CN113627759A - Dynamic scheduling method for manufacturing resources of mixed line manufacturing system - Google Patents
Dynamic scheduling method for manufacturing resources of mixed line manufacturing system Download PDFInfo
- Publication number
- CN113627759A CN113627759A CN202110872263.1A CN202110872263A CN113627759A CN 113627759 A CN113627759 A CN 113627759A CN 202110872263 A CN202110872263 A CN 202110872263A CN 113627759 A CN113627759 A CN 113627759A
- Authority
- CN
- China
- Prior art keywords
- manufacturing
- data
- time
- scheduling
- manufacturing system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 194
- 238000000034 method Methods 0.000 title claims abstract description 83
- 230000008569 process Effects 0.000 claims abstract description 47
- 238000010276 construction Methods 0.000 claims abstract description 26
- 238000005457 optimization Methods 0.000 claims abstract description 20
- 230000007246 mechanism Effects 0.000 claims abstract description 15
- 238000002360 preparation method Methods 0.000 claims abstract description 8
- 238000012423 maintenance Methods 0.000 claims abstract description 5
- 230000008707 rearrangement Effects 0.000 claims description 20
- 238000004891 communication Methods 0.000 claims description 10
- 230000008859 change Effects 0.000 claims description 9
- 238000012544 monitoring process Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 230000003044 adaptive effect Effects 0.000 claims description 4
- 230000003993 interaction Effects 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 2
- 238000012545 processing Methods 0.000 abstract description 13
- 230000004044 response Effects 0.000 abstract description 3
- 230000001960 triggered effect Effects 0.000 abstract description 3
- 238000013461 design Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 239000000284 extract Substances 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06316—Sequencing of tasks or work
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Data Mining & Analysis (AREA)
- Manufacturing & Machinery (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a dynamic scheduling method for manufacturing resources of a mixed line manufacturing system, which comprises the steps of utilizing a data structure to realize the construction of a process model indicator and completing the data modeling of an actual process model of a product; completing data modeling of the corresponding relation between the actual process and the manufacturing resources and the corresponding theoretical process duration; completing the construction and maintenance of the manufacturing resource utilization interval time such as the number of various manufacturing resources, the manufacturing resource preparation time, the product switching time and the like and relevant necessary information; constructing a dynamic scheduling program coder and a dynamic scheduling program decoder; designing and constructing a complete optimization program and setting program parameters; completing the construction of a scheduling scheme solver and solving the scheme; completing the release of the scheduling manufacturing scheme; constructing a detector; constructing a manufacturing system running core data monitor; the reordering mechanism is triggered. The invention provides a complete data acquisition-dynamic scheduling system architecture for the dynamic scheduling of the mixed line manufacturing system, improves the processing efficiency of the optimization algorithm and shortens the dynamic response time of the system.
Description
Technical Field
The invention relates to the technical field of mixed line processing and manufacturing systems, in particular to a dynamic production scheduling method for manufacturing resources of a mixed line manufacturing system.
Background
With the advance of the manufacturing industry of the intelligent manufacturing related technology in various countries, the flexibility and agility of the manufacturing system become the key for the manufacturing enterprises to improve the core competitiveness of the market. The faster and more agile dynamic scheduling technology is a main means for improving the flexibility and agility of the manufacturing system. With the continuous development of automation and information technology, it is not difficult to acquire relevant real-time processing data and superior order data in a manufacturing system.
Patent document No. CN107918367B discloses a real-time status management method for mixed production of multi-product batch products, which comprises: step 1: establishing a part processing procedure attribute model; step 2: constructing a process dimensional state model for mixed line production; and step 3: establishing a mixed line production resource attribute model; and 4, step 4: constructing a mixed line production resource dimensional state model; and 5: and constructing a mixed line production real-time state model taking part manufacturing processes and mixed production line resources as nodes and taking a task resource correlation relationship as an edge.
At present, a large amount of real-time data are different due to storage, or the data still stay in an inherent mode of later manual analysis of the data, so that a large amount of labor cost is consumed, and meanwhile, the value of the real-time data is greatly reduced along with the time. Therefore, a technical solution is needed to improve the above technical problems.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a dynamic production scheduling method for manufacturing resources of a mixed line manufacturing system.
