconds) (>), waiting time (seconds) (<), standard man-hour (seconds), sub-part name, sub-part quantity, etc.
TABLE 6 item route sheet
| Name of item | Process number | Work center | Stage of processing | Name of art |
| Part_1 | 020.000 | TGSOR | IQA | TDATA |
| Part_1 | 030.000 | TGSTH | IQA | TTKMA |
| Part_1 | 040.000 | TGSCRA | IQA | TSCRA |
| Part_1 | 050.000 | TGMIC | IQA | TINSA |
TABLE 6, item route form (continuation)
TABLE 6, item route form (continuation)
TABLE 6, item route form (continuation)
| Standard man-hour (seconds) | Name of |
Number of |
Name of |
| 15.6 | |||
| 36 | |||
| 11.1 | |||
| 36 |
The following table 7 shows an item single-time feeding form, in which the required data are information such as client name, object name, item name, starting process, ending process, quantity, priority parameter, customer requirement date, scheduling delivery date, etc.
TABLE 7 single item feed form
| Name of customer | Name of object | Name of item | Starting process | Finishing process |
| 29GA6008.1 | PD_1 | 050.000 | ||
| CFG95612.1 | PD_1 | 630.000 | ||
| CFG95613.1 | PD_1 | 630.000 | ||
| CFG95614.1 | PD_1 | 640.000 |
TABLE 7 single item feed form (continue)
| Number of | Priority order parameter | The day of customer's request | Scheduling delivery period |
| 25 | 6 | 2017/10/1 07:00:00 | 2017/10/5 23:00:00 |
| 25 | 3 | 2017/10/1 07:00:00 | 2017/10/5 06:00:00 |
| 25 | 3 | 2017/10/1 07:00:00 | 2017/10/5 06:00:00 |
| 25 | 3 | 2017/10/1 07:00:00 | 2017/10/5 06:00:00 |
After the basic data required in step (a1) is filled by the user, the user can proceed to step (a2), i.e., modify or adjust the specific individual or batch data, such as adding or deleting data, adding new tools and product items, etc. After the step (a2) is completed, the intelligent dispatching system starts to perform the step (a3), i.e. standardizing the imported data, and loading the standardized data into the intelligent dispatching system for operation.
First, step (b1) is performed: establishing a virtual factory, i.e. the intelligent scheduling system first establishes a virtual factory framework, and then proceeds to step (b 2): creating a group of devices, the intelligent scheduling system grouping according to the functions or characteristics of the devices, such as non-contact surface measurement devices, being put together in the virtual factory, and then performing step (b 3): establishing an equipment model, namely establishing specific characteristics, functions and connection relations with other machines of the individual machine, and then performing the step (b 4): establishing a product model, loading information such as the product item, the name, the processing path, the process and the like of the product, and then performing the step (b 5): receiving the equipment parameters and product parameters, such as processing time, etc. set by the user, and importing these parameters into the system, and then proceeding to step (b 6): the simulation starting time point set by the user is received, so that the current condition of the factory can be simulated to the future production condition.
Then the intelligent dispatching system starts to proceed the step (c1) in sequence: determining the product type, step (c 2): confirm route number, step (c 3): selecting usable equipment, and step (c 4): and (5) simulating production. In this embodiment, the step (c1) can be returned to for recycling after the step (c4) is finished.
After the operation of the simulation production is finished, the intelligent scheduling system carries out the step (d): and outputting the result of the simulation operation, wherein the result comprises the daily information of the equipment, the information of the simulated station passing and the information of the simulated batch. The user can make a decision based on the result.
In practical operation, referring to fig. 3 to 5, a user first opens the top page of the intelligent scheduling system shown in fig. 3, clicks the lower left button (Download Template), downloads the sample files of excel basic data forms, which are shown in tables 1 to 7, fills up the required data, clicks the lower right button (Upload) of the top page to Upload the data to be scheduled, and jumps out of the Upload screen shown in fig. 4, selects the filled excel file and clicks the button (Upload) to Upload, and jumps out of the other screen shown in fig. 5, if the basic data content format is correct, the lower right button (RUN) appears, and starts to execute the simulation scheduling, i.e., execute steps (b) to (d).
When the user inputs data, trial calculation can be carried out according to different unit roles such as purchasing, production management, business and the like. For purchasing personnel, the main use requirements are planning and issuing of the feeding schedule; for production management personnel, the main requirements are order trial calculation, material planning, the total production number target of the machine group and trial calculation of the capacity; the order delivery trial calculation and the order production progress inquiry are the most important use requirements for business personnel. Therefore, for the buyer, the data import includes material information, such as feed planning and inventory information, in addition to the basic data; for production management personnel, besides basic data, machine parameters and a process machine group are further required to be input; in addition to the basic data, the business personnel further need to input order items and order quantity.
The intelligent dispatching system is realized by simulating a real planning method in a computer simulation operation way, after the intelligent dispatching system converts data input by a user into a virtual factory, after the virtual material, the virtual manpower and the virtual machine are put into operation, the simulation production is carried out to generate a simulation planning result, the data initialization is to establish all the required virtual objects including equipment, manpower, material, orders and the like, and restore the objects to the state at the scheduled time point, e.g., want to start scheduling at 2018/8/18:00, then all current data, such as equipment, equipment status, material in use, order in process, manpower attendance, manpower status …, etc. need to be collected 2018/8/18:00, which facilitates the initialization of the model and the more accurate the result will be when the data can be collected; after all the data models are initialized, planning production is started, and capacity planning is performed according to the current conditions of the factories, the conditions of the materials and the manpower, and the dispatching logics of the factories, wherein if the data cannot be completely collected, the average value or the estimated value can be used as an input parameter.
