CN113627759B - Dynamic production scheduling method for manufacturing resources of hybrid wire manufacturing system - Google Patents
Dynamic production scheduling method for manufacturing resources of hybrid wire manufacturing system Download PDFInfo
- Publication number
- CN113627759B CN113627759B CN202110872263.1A CN202110872263A CN113627759B CN 113627759 B CN113627759 B CN 113627759B CN 202110872263 A CN202110872263 A CN 202110872263A CN 113627759 B CN113627759 B CN 113627759B
- Authority
- CN
- China
- Prior art keywords
- manufacturing
- data
- time
- manufacturing system
- scheduling
- 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.)
- Active
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 212
- 238000000034 method Methods 0.000 title claims abstract description 80
- 230000008569 process Effects 0.000 claims abstract description 48
- 230000008707 rearrangement Effects 0.000 claims abstract description 24
- 238000010276 construction Methods 0.000 claims abstract description 22
- 238000005457 optimization Methods 0.000 claims abstract description 18
- 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
- 238000004891 communication Methods 0.000 claims description 10
- 238000012544 monitoring process Methods 0.000 claims description 9
- 230000008859 change Effects 0.000 claims description 8
- 230000003044 adaptive effect Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000003993 interaction Effects 0.000 claims description 4
- 238000005520 cutting process Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 13
- 230000004044 response Effects 0.000 abstract description 3
- 238000006243 chemical reaction Methods 0.000 description 7
- 238000013461 design Methods 0.000 description 6
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 239000000284 extract Substances 0.000 description 3
- 230000001960 triggered effect Effects 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000012217 deletion Methods 0.000 description 2
- 230000037430 deletion Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000035772 mutation Effects 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003754 machining Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000000472 traumatic effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Manufacturing & Machinery (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Data Mining & Analysis (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a dynamic scheduling method of manufacturing resources of a hybrid manufacturing system, which comprises the steps of constructing a process model indicator by utilizing a data structure to finish data modeling of an actual process model of a product; completing data modeling of the corresponding relation between the actual process and the manufacturing resource and the corresponding theoretical process duration; the construction and maintenance of the utilization interval time of the manufacturing resources, such as the number of various manufacturing resources, the preparation time of the manufacturing resources, the switching time of products and the like, and relevant necessary information are completed; constructing a dynamic scheduling program coder and decoder; designing and constructing a complete optimization program and setting program parameters; completing construction of a production scheduling scheme solver and scheme solving; completing release of the production scheduling manufacturing scheme; constructing a detector; constructing a manufacturing system operation core data monitor; the rearrangement mechanism triggers. The application provides a complete data acquisition-dynamic scheduling system architecture for dynamic scheduling of the hybrid line manufacturing system, improves the processing efficiency of an optimization algorithm, and compresses the dynamic response time of the system.
Description
Technical Field
The application relates to the technical field of hybrid wire processing and manufacturing systems, in particular to a method for dynamically arranging manufacturing resources of a hybrid wire manufacturing system.
Background
Along with the promotion of the related technology of intelligent manufacturing in manufacturing industries of various countries, the flexibility and the agility of a manufacturing system become keys for improving the market core competitiveness of manufacturing enterprises. The faster and faster dynamic production technique is a main means for improving the flexibility and agility of the manufacturing system. With the continuous development of automation and informatization technologies, the acquisition of related real-time processing data and upper order data in a manufacturing system is not difficult.
Disclosed in patent document with publication number CN107918367B is a real-time status management method for mixed line production of multi-variety batch products, comprising: step 1: establishing a part machining procedure attribute model; step 2: constructing a process dimension state model of mixed line production; step 3: establishing a mixed line production resource attribute model; step 4: constructing a mixed line production resource dimension state model; step 5: and constructing a real-time state model of the mixed line production by taking the part manufacturing process and the mixed production line resource as nodes and the task resource association relationship as edges.
At present, a large amount of real-time data exist in different modes, or stay in a natural mode of later manual analysis of the data, so that the value of the real-time data is also greatly reduced along with the time while a large amount of labor cost is consumed. Therefore, a technical solution is needed to improve the above technical problems.