According to the invention, the method for dynamically scheduling the manufacturing resources of the mixed line manufacturing system comprises the following steps:
step S11: constructing a process model indicator by using a data structure to complete data modeling of an actual process model of a product;
step S12: the data structure is utilized to realize the construction of a process-manufacturing resource relation model indicator, and the data modeling of the corresponding relation between the actual process and the manufacturing resource and the corresponding theoretical process duration is completed;
step S13: the database is used for completing the construction of basic operation information of the manufacturing system, and completing the construction and maintenance of the manufacturing resource utilization interval time such as the number of various manufacturing resources, the preparation time of the manufacturing resources, the switching time of products and the like and related necessary information;
step S14: constructing a dynamic scheduling program coder and a dynamic scheduling program decoder;
step S15: designing and constructing a complete optimization program and setting program parameters;
step S16: based on the manufacturing system model information constructed in the steps S11-S14, the construction and solution of the production scheduling solution solver are completed by using the program designed in the step S15;
step S17: completing the release of the scheduling manufacturing scheme by using a communication device;
step S18: constructing a detector, collecting relevant data such as the actual time consumption of each procedure in the manufacturing system, the fault condition of manufacturing resources, the product completion progress and the like in real time and storing the data into a real-time database;
step S19: constructing a manufacturing system operation core data monitor, setting a corresponding security domain for each data item in a real-time database, and if the data exceeds the security domain range, triggering a rearrangement mechanism by the system after a set waiting period;
step S20: triggering the rearrangement mechanism, and repeatedly executing the steps S11-S19.
Preferably, the steps S11 and S12 implement the conversion of the real logic model into data using an adaptive data structure for the product object process, the manufacturing system, and the product object process interrelationships.
Preferably, the step S13 is to complete the construction of a manufacturing system and product object manufacturing process basic database, which contains information of manufacturing resources such as the type and number of manufacturing resources in the manufacturing system and transition pattern information such as manufacturing preparation and product switching duration.
Preferably, in the step S14, in the dynamic scheduling program encoding method, the encoding length is flexibly changed according to dynamic information obtained by the detector from the manufacturing system, and a dynamic truncation method is used to determine the shortest encoding length of each type of product and remove the invalid solution space.
Preferably, in the step S14, in the dynamic scheduling decoding manner, the decoding is performed unit by unit from the dimension of the manufacturing time axis, and when a product is completed at a certain time, the encoding elements corresponding to the product in the subsequent encoding are eliminated or locked, so as to reconstruct the subsequent code; and decoding the codes to specific planned manufacturing time by combining the manufacturing switching time interval requirement constraint and the working time constraint of different product workpieces.
Preferably, the calculation of the fitness value in step S15 includes related items such as total production scheduling plan time consumption, plan invalid manufacturing time length, order advance/delay completion penalty value, and the like, and the construction of the optimization program is completed by using the solution fitness value of each plan as heuristic information; and resetting corresponding index weight and program execution parameters according to real-time data of the manual input/manufacturing system in the running and resetting process.
Preferably, the step S16 implements the interaction between the optimization program and the system model data information to complete the optimization of the manufacturing system scheduling scheme.
Preferably, in step S17, the manufacturing system schedule is transmitted to the control execution system via the communication device, and the core data is transmitted to the corresponding display interface for graphical display.
Preferably, in step S18, the real-time data acquisition and the monitoring of the change of the superior order data in the manufacturing system are completed by the data acquisition software and hardware devices, and the corresponding data are stored in the real-time database.