After the virtual scheduling is finished, a detailed work execution plan is generated, the content of which includes start time, end time, work order code, cycle time, whether to reach the delivery … on time, etc., and these data become reference bases for user, company or enterprise decision, so as to know the usage planning of factory resources, forecast work order production data and machine statistics, etc. For example, as shown in fig. 6, the service personnel may query the detailed information of the customer order from the output information of the order list, and may query information such as a predicted start date (FeedDate), an expected completion date (SOD), and an earliest completion date (FinishingTime). For example, as shown in fig. 7 and 8, the production manager can use the cluster dispatching record and the scheduling planning output information to know and record the output targets of each day, such as the Machine group Name (ToolGroup _ Name), the Target completion date (DueDate), the Target Quantity (Target _ Quantity), etc., and the Machine dispatching plan, the real-time order production schedule, such as the work order number (LotID), the work order start time (TrackIn), the work order end time (trackouttime), the processing Machine (Machine), etc. If the delivery period of a certain factory is too long on average, the production manager can utilize the bottleneck cluster production data, such as cluster utilization rate (UTIL) in fig. 9, find the bottleneck station machine group, on the one hand, monitor, and on the other hand, can perform production simulation after adding machines in the virtual factory, and can check whether the capacity bottleneck is solved without actually purchasing new machines, thereby avoiding the factory from bearing unnecessary investment loss, and even if a new machine is added in the virtual factory, the connection relationship between the new machine and other sites or machines does not need to be established on the system in additional time, so that a lot of time can be saved for users. Finally, the buyer can use the output information of the material planning to know the information of the amount of stock (Inventory _ Qty), the Status of stock (Status), the amount of stock change (modification _ Qty), etc., as shown in fig. 10.
It should be noted that the detailed description provided above with reference to the drawings is only an embodiment provided for illustrating the technical contents and features of the present invention, and those skilled in the art of the present invention should be able to make various simple modifications, substitutions or component reductions without departing from the spirit of the present invention after understanding the technical contents and features of the present invention, and therefore, the present invention should fall within the claims of the present disclosure.
Claims (6)
1. A scheduling method of an intelligent scheduling system is characterized by comprising the following steps:
(a) receiving basic data provided by a user on a user interface;
(b) establishing an initialization model based on the basic data;
(c) performing simulation operation based on the initialization model; and
(d) and outputting the result of the analog operation.
2. The method of claim 1, wherein the step (a) comprises the steps of (a1) inputting basic data, step (a2) adjusting the basic data, and step (a3) collating the basic data.
3. The scheduling method of claim 1 wherein the step (b) of establishing the initialization model comprises the steps of (b1) establishing a virtual factory, step (b2) establishing a device group, step (b3) establishing a device model, step (b4) establishing a product model, step (b5) receiving user-set device parameters and product parameters, and step (b6) receiving user-set simulation start time.
4. The scheduling method of claim 1 wherein the simulation operation of step (c) comprises determining a product type of step (c1), confirming a route number of step (c2), selecting available equipment of step (c3), and simulating production of step (c 4).
5. The scheduling method of claim 1 wherein the result of step (d) includes daily information of equipment, information simulating station passing, and information simulating batch size.
6. The method of claim 4, wherein the step (c1) is repeated after the step (c4) is completed.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002351950A (en) * | 2001-05-28 | 2002-12-06 | Dainippon Printing Co Ltd | Schedule setting system |
| JP2002373012A (en) * | 2001-06-14 | 2002-12-26 | Ntn Corp | Method for designing/working plant facility and supporting system |
| US7003475B1 (en) * | 1999-05-07 | 2006-02-21 | Medcohealth Solutions, Inc. | Computer implemented resource allocation model and process to dynamically and optimally schedule an arbitrary number of resources subject to an arbitrary number of constraints in the managed care, health care and/or pharmacy industry |
| JP2007183817A (en) * | 2006-01-06 | 2007-07-19 | Sumitomo Heavy Ind Ltd | Scheduling device, scheduling method, scheduling program, and recording medium with the program recorded thereon |
| CN108038605A (en) * | 2017-12-05 | 2018-05-15 | 深圳市智物联网络有限公司 | The PLARD methods and PLARD systems of a kind of wisdom factory |
-
2018
- 2018-10-17 CN CN201811211832.2A patent/CN111062552A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7003475B1 (en) * | 1999-05-07 | 2006-02-21 | Medcohealth Solutions, Inc. | Computer implemented resource allocation model and process to dynamically and optimally schedule an arbitrary number of resources subject to an arbitrary number of constraints in the managed care, health care and/or pharmacy industry |
| JP2002351950A (en) * | 2001-05-28 | 2002-12-06 | Dainippon Printing Co Ltd | Schedule setting system |
| JP2002373012A (en) * | 2001-06-14 | 2002-12-26 | Ntn Corp | Method for designing/working plant facility and supporting system |
| JP2007183817A (en) * | 2006-01-06 | 2007-07-19 | Sumitomo Heavy Ind Ltd | Scheduling device, scheduling method, scheduling program, and recording medium with the program recorded thereon |
| CN108038605A (en) * | 2017-12-05 | 2018-05-15 | 深圳市智物联网络有限公司 | The PLARD methods and PLARD systems of a kind of wisdom factory |
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Application publication date: 20200424 |