Disclosure of Invention
Aiming at the defects in the prior art, the application aims to provide a method for dynamically arranging manufacturing resources of a hybrid wire manufacturing system.
The application provides a method for dynamically scheduling manufacturing resources of a hybrid wire manufacturing system, which comprises the following steps:
step S11: constructing a process model indicator by utilizing a data structure, and completing data modeling of an actual process model of the product;
step S12: constructing a process-manufacturing resource relation model indicator by utilizing a data structure, and completing data modeling of an actual process-manufacturing resource corresponding relation and a corresponding theoretical process duration;
step S13: the basic operation information construction of the manufacturing system is completed by utilizing the database, and the construction and maintenance of 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 relevant necessary information are completed;
step S14: constructing a dynamic scheduling program coder and 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, completing the construction of a production scheduling scheme solver and scheme solving by utilizing the program designed in the step S15;
step S17: completing release of the production scheduling manufacturing scheme by using the communication device;
step S18: constructing a detector, and collecting and storing relevant data such as actual time consumption of each procedure in the processing process, fault conditions of manufacturing resources, product completion progress and the like of the upper order, deletion and modification and the manufacturing system in real time 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 triggering a rearrangement mechanism after a set waiting period if the data exceeds the security domain range;
step S20: the rearrangement mechanism is triggered, and the steps S11-S19 are repeatedly executed.
Preferably, the step S11 and the step S12 complete conversion from the real logic model to the data by adopting an adaptive data structure for the product object process, the manufacturing system and the product object process interrelationship.
Preferably, the step S13 completes the construction of a basic database of the manufacturing system and the product object manufacturing process, and the database covers the information of the manufacturing resources such as the type and the number of the manufacturing resources in the manufacturing system and the conversion mode information such as the manufacturing preparation and the product switching time.
Preferably, in the step S14 of dynamic production program coding mode, the coding length is changed elastically according to the dynamic information obtained from the manufacturing system by the detector, and the shortest coding length of each type of product is determined by adopting a dynamic cutting mode, so as to eliminate the ineffective solution space.
Preferably, in the step S14 dynamic scheduling program decoding mode, decoding is performed unit by unit from the dimension of the manufacturing time axis, when a product is completed at a certain moment, the coding element corresponding to the product in the subsequent coding is eliminated or locked, and the subsequent code is reconstructed; and combining the manufacturing switching time interval requirement constraint and the working time constraint of the workpieces of different products to decode the codes to the specific planned manufacturing time.
Preferably, the calculation of the fitness value in step S15 covers the total time consumption of the scheduling scheme, the invalid manufacturing time of the scheme, the order advance/delay completion penalty value and other items, and the solution fitness value of each scheme is used as heuristic information to complete the construction of the optimization program; in the operation rearrangement process, the corresponding index weight and the program execution parameter are reset according to the real-time data of the manual input/manufacturing system.
Preferably, the step S16 implements interaction between the optimization program and the system model data information, so as to complete optimization of the production scheduling scheme of the manufacturing system.
Preferably, in step S17, the manufacturing system production schedule is transmitted to the control execution system by means of the communication device and the core data is transmitted to the corresponding display interface for graphical display.
Preferably, the step S18 completes the real-time data collection and the monitoring of the data change of the upper order in the manufacturing system through the data collection software and hardware devices, and stores the corresponding data into the real-time database.
Preferably, the step S19 performs autonomous monitoring of data values on the related data items in the real-time database, sets a security domain of data value fluctuation, and constructs a mapping relationship between the data value fluctuation and the rearrangement mechanism trigger.
Compared with the prior art, the application has the following beneficial effects:
1. the system and the method for dynamically arranging the manufacturing resources of the hybrid wire manufacturing system provide a complete data acquisition-dynamic arrangement system architecture for the dynamic arrangement of the manufacturing system;
2. the application has high processing efficiency on the dynamic change information of the manufacturing system, designs the coding and decoding strategies which are suitable for the hybrid dynamic manufacturing system according to the characteristics of the hybrid dynamic manufacturing system, improves the processing efficiency of an optimization algorithm, and compresses the dynamic response time of the system;
3. the application extracts effective information from real-time data in real time based on the existing data acquisition technical environment of the current manufacturing system, rapidly realizes conversion from data update to model update, and adaptively completes dynamic encoding and decoding of an optimization algorithm based on an update model so as to realize dynamic and efficient generation of a scheduling scheme.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a schematic flow chart of the present application;
FIG. 2 is a schematic diagram of a system module according to the present application.