Preferably, in step S19, the data value of the critical data item in the real-time database is autonomously monitored, a data value fluctuation security domain is set, and a mapping relationship between the data value fluctuation and the rearrangement mechanism trigger is constructed.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention provides a system and a method for dynamically scheduling manufacturing resources of a mixed line manufacturing system, which provide a complete data acquisition-dynamic scheduling system architecture for the dynamic scheduling of the manufacturing system;
2. the invention has high processing efficiency for the dynamic change information of the manufacturing system, designs the coding and decoding strategies adapted to the dynamic change information according to the characteristics of the mixed line dynamic manufacturing system, improves the processing efficiency of the optimization algorithm, and compresses the dynamic response time of the system;
3. the invention extracts effective information from real-time data in real time based on the existing data acquisition technical environment of the current manufacturing system, quickly realizes the conversion from data updating to model updating, and completes the dynamic coding and decoding of an optimization algorithm in a self-adaptive manner based on an updated model so as to realize the dynamic and efficient generation of a scheduling scheme.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a block diagram of the system of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
The core idea of the invention is that the system and the method for dynamically scheduling the manufacturing resources of the mixed line manufacturing system complete real-time data acquisition through data acquisition software and hardware, and then store the data into a real-time database. The data item security domain is used, so that the monitoring of critical data is realized; through the design of the rearrangement timer, the rearrangement mechanism is prevented from being triggered too frequently, and the calculation efficiency is improved; the time consumption of the operation of an invalid solution space is reduced through the self-adaption of the coding length; the repeated judgment of line changing time caused by the whole code decoding is reduced through a time axis dimension time unit-by-time unit decoding mode; through the design of the fitness function, each key index item is fused, and the algorithm is guided to find the optimal scheduling scheme of the comprehensive indexes.
Referring to fig. 1, the system and method for dynamically scheduling manufacturing resources of a hybrid manufacturing system includes the following steps:
step S11: and constructing a process model indicator by utilizing an efficient data structure, and completing data modeling of an actual process model of a product. It is only executed once at run-time after the product type changes or the process model changes. And acquiring a product process network structure relation graph, and expressing the process network relation in a designed data structure.
Step S12: and constructing a process-manufacturing resource relation model indicator by using a proper data structure design, and completing data modeling of the actual process-manufacturing resource corresponding relation and the corresponding theoretical process duration. Obtaining the manufacturing resource type Rk of each process pairijThe number of the required RnijAnd time-consuming process Rt for processing specific process of manufacturing resourceij. Corresponding data is represented in a designed data structure.
Step S13: and the database is used for completing the basic operation information construction of the manufacturing system, and completing the construction and maintenance of the manufacturing resource utilization interval time such as the available number of various manufacturing resources, the preparation time of the manufacturing resources, the product switching time and the like and related necessary information. The manufacturing resource utilization interval time data information such as various manufacturing resource types, available number, input manufacturing resource preparation time, product switching time and the like is input into the basic information database.
Step S14: and designing a dynamic scheduling algorithm encoding and decoding mode. Using real-time data information to dynamically update the related operation data in the system in real time, and recording the event occurrence time Ot if the acquired information contains event items which influence the production performance of unit time, such as manufacturing resource failure, maintenance and the likesAnd estimate the duration period OtdAnd rapidly calculating the productivity Opn of each product in the event stateiDue to the direct influence on the estimation of the productivity of each type of productIn terms of the coding length, the conventional method is to use the lowest capacity of each product after a relevant event occurs to perform conservative length accounting on the codes of each type of product, so that the coding length is increased invisibly, the algorithm solving efficiency is reduced, and the phenomenon is particularly obvious when the types of the related products are increased. In the invention, a more elaborate coding mode is adopted, and the longest time consumption calculation of each type of product is carried out in a mode of accumulating the capacity fluctuation in each event influence period. Calculating pessimistic processing time consumption of the product based on real-time events occurring in the manufacturing system, and recording that a certain type of product is produced in the foreseeable abnormal state time of the manufacturing system, wherein the total output is SumNiTotal product number Sum of a certain type of product in a production order at an upper level of a manufacturing systemPi＜SumNiBy finding out a match
(PinThe yield of the n time unit after sorting the i type products from small yield to large yield) as the length basis of the codes of the type products; total product number Sum of a certain type of product in a production order at the upper level of a manufacturing systemNi≤SumPiWhen the length of the product code of the product type is according to TN + (Sum)Pi-SumNi)/NPiWhere TN is the total length of time units in the abnormal state, NPiNormal unit time yield for class i products; a compact coding structure is obtained according to such a partitioning method. Correspondingly, when each code is decoded, the unobstructed production scheduling scheme is relatively optimistic from the perspective of a single type of product, namely, the processing time is distributed in a fault-free working time unit, and then redundant coding elements exist in the code. This process is illustrated in figure 1 below.
In the original code, when the 5 th bit is decoded, the 1 type product finishes the order output, the same elements after the 5 th bit are removed, and the coding form after the removal is obtained.