Detailed Description
The present application will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present application, but are not intended to limit the application in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present application.
The application has the core ideas that the system and the method for dynamically scheduling the manufacturing resources of the hybrid wire 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 for realizing the monitoring of the critical data; by means of the design of the rearrangement timer, the too frequent triggering of a rearrangement mechanism is avoided, and the calculation efficiency is improved; the time consumption of the operation of the ineffective solution space is reduced through the self-adaption of the coding length; the repeated judgment of line changing time caused by whole code decoding is reduced by a decoding mode of time axis dimension time units; through the design of the fitness function, each key index item is fused, and the guiding algorithm finds the production scheduling scheme with the optimal comprehensive index.
Referring to fig. 1, the system and method for dynamically scheduling manufacturing resources of a hybrid manufacturing system comprises the following steps:
step S11: and constructing a process model indicator by utilizing an efficient data structure, and completing data modeling of a product actual process model. Only once at run-time after product type changes or process model changes. And obtaining a product process network structure relation diagram, and expressing the process network relation in a designed data structure.
Step S12: and constructing a process-manufacturing resource relation model indicator by utilizing proper data structure design, and completing data modeling of the corresponding relation between the actual process and the manufacturing resource and the corresponding theoretical process time. Obtaining the kind Rk of manufacturing resource of each process ij Demand Rn of number ij And manufacturing resources process specific process time Rt ij . The corresponding data is expressed in a data structure of the design.
Step S13: and the database is utilized to complete the construction of basic operation information of the manufacturing system, and the construction and maintenance of manufacturing resource utilization interval time such as the available number of various manufacturing resources, the preparation time of the manufacturing resources, the switching time of products and the like and relevant necessary information are completed. And inputting the manufacturing resource utilization interval time data information such as various manufacturing resource types and available numbers, input manufacturing resource preparation time, product switching time and the like into a basic information database.
Step S14: and designing a coding and decoding mode of a dynamic scheduling algorithm. Dynamic real-time updating of relevant operation data in the system is performed by utilizing real-time data information, and if the acquired information contains event items such as manufacturing resource faults, maintenance and the like which influence the productivity in unit time, the event occurrence time Ot is recorded s Estimating a duration Ot d And rapidly calculating the productivity Opn of each product under the event state i Because the estimation of the productivity of each type of product directly affects the coding length, the conventional method is to use the lowest productivity of each product after related events occur to perform conservative length accounting of the codes of each type of product, so that the coding length is increased intangibly, the algorithm solving efficiency is reduced, and the phenomenon is particularly obvious when the types of the related products are increased. The application adopts a finer coding mode, and the longest time-consuming calculation of various types of products is performed in a form of capacity fluctuation accumulation in each event influence period. Based on real-time events of the manufacturing system, pessimistic processing time of the product is calculated, and the product is recorded to be produced in a certain type under the foreseeable abnormal state time of the manufacturing system, and the total yield is Sum Ni Sum of total product demand for a certain type of product in a production order at a high level of a manufacturing system Pi ＜Sum Ni In the process, the coincidence is obtained by solving(P in The production of the n time unit after the sorting from small to large production is used as the minimum value time unit PN of the condition of the i type of products, and the minimum value time unit PN is used as the length basis of the codes of the type of products; sum of total product demand for a certain type of product in a production order at a top level of a manufacturing system Ni ≤Sum Pi When the product code length of the product type is based on TN+ (Sum Pi -Sum Ni )/NP i Wherein TN is the total time unit length of abnormal state, NP i The normal unit time yield of the i-class product; and obtaining a simplified coding structure according to the dividing method. Correspondingly, when decoding each code, the smooth production arrangement 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 further redundant code elements exist in the code. The process is as follows1.
In the original code, when decoding to the 5 th bit, the 1 type product completes the order output, and the same elements after the 5 th bit are removed, so that the removed coding form is obtained.
Step S15: and designing and constructing a complete optimization algorithm and setting algorithm parameters. And using the fitness value as heuristic information, and completing optimization of the solution through the processes of selection, crossing and mutation. The fitness value is a comprehensive evaluation index of the scheme total time consumption, the order delay/advance completion penalty value, the product switching and other related invalid time consumption penalty values:
wherein Fit is a fitness value, k1 is a total time consumption weight factor, and Tt is total time consumption for manufacturing; k2 is an order advance completion penalty weight factor, bt is an advance completion time; k3 is an order delay finishing penalty weight factor, and Dt is delay finishing time; and k4 is an order delay finishing penalty value weight factor, and Wt is the total time spent in non-productive processes such as product switching in the manufacturing process.
Step S16: based on the manufacturing system model information constructed in the steps S11-S14, the construction of the optimal production scheme solver and scheme solving are completed by utilizing the algorithm designed in the step S15. And (3) 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 actually executable scheduling scheme.
Step S17: release of the production scheduling manufacturing scheme is accomplished using the communication device. And transmitting the scheduling scheme to a control execution system for execution by using a communication device.
Step S18: and constructing a detector, and acquiring and storing relevant data such as actual time consumption of each procedure in the processing process, fault conditions of manufacturing resources, product completion progress and the like of the upper order, deletion and modification and the manufacturing system in real time into a real-time database. And acquiring real-time change information of the manufacturing system and an upper order by a data acquisition software and hardware technology, and storing the real-time information 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. Setting corresponding security domains (SL, SU) for each data item in the real-time database, and triggering a rearrangement mechanism after a set waiting period Tw if the data exceeds the security domain range.
Step S20: the rearrangement mechanism is triggered, and the steps S11-S19 are repeatedly executed. The rearrangement timer triggers a rearrangement mechanism to update the data model according to the information in the real-time database, and the steps S11-S19 are re-executed.
Step S11 and step S12 complete conversion from the actual logic model to the data by adopting the adaptive data structure for the product object procedure, the manufacturing system and the product object procedure, so as to facilitate the efficient operation processing of the solving operation 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 covers the information of manufacturing resources such as the types, the numbers and the like of the manufacturing resources in the manufacturing system; manufacturing preparation and product switching time and other conversion mode information. In the step S14, the shortest coding length of each type of product can be determined by adopting a dynamic cutting mode according to the elastic change of dynamic information acquired by the detector from the manufacturing system, so that the invalid solution space is removed to the maximum extent, and the algorithm solving efficiency is improved. In the decoding mode of the dynamic scheduling algorithm in step S14, decoding is performed unit by unit from the dimension of the manufacturing time axis, when a product is completed at a certain moment, the coding element corresponding to the product in the subsequent coding is eliminated or locked, and the subsequent code is reconstructed. And combining the manufacturing switching time interval requirement constraint and the working time constraint of the workpieces of different products to decode the codes to the specific planned manufacturing time.
Step S15, calculating fitness values covers related items such as total time consumption of a scheduling scheme, invalid manufacturing time of the scheme, order advance/delay completion penalty values and the like, and constructing an optimization algorithm by using each scheme solution fitness value as heuristic information; in the operation rearrangement process, corresponding index weight and algorithm execution parameters can be reset according to the real-time data of the manual input/manufacturing system. And S16, realizing interaction between an optimization algorithm and system model data information, and completing optimization of a production scheduling scheme of the manufacturing system. In step S17, the manufacturing system production schedule is transmitted to the control execution system by means of the communication device and the core data is transmitted to the corresponding display interface for graphical display.
And step S18, completing real-time data acquisition and monitoring of upper order data change in the manufacturing system through the data acquisition software and hardware devices, and storing corresponding data into a real-time database. And S19, performing autonomous monitoring on the data value of the related heavy data item in the real-time database, setting a data value fluctuation security domain, and constructing a mapping relation between the data value fluctuation and the rearrangement mechanism trigger. When the data fluctuates beyond the range of the security domain, the rearrangement is triggered after the waiting period, and the change information of the related data item in a larger range can be obtained through the setting of the waiting period, so that the repeated frequent triggering of the rearrangement mechanism in a short period is avoided, and the solving efficiency is improved.