Step S15: designing and constructing a complete optimization algorithm and setting algorithm parameters. And (4) utilizing the fitness value as heuristic information, and completing solution optimization through selection, intersection and variation processes. Wherein the fitness value is a comprehensive evaluation index of the relative invalid time consuming penalty values such as total scheme time consumption, order delay/completion penalty value in advance, product switching and the like:
wherein Fit is the fitness value, k1 is the total time consumption weight factor, Tt is the total time consumption of manufacture; k2 is an order early completion penalty weighting factor, and Bt is early completion time; k3 is order delay completion penalty weight factor, Dt is delay completion time; k4 is the order delayed completion penalty weighting factor, and Wt is the total time consumed by the non-output value processes such as product switching in the manufacturing process.
Step S16: based on the manufacturing system model information constructed in the steps S11 to S14, the construction and solution of the optimal production plan solver are completed using the algorithm designed in the step S15. And setting algorithm parameters such as iteration number, population number and the like of the design solving algorithm constructed in the step S15, and decoding the codes into an actual executable scheduling scheme.
Step S17: the release of the scheduling manufacturing scheme is accomplished using a communication device. And transmitting the scheduling scheme to a control execution system for execution by using the communication device.
Step S18: and constructing a detector, and acquiring and storing related data such as the actual time consumption of each procedure in the manufacturing system, the fault condition of manufacturing resources, the product completion progress and the like in real time in a real-time database. Real-time change information of a manufacturing system and an upper-layer order is obtained through data acquisition software and hardware technologies, and the real-time information is stored in a real-time database in a certain acquisition period.
Step S19: and constructing a manufacturing system operation core data monitor, setting a corresponding security domain for each data item in the real-time database, and triggering a rearrangement mechanism after a set waiting period if the data exceeds the security domain range. And setting a corresponding security domain [ SL, SU ] for each data item in the real-time database, and if the data exceeds the security domain range, triggering a rearrangement mechanism by the system after a set waiting period Tw.
Step S20: triggering the rearrangement mechanism, and repeatedly executing the steps S11-S19. The rearrangement timer triggers the rearrangement mechanism, updates the data model according to the information in the real-time database, and re-executes the steps S11-S19.
The steps S11 and S12 adopt the adaptive data structure to complete the conversion from the real logical model to the data for the product object process, the manufacturing system, and the product object process interrelation, so as to facilitate the efficient calculation processing of the solution calculation device. Step S13, completing the construction of a basic database of the manufacturing system and the manufacturing process of the product object, wherein the database contains the information of manufacturing resources such as the type and the number of the manufacturing resources in the manufacturing system; preparation of manufacture and switching time length of product. In the step S14, in the dynamic scheduling algorithm coding mode, the coding length may be determined by a dynamic truncation mode according to the elastic change of the dynamic information obtained by the detector from the manufacturing system, so as to eliminate the invalid solution space to the maximum extent and improve the algorithm solving efficiency. In the step S14 dynamic scheduling algorithm decoding method, decoding is performed unit by unit from the dimension of the manufacturing time axis, and when a product is completed at a certain time, the encoding elements corresponding to the product in the subsequent encoding are eliminated or locked, and the subsequent code is reconstructed. And decoding the codes to specific planned manufacturing time by combining the manufacturing switching time interval requirement constraint and the working time constraint of different product workpieces.
Step S15, calculating the fitness value which covers the total time consumption of the scheduling scheme, the invalid manufacturing time length of the scheme, the advance/delay completion penalty value of the order and other related items, and using the solution fitness value of each scheme as the heuristic information to complete the construction of the optimization algorithm; during the operation and rearrangement process, the corresponding index weight and the algorithm execution parameter can be reset according to the real-time data of the manual input/manufacturing system. And step S16, the interaction between the optimization algorithm and the system model data information is realized, and the optimization of the production scheduling scheme of the manufacturing system is completed. In step S17, the manufacturing system scheduling scheme is transmitted to the control execution system via the communication device and the core data is transmitted to the corresponding display interface for graphical display.
Step S18, the data acquisition software and hardware devices complete real-time data acquisition inside the manufacturing system and monitoring of the change of the superior order data, and store the corresponding data in the real-time database. Step S19 is to perform data value autonomous monitoring on the relevant data items in the real-time database, set a data value fluctuation security domain, and construct a mapping relationship between data value fluctuation and a rearrangement mechanism trigger. When the data fluctuates beyond the safety domain range, the rearrangement is triggered after the waiting period, and the setting of the waiting period can obtain the change information of the associated data items in a larger range, so that the repeated frequent triggering of a rearrangement mechanism in a short period is avoided, and the solving efficiency is improved.
In the embodiment of the invention, the system and the method for dynamically scheduling the manufacturing resources of the mixed line manufacturing system extract effective information from real-time data in real time, quickly realize the conversion from data updating to model updating, and adaptively complete the dynamic coding and decoding of an optimization algorithm based on an updated model, thereby realizing the dynamic and efficient generation of a scheduling scheme.
Referring to fig. 2, before scheduling, a process-resource indicator and a process model indicator complete a structured structure of relevant data such as manufacturing resources and product process information related to actual production of the manufacturing system; the construction of basic data of the manufacturing system is completed manually/by means of auxiliary devices; after the manufacturing is started, the detector monitors relevant data such as a manufacturing system, a superior order and materials in real time, and periodically updates relevant items of process-resource data, process data and manufacturing basic data; the communication device sets a rearrangement rule for the scheduling triggering decision of the human-computer interaction interface/the monitor of the manufacturing system to carry out scheduling triggering decision and sends the scheduling triggering decision to the optimized scheduling device, and sends scheduling scheme data to the manufacturing system/transmits the scheduling scheme data to the graphical interface display module; the optimized scheduling device completes dynamic encoding/decoding of the scheme based on information such as process-resource data, process data, manufacturing resource basic database and the like, and the scheduling optimized scheme is obtained through iterative optimization and decoding.
The computer is used for compiling and constructing a scheduling optimization algorithm module, a basic database module and a real-time database module. The Win7 system is selected as the operating system, and the system and the method for dynamically scheduling manufacturing resources of the mixed-line manufacturing system provided by the embodiment of the invention comprise:
1. and constructing a basic database, and filling information such as process relation data information, corresponding relation of processes and manufacturing resources, process duration and the like of each product into a data table.
2. The main program data loading module loads basic database data to complete the initialization of corresponding quantities in the manufacturing resource and product object related data structure containers.
3. Dynamic data of orders inside the manufacturing system and at the upper level are collected in real time by using data collection software and hardware devices, and a real-time database is constructed based on the collected data.
4. The main program data loading module loads the real-time database and other auxiliary information and completes the initialization of corresponding data in the program.
5. After the initialization of the corresponding data of the main program is completed, the construction of a corresponding coding structure is completed by adopting a designed coding mechanism according to the data information which affects the productivity.
6. Generating an initial population based on the 5-step coding structure, and calculating a function according to the fitness value
And selecting population individuals, and performing crossover and mutation operations to obtain the optimal scheme code. And judging the completion state of each type of product in the decoding process through decoding by time units, and deciding the operation of subsequent coding elements according to the completion state of each type of product to finish decoding so as to finally obtain an implementable scheduling scheme.
7. And (4) transmitting the production scheduling scheme in the 6 to a control execution system for execution in the format of the manufacturing task by using a communication device.
8. And setting a rearrangement timer, starting timing after the real-time database triggers rearrangement in step 3, and triggering rearrangement after set time.
In the model construction module, a process model constructor realizes the expression of the logical relation of the product process by using a data structure; the manufacturing resource indicator realizes the expression of the manufacturing resources in the production and processing process by using the data structure; the process-resource associator implements associative binding of each process-related process with the required available manufacturing resources. In the data construction module, a basic data constructor extracts/guides and inputs required basic data based on the procedure, manufacturing resources and a relation model of the procedure and the manufacturing resources obtained by the model construction module to form a complete model. In the optimization module, an encoder completes the construction of algorithm solving encoding based on model and data information; the core optimization algorithm in the optimizer carries out algorithm iteration optimization operation based on the codes constructed by the encoder, the evaluation of each optimization scheme is completed based on the decoder in each iteration, and the optimal codes are obtained after multiple rounds of generation optimization. In the scheme generation module, a scheme generator clearly shows an unambiguous scheme in a visual interface mode and sends a production task on the same day to a workshop production field through a communication device. The data detection module detector detects the current workshop production condition by directly detecting the relevant workshop production running condition in real time and indirectly converting corresponding production indexes. And the data monitoring module monitor completes the monitoring and early warning of the data based on the real-time dynamic comparison of the data detection module value and the preset threshold value. And monitoring the early warning information by a trigger of the model reconstruction module, reconstructing the corresponding model and data, and deciding whether to perform production scheduling again based on the reconstructed model. All modules implement a complete closed loop.