According to the system and the method for dynamically scheduling the manufacturing resources of the hybrid manufacturing system, disclosed by the embodiment of the application, effective information is extracted from real-time data in real time, the conversion from data update to model update is quickly realized, the dynamic encoding and decoding of an optimization algorithm are adaptively completed based on an update model, and the dynamic and efficient generation of a scheduling scheme is realized.
Referring to fig. 2, prior to the production, the process-resource indicator and the process model indicator complete the structured construction of relevant data such as manufacturing resources, product process information, etc. relevant to the actual production of the manufacturing system; constructing basic data of the manufacturing system manually/by aid of auxiliary devices; after the manufacturing is started, the detector monitors related data such as a manufacturing system, a superior order, materials and the like in real time, and periodically updates related items of procedure-resource data, procedure data and manufacturing basic data; the communication device sets a rearrangement rule for scheduling triggering decision of the human-computer interaction interface/a manufacturing system monitor to transmit the scheduling triggering decision to the optimized scheduling device, and transmits scheduling scheme data to the manufacturing system/a graphical interface display module; the optimized production scheduling device completes dynamic coding/decoding of the scheme based on the information of the process-resource data, the process data, the manufacturing resource basic database and the like, and obtains a production scheduling optimization scheme through iterative optimization and decoding.
And a production scheduling optimization algorithm module, a basic database module and a real-time database module are compiled and constructed in a computer. The Win7 system is selected as the operating system, and the system and the method for dynamically scheduling manufacturing resources of the hybrid manufacturing system provided by the embodiment of the application comprise the following steps:
1. constructing a basic database, and filling the information of the process relation data information, the corresponding relation of the process and the manufacturing resource, the process duration and the like of each product into a data table.
2. And the main program data loading module loads the basic database data to finish the initialization of corresponding variables in the manufacturing resource and product object related data structure containers.
3. And acquiring dynamic data of orders in the manufacturing system and at the upper level in real time by utilizing a data acquisition software device and a data acquisition hardware device, and constructing a real-time database based on the acquired data.
4. And 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 the adaptive coding structure is completed by adopting a designed coding mechanism according to the data information influencing the productivity.
6. Generating an initial population based on the coding structure in 5, and calculating a function according to the fitness valueAnd selecting population individuals, and executing 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 of time units, deciding the operation of the subsequent coding elements according to the completion state, and finally obtaining the practical production scheduling scheme.
7. The production scheduling scheme in 6 is issued to the control execution system for execution in the format of manufacturing tasks by using the communication device.
8. Setting a rearrangement timer, starting timing after the real-time database triggers rearrangement in step 3 and triggering rearrangement after the set time.
In the model construction module, a process model constructor realizes the expression of the logical relationship of the product process by using a stress data structure; the manufacturing resource indicator realizes the expression of the manufacturing resources in the production and processing process by utilizing the data structure; the process-resource correlator realizes the association relation binding of each process related process and the required available manufacturing resources. In the data construction module, a basic data constructor extracts/guides input required basic data based on the working procedure, manufacturing resources and a relation model of the working procedure and the manufacturing resources obtained by the model construction module to form a complete model. In the optimization module, the encoder completes the construction of algorithm solving encoding based on the model and the data information; the core optimization algorithm in the optimizer carries out algorithm iteration optimizing operation based on the codes constructed by the encoder, the evaluation of each optimizing scheme is completed based on the decoder in each iteration, and the optimal codes are obtained after multiple rounds of traumatic optimizing. In the scheme generation module, a scheme generator clearly displays an unambiguous scheme in a visual interface mode and sends a daily production task to a workshop production site through a communication device. The data detection module detector directly detects the relevant operation conditions of workshop production in real time and indirectly converts corresponding production indexes to finish the detection of the current workshop production conditions. And the data monitoring module monitor completes monitoring and early warning of the data based on real-time dynamic comparison of the data detection module value and a preset threshold value. The model reconstruction module triggers monitor the early warning information, reconstruct the corresponding model and data, and decide whether to carry out scheduling again based on the reconstructed model. All modules realize a complete closed loop.