The invention provides a system and a method for dynamically scheduling manufacturing resources of a mixed line manufacturing system, which provide a complete data acquisition-dynamic scheduling system architecture for the dynamic scheduling of the manufacturing system; the processing efficiency of the dynamic change information of the manufacturing system is high, and according to the characteristics of the mixed line dynamic manufacturing system, the encoding and decoding strategies adaptive to the mixed line dynamic manufacturing system are designed, so that the processing efficiency of the optimization algorithm is improved, and the dynamic response time of the system is shortened; the invention extracts effective information from real-time data in real time based on the existing data acquisition technical environment of the current manufacturing system, quickly realizes the conversion from data updating to model updating, and completes the dynamic coding and decoding of an optimization algorithm in a self-adaptive manner based on an updated model so as to realize the dynamic and efficient generation of a scheduling scheme.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A dynamic scheduling method for manufacturing resources of a mixed line manufacturing system is characterized by comprising the following steps:
step S11: constructing a process model indicator by using a data structure to complete data modeling of an actual process model of a product;
step S12: the data structure is utilized to realize the construction of a process-manufacturing resource relation model indicator, and the data modeling of the corresponding relation between the actual process and the manufacturing resource and the corresponding theoretical process duration is completed;
step S13: the database is used for completing the construction of basic operation information of the manufacturing system, and completing the construction and maintenance of the manufacturing resource utilization interval time such as the number of various manufacturing resources, the preparation time of the manufacturing resources, the switching time of products and the like and related necessary information;
step S14: constructing a dynamic scheduling program coder and a dynamic scheduling program decoder;
step S15: designing and constructing a complete optimization program and setting program parameters;
step S16: based on the manufacturing system model information constructed in the steps S11-S14, the construction and solution of the production scheduling solution solver are completed by using the program designed in the step S15;
step S17: completing the release of the scheduling manufacturing scheme by using a communication device;
step S18: constructing a detector, collecting relevant data such as the actual time consumption of each procedure in the manufacturing system, the fault condition of manufacturing resources, the product completion progress and the like in real time and storing the data into a real-time database;
step S19: constructing a manufacturing system operation core data monitor, setting a corresponding security domain for each data item in a real-time database, and if the data exceeds the security domain range, triggering a rearrangement mechanism by the system after a set waiting period;
step S20: triggering the rearrangement mechanism, and repeatedly executing the steps S11-S19.
2. The method of claim 1, wherein the steps S11 and S12 employ adaptive data structures for product object processes, manufacturing systems, and product object process interrelationships to accomplish the conversion of real-world logical models to data.
3. The method of claim 1, wherein the step S13 completes construction of a manufacturing system and product object manufacturing process base database, which contains information of manufacturing resources such as manufacturing resource type and number in the manufacturing system and transition pattern information such as manufacturing preparation and product switching duration.
4. The method as claimed in claim 1, wherein in the step S14, the encoding method of the dynamic scheduling program is used to determine the shortest encoding length of each type of product by a dynamic truncation according to the elastic variation of the dynamic information obtained from the manufacturing system by the detector, so as to remove the invalid solution space.
5. The method for dynamically scheduling manufacturing resources of a mixed-line manufacturing system as claimed in claim 1, wherein in the step S14, the decoding procedure is performed unit by unit from the dimension of the manufacturing time axis, and when a product is completed at a certain time, the encoding elements corresponding to the product in the subsequent encoding are removed or locked to reconstruct the subsequent code; and decoding the codes to specific planned manufacturing time by combining the manufacturing switching time interval requirement constraint and the working time constraint of different product workpieces.
6. The method for dynamically scheduling manufacturing resources of a hybrid manufacturing system as claimed in claim 1, wherein the calculation of the fitness value of step S15 covers the total time consumption of scheduling project, the length of the project invalid manufacturing time, the order advance/delay completion penalty value, and so on, and the construction of the optimization program is completed by using the solution fitness value of each project as the heuristic information; and resetting corresponding index weight and program execution parameters according to real-time data of the manual input/manufacturing system in the running and resetting process.
7. The method of claim 1, wherein the step S16 implements interaction between the optimization program and the system model data information to complete optimization of the manufacturing system scheduling.
8. The method of claim 1, wherein in step S17, the manufacturing system scheduling plan is transmitted to the control execution system via the communication device and the core data is transmitted to the corresponding display interface for graphical display.
9. The method as claimed in claim 1, wherein the step S18 is implemented by data acquisition software and hardware devices to complete real-time data acquisition and monitoring of the change of the upper-level order data in the manufacturing system, and store the corresponding data in the real-time database.
10. The method for dynamically scheduling manufacturing resources of a mixed-line manufacturing system as claimed in claim 1, wherein said step S19 is implemented to perform data value autonomous monitoring on critical data items in a real-time database, set a data value fluctuation security domain, and construct a mapping relationship between data value fluctuation and a reordering mechanism trigger.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110872263.1A CN113627759B (en) | 2021-07-30 | 2021-07-30 | Dynamic production scheduling method for manufacturing resources of hybrid wire manufacturing system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110872263.1A CN113627759B (en) | 2021-07-30 | 2021-07-30 | Dynamic production scheduling method for manufacturing resources of hybrid wire manufacturing system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113627759A true CN113627759A (en) | 2021-11-09 |
| CN113627759B CN113627759B (en) | 2023-12-12 |
Family
ID=78381764
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110872263.1A Active CN113627759B (en) | 2021-07-30 | 2021-07-30 | Dynamic production scheduling method for manufacturing resources of hybrid wire manufacturing system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113627759B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103632222A (en) * | 2013-11-19 | 2014-03-12 | 西安工业大学 | Manufacturing resource service state monitoring system |
| CN105117800A (en) * | 2015-08-28 | 2015-12-02 | 国网天津市电力公司 | Method for optimizing power transmission and transformation project construction network plan on basis of genetic algorithm |
| CN105321042A (en) * | 2015-10-19 | 2016-02-10 | 金航数码科技有限责任公司 | Genetic algorithm-based advanced plan scheduling system and method |
| US9727829B2 (en) * | 2009-11-25 | 2017-08-08 | General Electric Company | Systems and methods for multi-resource scheduling |
| CN107918367A (en) * | 2017-11-20 | 2018-04-17 | 上海航天精密机械研究所 | Multi items bulk article Flexible production real-time status management method |
| CN108320069A (en) * | 2017-12-20 | 2018-07-24 | 甘肃银光化学工业集团有限公司 | A kind of manufacturing execution system |
| CN109598416A (en) * | 2018-11-13 | 2019-04-09 | 中国航天***科学与工程研究院 | A kind of the Dynamic Scheduling system and scheduled production method in multiple material workshop |
| CN109829594A (en) * | 2017-11-23 | 2019-05-31 | 上海零参科技发展有限公司 | A kind of PEBS intelligence productivity ecological balance processing system and method |
| KR102219993B1 (en) * | 2020-01-10 | 2021-02-25 | 한국전자기술연구원 | Energy-saving dynamic production scheduling operation method for parallel heat treatment process |
-
2021
- 2021-07-30 CN CN202110872263.1A patent/CN113627759B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9727829B2 (en) * | 2009-11-25 | 2017-08-08 | General Electric Company | Systems and methods for multi-resource scheduling |
| CN103632222A (en) * | 2013-11-19 | 2014-03-12 | 西安工业大学 | Manufacturing resource service state monitoring system |
| CN105117800A (en) * | 2015-08-28 | 2015-12-02 | 国网天津市电力公司 | Method for optimizing power transmission and transformation project construction network plan on basis of genetic algorithm |
| CN105321042A (en) * | 2015-10-19 | 2016-02-10 | 金航数码科技有限责任公司 | Genetic algorithm-based advanced plan scheduling system and method |
| CN105321042B (en) * | 2015-10-19 | 2017-02-22 | 金航数码科技有限责任公司 | Genetic algorithm-based advanced plan scheduling system and method |