The system and the method for dynamically arranging the manufacturing resources of the hybrid wire manufacturing system provide a complete data acquisition-dynamic arrangement system architecture for the dynamic arrangement of the manufacturing system; for the high processing efficiency of the dynamic change information of the manufacturing system, according to the characteristics of the hybrid dynamic manufacturing system, the coding and decoding strategies which are suitable for the hybrid dynamic manufacturing system are designed, the processing efficiency of an optimization algorithm is improved, and the dynamic response time of the system is shortened; the application extracts effective information from real-time data in real time based on the existing data acquisition technical environment of the current manufacturing system, rapidly realizes conversion from data update to model update, and adaptively completes dynamic encoding and decoding of an optimization algorithm based on an update model so as to realize dynamic and efficient generation of a scheduling scheme.
Those skilled in the art will appreciate that the application provides a system and its individual devices, modules, units, etc. that can be implemented entirely by logic programming of method steps, in addition to being implemented as pure computer readable program code, in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Therefore, the system and various devices, modules and units thereof provided by the application can be regarded as a hardware component, and the devices, modules and units for realizing various functions included in the system can also be regarded as structures in the hardware component; means, modules, and units for implementing the various functions may also be considered as either software modules for implementing the methods or structures within hardware components.
The foregoing describes specific embodiments of the present application. It is to be understood that the application is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the application. The embodiments of the application and the features of the embodiments may be combined with each other arbitrarily without conflict.
Claims (8)
1. A method for dynamically scheduling manufacturing resources of a hybrid wire manufacturing system, the method comprising the steps of:
step S11: constructing a process model indicator by utilizing a data structure, and completing data modeling of an actual process model of the product;
step S12: constructing a process-manufacturing resource relation model indicator by utilizing a data structure, and completing data modeling of an actual process-manufacturing resource corresponding relation and a corresponding theoretical process duration;
step S13: the basic operation information construction of the manufacturing system is completed by utilizing the database, and the construction and maintenance of the number of various manufacturing resources, the preparation time of the manufacturing resources and the switching time of products are completed;
step S14: constructing a dynamic scheduling program coder and 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, completing the construction of a production scheduling scheme solver and scheme solving by utilizing the program designed in the step S15;
step S17: completing release of the production scheduling manufacturing scheme by using the communication device;
step S18: constructing a detector, acquiring and storing real-time consumption, fault conditions of manufacturing resources and product completion progress of each process in the process of adding, deleting and changing upper orders and manufacturing systems in real time 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 triggering a rearrangement mechanism after a set waiting period if the data exceeds the security domain range;
step S20: triggering a rearrangement mechanism, and repeatedly executing the steps S11-S19;
in the step S14, in the dynamic scheduling program coding mode, the coding length is changed elastically according to the dynamic information acquired by the detector from the manufacturing system, and the shortest coding length of each type of product is determined by adopting a dynamic cutting mode, so that the ineffective solution space is removed;
in the step S14 dynamic scheduling program decoding mode, decoding is performed unit by unit from the dimension of the manufacturing time axis, when a product is completed at a certain moment, the coding element corresponding to the product in the subsequent coding is eliminated or locked, and the subsequent code is reconstructed; and combining the manufacturing switching time interval requirement constraint and the working time constraint of the workpieces of different products to decode the codes to the specific planned manufacturing time.
2. The method for dynamically scheduling manufacturing resources of a hybrid manufacturing system according to claim 1, wherein the step S11 and the step S12 are performed by adopting an adaptive data structure for the product object process, the manufacturing system, and the product object process interrelationships to convert the real logic model into the data.
3. The method according to claim 1, wherein the step S13 is performed to complete the construction of a basic database of the manufacturing process of the manufacturing system and the product object, and the database includes the types and numbers of the manufacturing resources in the manufacturing system and the manufacturing preparation and the product switching time.
4. The method for dynamically scheduling production of manufacturing resources of a hybrid manufacturing system according to claim 1, wherein the calculation of the fitness value in step S15 includes total time consumption of a scheduling scheme, invalid manufacturing time of the scheme, order advance/delay completion penalty, and the construction of an optimization program is completed by using each scheme solution fitness value as heuristic information; in the operation rearrangement process, the corresponding index weight and the program execution parameter are reset according to the real-time data of the manual input/manufacturing system.
5. The method for dynamically scheduling manufacturing resources of a hybrid manufacturing system according to claim 1, wherein the step S16 implements interaction between an optimization program and system model data information to complete optimization of a manufacturing system scheduling scheme.
6. The method according to claim 1, wherein in step S17, the manufacturing system scheduling scheme is transmitted to the control execution system by means of the communication device and the core data is transmitted to the corresponding display interface for graphical display.
7. The method for dynamically scheduling manufacturing resources of a hybrid manufacturing system according to claim 1, wherein the step S18 is implemented by data acquisition software and hardware devices to complete real-time data acquisition and monitoring of upper order data change inside the manufacturing system, and store corresponding data in a real-time database.
8. The method for dynamically scheduling manufacturing resources of a hybrid manufacturing system according to claim 1, wherein the step S19 is to perform autonomous monitoring of data values on related data items in a real-time database, set a data value fluctuation security domain, and construct a mapping relationship between data value fluctuation and rearrangement mechanism triggering.
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 CN113627759A (en) | 2021-11-09 |
| CN113627759B true 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 |
| 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 |
| US9727829B2 (en) * | 2009-11-25 | 2017-08-08 | General Electric Company | Systems and methods for multi-resource scheduling |
| 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 | 中国航天系统科学与工程研究院 | 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 |
| 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 |
| 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 (9)
| 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 |
| 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 |
| 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 | 中国航天系统科学与工程研究院 | 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).2018,全文. * |
| 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).2018,全文. * |
| 基于MES的离散型制造业的高级计划排产的应用研究;吴雁;王彦瑞;张杰人;王犁;王开发;;制造技术与机床(第08期);全文 * |
| 基于感知信息的车间动态排产方法;张伟;于成龙;侯俊杰;徐皓;赵颖;;现代制造工程(第02期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113627759A (en) | 2021-11-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Fuller et al. | Fast heuristics for transmission-line switching | |
| 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 | |
| Wang et al. | An approach for integrated generation and transmission maintenance scheduling considering N-1 contingencies | |
| CN109946965A (en) | A kind of Discrete Production Workshop scheduled production method based on improved multiple target Jaya algorithm | |
| CN115600774A (en) | Multi-target production scheduling optimization method for assembly type building component production line | |
| Pan et al. | A single machine-based scheduling optimisation model integrated with preventive maintenance policy for maximising the availability | |
| CN113627759B (en) | Dynamic production scheduling method for manufacturing resources of hybrid wire manufacturing system | |
| Lian et al. | Research on complex product design change propagation based on complex networks | |
| Li | Parallel nonconvex generalized Benders decomposition for natural gas production network planning under uncertainty | |
| Zhang et al. | Counterfactual-attention multi-agent reinforcement learning for joint condition-based maintenance and production scheduling | |
| CN109213105B (en) | Reconfigurable device, reconfigurable realization method and distributed control system | |
| CN101546273B (en) | Method for forecasting execution time of software process | |
| CN102184316B (en) | Method for automatically selecting type of variable-force spring and determining type information | |
| CN102226913A (en) | Method for automatically generating Bug repair plan and Bug repair method | |
| CN115062925A (en) | Method and device for evaluating toughness of natural gas pipe network based on uncertainty disturbance | |
| CN112784447B (en) | Nuclear power plant accident modeling method for DET and RELAP5 program dynamic coupling framework | |
| CN113344317A (en) | Close cooperation type supply chain task scheduling method based on double-depth time sequence differential neural network | |
| CN110675088A (en) | Efficient division method for complex tasks of digital twin system | |
| Liu | Research and application of computer aided analysis and optimization algorithm for fault tree | |
| CN115577542B (en) | Model data driven aviation complex structure and reliability fusion design method | |
| CN114036823B (en) | Power transformer load control method and device based on coding-decoding and memory mechanism | |
| Hu et al. | Optimization Analysis of Product Design Process Based on Reinforcement Learning Algorithm. | |
| Xiwang et al. | An improved multi-objective scatter search approach for solving selective disassembly optimization problem | |
| Fen | AN IMPROVED QUANTUM EVOLUTIONARY ALGORITHM FOR LARGE-SCALE MANUFACTURING WORKSHOP SCHEDULING PROBLEM. |
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 |