| CN107918367A (en) * | 2017-11-20 | 2018-04-17 | 上海航天精密机械研究所 | Multi items bulk article Flexible production real-time status management method |
| CN107918367B (en) * | 2017-11-20 | 2019-09-17 | 上海航天精密机械研究所 | Multi items bulk article Flexible production real-time status management method |
| CN109829594A (en) * | 2017-11-23 | 2019-05-31 | 上海零参科技发展有限公司 | A kind of PEBS intelligence productivity ecological balance processing system and method |
| CN108320069A (en) * | 2017-12-20 | 2018-07-24 | 甘肃银光化学工业集团有限公司 | A kind of manufacturing execution system |
| CN109598416A (en) * | 2018-11-13 | 2019-04-09 | 中国航天***科学与工程研究院 | A kind of the Dynamic Scheduling system and scheduled production method in multiple material workshop |
| KR102219993B1 (en) * | 2020-01-10 | 2021-02-25 | 한국전자기술연구원 | Energy-saving dynamic production scheduling operation method for parallel heat treatment process |
Non-Patent Citations (4)
| Title |
|---|
| JIANYU LONG; ZHENZHONG SUN; YING HONG; YUN BAI;: "Robust Dynamic Scheduling with Uncertain Release Time for the Steelmaking-Continuous Casting Production", 2018 INTERNATIONAL CONFERENCE ON SENSING, DIAGNOSTICS, PROGNOSTICS, AND CONTROL (SDPC) * |
| JINGTAO ZHOU; JING GAO; MINGWEI WANG; TENGYUAN JIANG; BO JIANG;: "Genetic algorithm based scheduling optimization for design and manufacturing integration", 2018 2ND IEEE ADVANCED INFORMATION MANAGEMENT, COMMUNICATES, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IMCEC) * |
| 吴雁;王彦瑞;张杰人;王犁;王开发;: "基于MES的离散型制造业的高级计划排产的应用研究", 制造技术与机床, no. 08 * |
| 张伟;于成龙;侯俊杰;徐皓;赵颖;: "基于感知信息的车间动态排产方法", 现代制造工程, no. 02 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113627759B (en) | 2023-12-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Discovering dispatching rules using data mining | |
| CN100487700C (en) | Data processing method and system of data library | |
| CN105959401B (en) | A kind of manufacturing service supply-demand mode and dynamic dispatching method based on super-network | |
| CN102624865B (en) | Cluster load prediction method and distributed cluster management system | |
| CN102999528B (en) | ETL tasks are offline in a kind of data warehouse and the method and device of data scrubbing | |
| CN104318481A (en) | Power-grid-operation-oriented holographic time scale measurement data extraction conversion method | |
| Valverde et al. | Logarithmic growth dynamics in software networks | |
| CN104076809A (en) | Data processing device and data processing method | |
| Simeu-Abazi et al. | Maintenance integration in manufacturing systems: from the modeling tool to evaluation | |
| CN103606121A (en) | Automatic generation method of emergency plan | |
| WO2014153148A1 (en) | System and method for integrated modeling and analysis | |
| Aghimien et al. | A review of the application of data mining for sustainable construction in Nigeria | |
| CN116993052A (en) | Intelligent factory production on-line monitoring analysis system based on digital twinning | |
| Lecoutre | Ace, a generic constraint solver | |
| CN112700065B (en) | Business process completion time interval prediction method and system based on deep learning | |
| CN113627759B (en) | Dynamic production scheduling method for manufacturing resources of hybrid wire manufacturing system | |
| Aldaeej et al. | From lasagna to spaghetti, a decision model to manage defect debt | |
| CN109213105B (en) | Reconfigurable device, reconfigurable realization method and distributed control system | |
| CN115238583B (en) | Business process remaining time prediction method and system supporting incremental log | |
| CN114819925B (en) | Industrial Internet of things system based on event sequence analysis and prediction and control method thereof | |
| CN114037341B (en) | DDQN-based intelligent workshop dynamic self-adaptive scheduling method and system | |
| CN114756602A (en) | Real-time streaming process mining method and system and computer readable storage medium | |
| US7577627B2 (en) | Modular SQL rule-based management of job process flow | |
| CN112784447B (en) | Nuclear power plant accident modeling method for DET and RELAP5 program dynamic coupling framework | |
| Liu | Research and application of computer aided analysis and optimization algorithm for fault tree |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |