CN106527373B - Workshop Autonomous Scheduling system and method based on multiple agent - Google Patents
Workshop Autonomous Scheduling system and method based on multiple agent Download PDFInfo
- Publication number
- CN106527373B CN106527373B CN201611100675.9A CN201611100675A CN106527373B CN 106527373 B CN106527373 B CN 106527373B CN 201611100675 A CN201611100675 A CN 201611100675A CN 106527373 B CN106527373 B CN 106527373B
- Authority
- CN
- China
- Prior art keywords
- intelligent body
- workpiece
- production
- information
- equipment
- 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
- 238000000034 method Methods 0.000 title claims abstract description 57
- 238000004519 manufacturing process Methods 0.000 claims abstract description 263
- 238000009826 distribution Methods 0.000 claims abstract description 35
- 230000008569 process Effects 0.000 claims description 27
- 238000003860 storage Methods 0.000 claims description 14
- 238000003754 machining Methods 0.000 claims description 13
- 230000002159 abnormal effect Effects 0.000 claims description 11
- 238000011156 evaluation Methods 0.000 claims description 11
- 238000005457 optimization Methods 0.000 claims description 11
- 238000012546 transfer Methods 0.000 claims description 7
- 230000005540 biological transmission Effects 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 abstract description 7
- 238000012545 processing Methods 0.000 description 12
- 230000008859 change Effects 0.000 description 5
- 238000013459 approach Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 241000196324 Embryophyta Species 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 230000002068 genetic effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000010845 search algorithm Methods 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000000137 annealing Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000012407 engineering method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000003923 mental ability Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
Classifications
-
- 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
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The present invention relates to a kind of Job-Shop system and method, and in particular to a kind of workshop Autonomous Scheduling system and method based on multiple agent.The problem of in order to realize workshop Autonomous Scheduling, the workshop Autonomous Scheduling system of the invention based on multiple agent include production information library, facility information library, workpiece intelligent body group, device intelligence body group and physical-distribution intelligent body；The physical-distribution intelligent body is configured to execute the instruction that the workpiece intelligent body group that receives is sent, and is capable of the working condition of real-time monitoring its corresponding logistics tool.Intelligent body of the invention covers all workpiece, equipment and logistics tool, therefore the data that can be easily collected into production process, and then monitor production status and equipment operation condition, it breaks down and alarm, it is adaptively adjusted according to the variation of workshop condition, improves the robustness and reliability of system.
Description
Technical field
The present invention relates to a kind of Job-Shop system and method, and in particular to it is a kind of based on the workshop of multiple agent from homophony
Spend system and method.
Background technique
Job-Shop is the key that realize production high efficiency, high flexibility.Job-Shop generally comprise task-set to be processed plus
Construction equipment collection and performance indicator collection, the purpose is to the processing requests according to object to be processed, on the basis of existing process equipment,
A scheduling rule is selected, so that performance indicator collection (comprising one or more performance indicator) be made to be optimal.Job-Shop
Object to be processed in problem generally requires one or more process equipment and is processed in a certain order, and an equipment exists
Sometime point can only process a workpiece, and a workpiece can only be added in sometime point by a process equipment
Work solves and optimizes that such as time is most short, scheduling scheme problem of the most low performance indicator of cost belongs to NP (Non-
Deterministic Polynomial, nondeterministic polynomial) problem.
As the research hotspot of artificial intelligence field, multi-agent system is the set of multiple intelligent body compositions, its mesh
Mark be will be big and complicated system Construction at it is small, each other communicate and coordinate, the system being easily managed.Multiple agent system
Intelligent body in system possesses adaptivity, learning ability, the ability interacted with environment and other intelligent bodies, by appropriate
Intelligent body is organized and just obtains multi-agent system by structure.Intelligent body in multi-agent system plays different angles
Color, it is mutually coordinated, it cooperates to complete complicated task.
With the expansion of workshop scale, constraint condition increases, and the dynamic complexity of Job-Shop is more and more prominent.Mesh
Before, when multi-agent system is applied to Job-Shop, management-resource-task intelligent body three-decker is generally used, point
Not complicated co-ordination, management process equipment resource and reception distributed tasks.It is this that multi-agent system is applied to workshop tune
Job-Shop is actually only divided into several modules and handled by the mode of degree, without by basic workpiece, equipment and
Logistics tool cannot achieve workshop Autonomous Scheduling as primary mental ability unit, i.e., can not solve workshop by the interaction of intelligent body
The problem of scheduling.
Based on this, this field needs new workshop Autonomous Scheduling system and method to solve the above problems.
Summary of the invention
In order to solve the problem of the above problem in the prior art in order to realize workshop Autonomous Scheduling, the present invention is provided
A kind of workshop Autonomous Scheduling system based on multiple agent.
The workshop Autonomous Scheduling system includes: production information library, facility information library, workpiece intelligent body group, device intelligence body
Group and physical-distribution intelligent body；
The production information library is configured to storage production information；
The facility information library is configured to storage production equipment information；
The workpiece intelligent body group is configured to receive production plan and reads and connect from the production information library
The production information that the production plan of receipts matches, and the production information corresponding with the received production plan of institute read is sent
To the device intelligence body group；
The device intelligence body group is configured to receive production information that the workpiece intelligent body group is sent and from institute
It states facility information library and reads production equipment information, and is feasible to production plan progress according to the production equipment information read
Property judgement；
The physical-distribution intelligent body is configured to execute the instruction that the workpiece intelligent body group received is sent, and energy
The working condition of enough its corresponding logistics tools of real-time monitoring.
In above-mentioned workshop Autonomous Scheduling system, the workpiece intelligent body group includes: that workpiece general pipeline intelligent body, workpiece are in charge of intelligence
It can body and workpiece intelligent body.
The corresponding workpiece to be processed of each workpiece intelligent body, the workpiece intelligent body can monitor in real time it is described to
The machining state of workpieces processing, and the machining state data of the workpiece to be processed are uploaded into the workpiece and are in charge of intelligent body；
Each workpiece is in charge of intelligent body, is configured to receive the data that the workpiece intelligent body uploads, and by the number
According to uploading to the workpiece general pipeline intelligent body；
The workpiece general pipeline intelligent body is configured to receive the data that the workpiece is in charge of intelligent body upload, to supervise in real time
Control the machining state of workshop workpiece.
In above-mentioned workshop Autonomous Scheduling system, the device intelligence body group includes: that equipment general pipeline intelligent body, equipment are in charge of intelligence
It can body and device intelligence body.
Each corresponding process equipment of the device intelligence body, the device intelligence body can monitor process equipment in real time
Working condition, and the operating state data of the process equipment is uploaded into the equipment and is in charge of intelligent body；
Each equipment is in charge of intelligent body, is configured to receive the data that the device intelligence body uploads, and by the number
According to uploading to equipment general pipeline intelligent body；
The equipment general pipeline intelligent body is configured to receive the data that the equipment is in charge of intelligent body upload, to supervise in real time
Control the equipment state in workshop.
In above-mentioned workshop Autonomous Scheduling system, the workpiece general pipeline intelligent body can receive production plan and from the life
It produces information bank and reads the production information to match with received production plan, and the production information read is sent to described set
Standby general pipeline intelligent body；
The equipment general pipeline intelligent body receives the production information that the workpiece general pipeline intelligent body is sent and from the equipment
Information bank reads production equipment information, and carries out feasibility to the production plan according to the production equipment information read and sentence
It is disconnected；
The production information of the production information library storage includes production content information, production constraint information and optimization aim letter
Breath；The production equipment information of the facility information library storage includes the essential information and production information of production equipment.
In above-mentioned workshop Autonomous Scheduling system, the system further include:
Behavior record library, be configured to receive the workpiece be in charge of intelligent body and the equipment be in charge of intelligent body transmission in life
The data generated during producing；
Decision support library, is configured to storage information for supporting some decision, and the information for supporting some decision includes scheduling strategy, evaluation letter
Breath and abnormal problem solution；And the data that the decision support library can be stored according to the behavior record library carry out more
Newly.
In above-mentioned workshop Autonomous Scheduling system, the equipment general pipeline intelligent body judges according to the production equipment information read
When production plan is feasible, each equipment is in charge of the corresponding production equipment information of intelligent body and sent out by the equipment general pipeline intelligent body
The workpiece general pipeline intelligent body is given, the workpiece general pipeline intelligent body is in charge of the production equipment of intelligent body according to each equipment
Production plan is decomposed into each production task by information, and transfers the production task to be in charge of intelligent body corresponding to the equipment
Workpiece is in charge of intelligent body；
It is described to set when the equipment general pipeline intelligent body judges that production plan is infeasible according to the production equipment information read
Standby general pipeline intelligent body by production plan can not row information feed back to the workpiece general pipeline intelligent body, the workpiece general pipeline intelligent body hair
The infeasible prompt of production plan out.
In above-mentioned workshop Autonomous Scheduling system, the workpiece is in charge of after intelligent body receives production task information, from described
Equipment is in charge of intelligent body and obtains corresponding production equipment information, and according to the production equipment information and the production task information
Establish task model；The workpiece is in charge of intelligent body and reads scheduling plan from the decision support library according to the task model of foundation
Slightly, and the task model and the scheduling strategy are sent to workpiece intelligent body.
In above-mentioned workshop Autonomous Scheduling system, the workpiece intelligent body is according to the task model and the tune received
Degree strategy, calculates optimal scheduling scheme, and be sent to the workpiece and be in charge of intelligent body；The workpiece is in charge of intelligent body will be described
Optimal scheduling scheme is sent to the workpiece general pipeline intelligent body, and the workpiece general pipeline intelligent body is according to the optimal tune received
Degree scheme redistributes the production task, and carries out feasibility judgement to the production task redistributed.
In above-mentioned workshop Autonomous Scheduling system, the production task redistributed described in the workpiece general pipeline intelligent body judgement can
When row, the workpiece general pipeline intelligent body sends feasibility instruction and is in charge of intelligent body to the workpiece, and the workpiece is in charge of intelligent body
Feasibility instruction is sent to the workpiece intelligent body, the workpiece intelligent body, which receives the feasibility and instructs and send, appoints
The physical-distribution intelligent body is arrived in business instruction, and the physical-distribution intelligent body receives and executes the assignment instructions；The workpiece general pipeline intelligence
When the production task redistributed described in body judgement is infeasible, the workpiece general pipeline intelligent body issues that production plan is infeasible to be mentioned
Show.
On the other hand, the workshop Autonomous Scheduling method based on multiple agent that the present invention also provides a kind of, this method include
The following steps:
Step 10 receives production plan by workpiece general pipeline intelligent body and sends it to equipment general pipeline intelligent body；
Step 20, the equipment general pipeline intelligent body carry out feasibility judgement to the received production plan of institute；
Step 30, in the case of production plan is feasible, the workpiece general pipeline intelligent body decomposes production plan, and
It transfers production task and is in charge of intelligent body to workpiece；
Step 40, the workpiece are in charge of intelligent body and establish task model according to the production task received, and according to described
Task model reads scheduling strategy from decision support library, and then the task model and the scheduling strategy are sent to workpiece intelligence
It can body；
Step 50, the workpiece intelligent body send task according to the task model and the scheduling strategy received
Instruct physical-distribution intelligent body；
Step 60, the physical-distribution intelligent body execute the instruction received.
In the above-mentioned methods, step 30 further include:
Step 301, in the case of production plan is infeasible, the equipment general pipeline intelligent body is by the infeasible letter of production plan
Breath feeds back to the workpiece general pipeline intelligent body, and the workpiece general pipeline intelligent body issues the infeasible prompt of production plan.
In the above-mentioned methods, step 40 further include:
Step 401, the workpiece are in charge of after intelligent body receives production task information, are in charge of intelligent body from the equipment and obtain
Production equipment information is taken, and task model is established according to the production equipment information and the production task information.
In the above-mentioned methods, step 50 further include:
Step 501, workpiece intelligent body carry out parallel computation to the task model received and the scheduling strategy and obtain
To optimal scheduling scheme, and the optimal scheduling scheme is sent to the workpiece and is in charge of intelligent body；
Step 502, the workpiece are in charge of intelligent body and the optimal scheduling scheme are sent to the workpiece general pipeline intelligent body,
The workpiece general pipeline intelligent body redistributes the production task according to the optimal scheduling scheme received, and right
The production task redistributed carries out feasibility judgement；
Step 503, in the case of the optimal scheduling concept feasible, the workpiece general pipeline intelligent body sends feasibility and refers to
The workpiece is enabled to be in charge of intelligent body, the workpiece is in charge of intelligent body and feasibility instruction is sent to the workpiece intelligence
Body, the workpiece intelligent body receive the feasibility and instruct and send assignment instructions to the physical-distribution intelligent body, the logistics intelligence
Energy body receives and executes the assignment instructions；
Step 504, in the case of the optimal scheduling scheme is infeasible, the workpiece general pipeline intelligent body issues production meter
Draw infeasible prompt.
In conclusion plant site resource of the invention is divided into workpiece intelligent body group, device intelligence body group and physical-distribution intelligent
Single workpiece to be processed, process equipment and logistics tool are accordingly to be regarded as intelligent body by body group, pass through the mutual association between intelligent body
Make reception, decentralization, Autonomous Scheduling and the implementation of realization production plan.In addition, be equivalent to using multi-agent system introduce it is more
A expert system is complementary to one another between them, cooperates, complicated problem itself can be simplified, and multiple agent is efficiently simultaneously
Row processing is capable of handling extensive scheduling problem, improves production efficiency and product quality, reduces cost.Also, due to the present invention
In intelligent body cover all workpieces to be processed, equipment and logistics tool, therefore can easily be collected into production process
Data, and then monitor production status and equipment operation condition, break down and alarm, it is adaptive according to the variation of workshop condition
It should adjust, improve the robustness and reliability of system.
Detailed description of the invention
Fig. 1 is the structural schematic diagram of the workshop Autonomous Scheduling system of the invention based on multiple agent；
Fig. 2 is the flow chart of the workshop Autonomous Scheduling method of the invention based on multiple agent.
Specific embodiment
The preferred embodiment of the present invention described with reference to the accompanying drawings.It will be apparent to a skilled person that this
A little embodiments are used only for explaining technical principle of the invention, it is not intended that limit the scope of the invention.
Workshop Autonomous Scheduling system disclosed by the invention based on multiple agent, the resource that plant site is related to are divided into
Workpiece intelligent body group, device intelligence body group and physical-distribution intelligent body.Pass through workpiece intelligent body group, device intelligence body group and physical-distribution intelligent
Mutually coordinated and cooperation between body, completes the whole process that output of products is input to from production plan.
Technical solution of the present invention is described in detail with reference to the accompanying drawing.As shown in Figure 1, of the invention based on more intelligence
The workshop Autonomous Scheduling system of energy body mainly includes production information library, facility information library, workpiece intelligent body group, device intelligence body group
With physical-distribution intelligent body.
Production information library is configured to storage production information.Specifically, the production information of production information library storage is mainly wrapped
Include production content (such as production product category, production quantity, processing method, process time and duration etc.), optimization aim (such as
Minimum time, the lowest energy consumption etc. that entire production process can reach), production constraint (such as time, submission time and excellent
First constraint etc.) etc. information.
Facility information library is configured to storage production equipment information, specifically includes essential information (such as the equipment of production equipment
Title, type, time etc.) and the production information (such as speed of production, precision of equipment etc.) of equipment etc..
Workpiece intelligent body group is configured to receive production plan and read and the received production of institute from production information library
Plan the production information to match, and the production information corresponding with the received production plan of institute read is sent to equipment intelligence
It can body group.Wherein, workpiece intelligent body group includes that workpiece general pipeline intelligent body, workpiece are in charge of intelligent body and workpiece intelligent body.
Device intelligence body group is configured to receive the production information that workpiece intelligent body group is sent and from facility information library
Production equipment information is read, and feasibility judgement is carried out to production plan according to the production equipment information read.Wherein, equipment
Intelligent body group includes that equipment general pipeline intelligent body, equipment are in charge of intelligent body and device intelligence body.
Physical-distribution intelligent body is configured to receive and execute the instruction of workpiece intelligent body group transmission, and can real-time monitoring its
The working condition of corresponding logistics tool.Wherein, each physical-distribution intelligent body corresponds to one for transporting the logistics of tool to be processed
Tool, the working condition of the corresponding logistics tool of physical-distribution intelligent body real-time monitoring, and when logistics tool breaks down, it sends out in time
Alarm and reminding out.
In one embodiment of the invention, workpiece general pipeline intelligent body and equipment general pipeline intelligent body are located in field control
The heart.Workpiece is in charge of intelligent body and equipment is in charge of intelligent body and is located at corresponding fixed area, i.e., respective be responsible for production area, and
And have display, alarm, bar code or RFID scanning read-write, data storage, it is wired and wireless communication, high-performance calculation ability and
Database access and production status monitor permission.Workpiece intelligent body, device intelligence body and physical-distribution intelligent body pass through respectively absorption or
The mode of clamping is fixed on workpiece, equipment and logistics tool, has alarm, wireless communication, data storage and low performance
Computing capability, and there is the permission for monitoring itself workpiece or equipment state.
The workshop Autonomous Scheduling system further includes behavior record library and decision support library.
Behavior record library is configured to receive that workpiece is in charge of intelligent body and equipment is in charge of intelligent body and is sent in process of production
The data of generation.Specifically include what workpiece intelligent body group, device intelligence body group and physical-distribution intelligent body generated in process of production
Data.
Decision support library is configured to storage information for supporting some decision, which mainly includes scheduling strategy, evaluation
Information and abnormal problem countermeasure etc..Specifically, scheduling strategy includes scheduling model, method for solving, solves parameter setting, intelligence
It can the distribution of body task, implementing result feedback and evaluation score.Wherein, method for solving includes that the corresponding basic operational research of model is calculated
Method, Neighborhood-region-search algorithm and the algorithm based on agency；Evaluation score is directed to different models, for describing algorithm for the suitable of model
Answer degree.Evaluation information includes the error rate, production status, degree of aging of intelligent body controller, logistics tool and process equipment
Deng and the obtained overall merit score of weighting.Abnormal problem countermeasure mainly includes current task change, equipment fault, intelligence out
Processing scheme when energy body failure or insufficient raw material.In addition, what decision support library can be stored according to behavior record library
Data are updated, i.e., with the progress of production, information for supporting some decision can be constantly updated and expand.
In the present embodiment, workshop is divided into multiple fixed area, each fixed area is provided with corresponding workpiece and is in charge of
Intelligent body and equipment are in charge of intelligent body to manage the workpiece intelligent body of the fixed area and device intelligence body respectively.
The corresponding workpiece to be processed of each workpiece intelligent body, workpiece intelligent body can monitor adding for workpiece to be processed in real time
Work state, and the machining state data of workpiece to be processed are uploaded into corresponding workpiece and are in charge of intelligent body；Each device intelligence body
A corresponding process equipment, device intelligence body can monitor the working condition of process equipment in real time, and by the work of process equipment
Status data uploads to corresponding equipment and is in charge of intelligent body.
Each workpiece is in charge of intelligent body and is configured to receive the data that workpiece intelligent body uploads, and it is total to upload the data to workpiece
Pipe intelligent body.Each equipment is in charge of the data that intelligent body is configured to the upload of receiving device intelligent body, and upload the data to equipment
General pipeline intelligent body.Each equipment is in charge of status information (such as vibration, temperature for the equipment that intelligent body can also obtain in fixed area
Degree, sound etc., can not obtain automatically by manual inspection and typing), and send facility information for the status information of equipment
Library, for updating facility information library.
All intelligent bodies relevant to workpiece of workpiece general pipeline intelligent body general pipeline can receive workpiece and be in charge of intelligent body upload
Data, to monitor the machining state of workshop workpiece in real time.When monitoring the machining state of workshop workpiece, monitoring can be passed through
Platform shows manufacturing schedule, the fault in production lamp information in workshop, and will be in the presence of timely feedbacking to vehicle in production process
Between administrative staff.Workpiece general pipeline intelligent body is responsible for receiving production plan and be read and received production plan from production information library
The production information to match, the production information include production content information, production constraint information and optimization aim information etc., and will
The production information is sent to equipment general pipeline intelligent body.
Equipment general pipeline intelligent body general pipeline all devices correlation intelligent body, can receiving device be in charge of intelligent body upload number
According to monitor the equipment state in workshop in real time, and device status information being reported to workshop management personnel in time.Equipment general pipeline
Intelligent body receives the production information that workpiece general pipeline intelligent body is sent and reads production equipment information from facility information library, according to vehicle
Between production equipment information the feasibility of production plan is judged, that is, judge whether the condition of production equipment meets production plan
Requirement.
Equipment general pipeline intelligent body judges infeasible (the i.e. production equipment of production plan according to the production equipment information that reads
Condition is unsatisfactory for production plan) when, equipment general pipeline intelligent body by production plan can not row information feed back to workpiece general pipeline intelligent body,
Workpiece general pipeline intelligent body issues the infeasible prompt of production plan, and by plan, department reformulates production plan.
Equipment general pipeline intelligent body judges feasible (the i.e. item of production equipment of production plan according to the production equipment information read
Part meets production plan) when, each equipment is in charge of the corresponding production equipment information of intelligent body and is sent to by equipment general pipeline intelligent body
Workpiece general pipeline intelligent body, workpiece general pipeline intelligent body according to each equipment be in charge of the production equipment information of intelligent body to production plan into
Row decomposes, and transfers production task and be in charge of the corresponding workpiece of intelligent body to equipment and be in charge of intelligent body.
After workpiece is in charge of the production task (production plan after decomposing) that intelligent body receives decentralization, fixed from this is responsible for
The equipment of regional production equipment is in charge of acquisition production equipment information at intelligent body comprising the quantity of production equipment, every procedure
Production time, the buffer area of equipment being applicable between limitation, equipment etc., in conjunction with production task information (process time, duration
Etc. essential informations and optimization aim) establish task model.Specifically, workpiece be in charge of intelligent body according to production equipment information and
The task model that production task information is established generally uses<E, R, and O>triple indicates that E represents environment, and R, which is represented, to be constrained, O generation
Entry mark.Wherein, environment representation Workshop Production facility environment mainly includes Flow Shop, processing workshop and opens workshop, if
There is LPT device, above-mentioned workshop condition can all have certain flexibility；Workpiece in Flow Shop has determining identical processing
Path, the workpiece in processing workshop have a determining machining path, these machining paths may it is identical may also be different, open vehicle
Between without specific machining path, it is generally uncommon；The addition of LPT device, so that the processing node in identical machining path has
Mutiple Choice increases the flexibility of processing workshop.Constraint mainly includes task restriction, facility constraints and other constraints；Task
Constraint includes the submission date, processes the specified constraint content of priority restrictions task；Facility constraints include the workpiece of equipment application
Constraint and time-constrain, workpiece are processed in a kind of equipment, but not all same type equipment can process the workpiece, together
When, the time point of equipment application and the maximum duration of continuous work also have certain limitation；Other constraints, which refer to, removes above-mentioned constraint
Except other constraint.Target mainly dispatches the index to be optimized, and usually minimizes or maximize some objective function, and one
As have the manufacture phase that minimizes, minimize the targets such as maximum delay time.
Task model triple is combined according to the actual situation, for specific workshop, in most cases task
Triple be all it is determining, i.e., task model is known.Corresponding task model has mature dispatching algorithm (such as determination
Property processing workshop, by Adams, Balas and Zawack propose Bottleneck Procedure for Job heuritic approach, for solving production scene more
Traditional genetic algorithm is applied to process by the universal minimum manufacture phase without constraint processing workshop problem, Nakano and Yamada
The method of Job-Shop acquires the feasible solution as close possible to optimal solution by coding appropriate, on those bases, with mould
The heuritic approaches such as quasi- annealing, tabu search algorithm combine, and have obtained more accurate or can solve and deposit for various constrain
In the case that scheduling problem solution, achievement in recent years includes improved heuritic approach application, such as improve heredity
Algorithm, improvement artificial fish-swarm algorithm achieve good achievement, these method applications are strong, are capable of handling constraint and multiple target
Optimization problem；For multi-agent systems, the method based on market and agency is simple and easy, although precision is excellent compared with other specific aims
It is poor to change algorithm, but its is adaptable, can find preferable feasible solution, in addition study mechanism, can be used for handling complex environment
Scheduling problem；Genetic algorithm and based on the method for agency in some ERP and MES system, such as in famous SAP system have
It embodies；Other task models are not repeated, but generally can find corresponding feasible solution by existing algorithm), these calculations
Method is stored in decision support library.
Workpiece is in charge of intelligent body and reads scheduling strategy, and appointing foundation from decision support library according to the task model of foundation
Business model and the scheduling strategy read are sent to workpiece intelligent body.Specifically, workpiece is in charge of after task model foundation
Intelligent body is inquired first either with or without identical example in decision support library, directly by the tune in the example if having identical example
Degree strategy is sent to workpiece intelligent body；If selecting corresponding scheduling strategy according to task model without identical example, according to
Task model information is scheduled the change of policing parameter, and obtained scheduling strategy is sent to workpiece intelligent body；If not yet
Have identical example, also not scheduling strategy corresponding with task model when, workpiece be in charge of intelligent body can be used based on agency
Either Knowledge based engineering method finds feasible schedule strategy, and obtained scheduling strategy is sent to workpiece intelligent body.
In a possible embodiment, workpiece is in charge of intelligent body in the corresponding scheduling strategy of decision support library inquiry
When, it can be selected on earth according to evaluation score of the scheduling strategy algorithm to task model by height.
Workpiece intelligent body passes through multiple workpiece intelligent body parallel computations according to received task model and scheduling strategy
Mode seeks the optimal solution of task distribution, and obtained optimal scheduling scheme is sent to workpiece general pipeline intelligent body；Workpiece general pipeline
Intelligent body redistributes production task according to the optimal scheduling scheme received, and the newly assigned production task of counterweight into
The judgement of row feasibility.Specifically, since the production capacity of each fixed area is different, workpiece general pipeline intelligent body distributes to workpiece point
The production task index of pipe intelligent body may be unable to satisfy the optimal scheduling scheme, may be just met for the optimal scheduling scheme,
The optimal scheduling scheme may also be exceeded.Therefore, workpiece general pipeline intelligent body redistributes production task, so that each work
Part, which is in charge of the fixed area that intelligent body is responsible for, can complete production task.Each workpiece is in charge of intelligent body by workpiece general pipeline intelligent body
The responsible regional production ability information of institute is sent to equipment general pipeline intelligent body and production information library is written, and checks manufacture phase, delay etc.
Production task requires whether meet, and when the production task redistributed is infeasible, (production tasks such as manufacture phase, delay require discontented
Foot) when, workpiece general pipeline intelligent body issues the infeasible prompt of production plan, reformulates production plan by administrative staff.
The production task that the judgement of workpiece general pipeline intelligent body is redistributed is feasible, and (production tasks such as manufacture phase, delay require full
Foot) when, workpiece general pipeline intelligent body sends feasibility instruction and is in charge of intelligent body to workpiece, and workpiece is in charge of intelligent body and instructs feasibility
It is sent to workpiece intelligent body, workpiece intelligent body receives feasibility and instructs and send assignment instructions to physical-distribution intelligent body, physical-distribution intelligent
Body receives and executes the assignment instructions.Specifically, received instruction is sent to logistics tool controller, logistics by physical-distribution intelligent body
Tool to be processed is carried at specified process equipment by tool controller control logistics workpiece to be processed, and then is sequentially completed
Production task.Data obtained in workpiece intelligent body and device intelligence body record production process, and upload to workpiece respectively and be in charge of
Intelligent body and equipment are in charge of intelligent body and are temporarily stored.When occurring abnormal problem in process of production, inquire in decision support library
Abnormal problem countermeasure selects corresponding solution, when there is no the solution of the abnormal problem in decision support library,
It can sound an alarm at this time and remind workshop management personnel.
After the completion of production plan, workpiece is in charge of intelligent body and equipment is in charge of intelligent body and believes the data received and operation
Breath, abnormal problem solution and evaluation information etc. are stored in behavior record library；It reads in behavior record library in decision support library
Content, the evaluation score of scheduling strategy, the scheduling strategy parameter after record optimization are adjusted according to executive condition, reading executed
The abnormal condition that occurs in journey simultaneously records solution, excavates potential problem in data by data analysis and carries out failure
Early warning.
On the other hand, the workshop Autonomous Scheduling method based on multiple agent that the present invention also provides a kind of.This method includes
Following steps:
Step 10 receives production plan by workpiece general pipeline intelligent body and sends it to equipment general pipeline intelligent body.At this
In step, workpiece general pipeline intelligent body carries out preliminary analysis to received production plan, obtains production content information, production constraint letter
Breath and optimization aim information, and production content information, the production constraint information and the optimization aim information are sent to and are set
Standby general pipeline intelligent body.
Step 20, equipment general pipeline intelligent body carry out feasibility judgement to the received production plan of institute.In this step, equipment
General pipeline intelligent body receives production content information, production constraint information and optimization aim information and reads facility information library, and root
The feasibility of production plan is judged according to the production equipment information read.
Step 30, in the case of production plan is feasible, the workpiece general pipeline intelligent body decomposes production plan, and
It transfers production task and is in charge of intelligent body to workpiece.Specifically, each equipment is in charge of intelligent body and is responsible for region by equipment general pipeline intelligent body
Production capacity information be sent to workpiece general pipeline intelligent body, production plan is decomposed by workpiece general pipeline intelligent body and transfer to
Workpiece is in charge of intelligent body.In the case of production plan is infeasible, equipment general pipeline intelligent body by production plan can not row information it is anti-
It feeds the workpiece general pipeline intelligent body, workpiece general pipeline intelligent body issues the infeasible prompt of production plan.
Step 40, workpiece are in charge of intelligent body and establish task model according to the production task received, and according to task model
Scheduling strategy is read from decision support library, and then task model and scheduling strategy are sent to workpiece intelligent body.In this step,
Workpiece is in charge of after intelligent body receives production task information, is in charge of intelligent body from equipment and obtains production equipment information, and according to life
It produces facility information and production task information establishes task model.It can specifically participate in described above.
Step 50, workpiece intelligent body send assignment instructions to logistics intelligence according to received task model and scheduling strategy
It can body.In this step, it may further comprise:
Step 501, workpiece intelligent body carry out parallel computation to the task model and scheduling strategy received and obtain optimal tune
Degree scheme, and optimal scheduling scheme is sent to workpiece and is in charge of intelligent body.
Step 502, workpiece are in charge of intelligent body and optimal scheduling scheme are sent to workpiece general pipeline intelligent body, workpiece general pipeline intelligence
Body redistributes production task according to the optimal scheduling scheme received, and the newly assigned production task progress of counterweight can
Row judgement.
Step 503, in the case of optimal scheduling concept feasible, workpiece general pipeline intelligent body sends feasibility instruction to workpiece
It is in charge of intelligent body, workpiece is in charge of intelligent body and feasibility instruction is sent to workpiece intelligent body, and workpiece intelligent body receives feasibility and refers to
It enables and sends assignment instructions to physical-distribution intelligent body, physical-distribution intelligent body receives and executes assignment instructions.
Step 504, in the case of optimal scheduling scheme is infeasible, workpiece general pipeline intelligent body issue production plan it is infeasible
Prompt.
Step 60, physical-distribution intelligent body executes the instruction received.Specifically, received instruction is sent to by physical-distribution intelligent body
Logistics tool controller, logistics tool controller control logistics workpiece tool to be processed is carried at specified process equipment into
Row processing, and then it is sequentially completed production task.
In the present embodiment, after the completion of production plan, workpiece, which is in charge of intelligent body, and equipment is in charge of intelligent body to receive
Data and operation information, abnormal problem solution and evaluation information etc. are stored in behavior record library；Decision support library according to
Content in behavior record library, adjusts the evaluation score of scheduling strategy, the scheduling strategy parameter after record optimization, and reading executed
The abnormal condition that occurs in journey simultaneously records solution, excavates potential problem in data by data analysis and carries out failure
Early warning.
So far, it has been combined preferred embodiment shown in the drawings and describes technical solution of the present invention, still, this field
Technical staff is it is easily understood that protection scope of the present invention is expressly not limited to these specific embodiments.Without departing from this
Under the premise of the principle of invention, those skilled in the art can make equivalent change or replacement to the relevant technologies feature, these
Technical solution after change or replacement will fall within the scope of protection of the present invention.
Claims (12)
1. a kind of workshop Autonomous Scheduling system based on multiple agent, which is characterized in that the system includes: production information library, sets
Standby information bank, workpiece intelligent body group, device intelligence body group and physical-distribution intelligent body；
The production information library is configured to storage production information；
The facility information library is configured to storage production equipment information；
The workpiece intelligent body group is configured to receive production plan and read from the production information library received with institute
The production information that production plan matches, and the production information corresponding with the received production plan of institute read is sent to institute
State device intelligence body group；
The device intelligence body group is configured to receive the production information and set from described that the workpiece intelligent body group is sent
Standby information bank reads production equipment information, and carries out feasibility to the production plan according to the production equipment information read and sentence
It is disconnected；
The physical-distribution intelligent body is configured to execute the instruction that the workpiece intelligent body group received is sent, and can be real
When monitor the working condition of its corresponding logistics tool；
The workpiece intelligent body group includes: that workpiece intelligent body, workpiece are in charge of intelligent body and workpiece general pipeline intelligent body；
Each corresponding workpiece to be processed of the workpiece intelligent body, the workpiece intelligent body can monitor in real time described to be processed
The machining state of workpiece, and the machining state data of the workpiece to be processed are uploaded into the workpiece and are in charge of intelligent body；
Each workpiece is in charge of intelligent body, is configured to receive the data that the workpiece intelligent body uploads, and will be in the data
Pass to the workpiece general pipeline intelligent body；
The workpiece general pipeline intelligent body is configured to receive the data that the workpiece is in charge of intelligent body upload, to monitor vehicle in real time
Between workpiece machining state.
2. Autonomous Scheduling system in workshop according to claim 1, which is characterized in that the device intelligence body group includes: to set
Standby intelligent body, equipment general pipeline intelligent body and equipment are in charge of intelligent body；
Each corresponding process equipment of the device intelligence body, the device intelligence body can monitor the work of process equipment in real time
Make state, and the operating state data of the process equipment is uploaded into the equipment and is in charge of intelligent body；
Each equipment is in charge of intelligent body, is configured to receive the data that the device intelligence body uploads, and will be in the data
Pass to equipment general pipeline intelligent body；
The equipment general pipeline intelligent body is configured to receive the data that the equipment is in charge of intelligent body upload, to monitor vehicle in real time
Between equipment state.
3. Autonomous Scheduling system in workshop according to claim 2, which is characterized in that
The workpiece general pipeline intelligent body can receive production plan and read from the production information library and receive production meter
The production information to match is drawn, and the production information read is sent to the equipment general pipeline intelligent body；
The equipment general pipeline intelligent body receives the production information that the workpiece general pipeline intelligent body is sent and from the facility information
Production equipment information is read in library, and carries out feasibility judgement to the production plan according to the production equipment information read；Its
In,
The production information of the production information library storage includes production content information, production constraint information and optimization aim information；
The production equipment information of the facility information library storage includes the essential information and production information of production equipment.
4. Autonomous Scheduling system in workshop according to claim 3, which is characterized in that the system further include:
Behavior record library, is configured to receive that the workpiece is in charge of intelligent body and the equipment is in charge of producing for intelligent body transmission
The data generated in journey；
Decision support library, be configured to storage information for supporting some decision, the information for supporting some decision include scheduling strategy, evaluation information and
Abnormal problem solution；And
The decision support library can be updated according to the data that the behavior record library stores.
5. Autonomous Scheduling system in workshop according to claim 4, which is characterized in that
When the equipment general pipeline intelligent body judges that production plan is feasible according to the production equipment information read, the equipment general pipeline
Each equipment is in charge of the corresponding production equipment information of intelligent body and is sent to the workpiece general pipeline intelligent body by intelligent body, described
Production plan is decomposed into each life according to the production equipment information that each equipment is in charge of intelligent body by workpiece general pipeline intelligent body
Production task, and transfer the production task and be in charge of the corresponding workpiece of intelligent body to the equipment and be in charge of intelligent body；
When the equipment general pipeline intelligent body judges that production plan is infeasible according to the production equipment information read, the equipment is total
Pipe intelligent body by production plan can not row information feed back to the workpiece general pipeline intelligent body, the workpiece general pipeline intelligent body issues life
It produces and plans infeasible prompt.
6. Autonomous Scheduling system in workshop according to claim 5, which is characterized in that
The workpiece is in charge of after intelligent body receives production task information, is in charge of intelligent body from the equipment and obtains corresponding production
Facility information, and task model is established according to the production equipment information and the production task information；
The workpiece is in charge of intelligent body and reads scheduling strategy from the decision support library according to the task model of foundation, and will be described
Task model and the scheduling strategy are sent to workpiece intelligent body.
7. Autonomous Scheduling system in workshop according to claim 6, which is characterized in that
The workpiece intelligent body calculates optimal scheduling scheme according to the task model and the scheduling strategy that receive,
And it is sent to the workpiece and is in charge of intelligent body；
The workpiece is in charge of intelligent body and the optimal scheduling scheme is sent to the workpiece general pipeline intelligent body, the workpiece general pipeline
Intelligent body redistributes the production task according to the optimal scheduling scheme received, and redistributes to described
Production task carry out feasibility judgement.
8. Autonomous Scheduling system in workshop according to claim 7, which is characterized in that
When the production task redistributed described in the workpiece general pipeline intelligent body judgement is feasible, the workpiece general pipeline intelligent body is sent
Feasibility instruction is in charge of intelligent body to the workpiece, and the workpiece is in charge of intelligent body and feasibility instruction is sent to the work
Part intelligent body, the workpiece intelligent body receives the feasibility and instructs and send assignment instructions to the physical-distribution intelligent body, described
Physical-distribution intelligent body receives and executes the assignment instructions；
When the production task redistributed described in the workpiece general pipeline intelligent body judgement is infeasible, the workpiece general pipeline intelligent body hair
The infeasible prompt of production plan out.
9. a kind of dispatching method of the workshop Autonomous Scheduling system based on multiple agent as claimed in claim 8, feature exist
In, including the following steps:
Step 10 receives production plan by workpiece general pipeline intelligent body and sends it to equipment general pipeline intelligent body；
Step 20, the equipment general pipeline intelligent body carry out feasibility judgement to the received production plan of institute；
Step 30, in the case of production plan is feasible, the workpiece general pipeline intelligent body decomposes production plan, and transfers
Production task is in charge of intelligent body to workpiece；
Step 40, the workpiece are in charge of intelligent body and establish task model according to the production task received, and according to the task
Model reads scheduling strategy from decision support library, and then the task model and the scheduling strategy are sent to workpiece intelligence
Body；
Step 50, the workpiece intelligent body send assignment instructions according to the task model and the scheduling strategy received
To physical-distribution intelligent body；
Step 60, the physical-distribution intelligent body execute the instruction received.
10. according to the method described in claim 9, it is characterized in that, step 30 further include: step 301, production plan can not
In the case of row, the equipment general pipeline intelligent body by production plan can not row information feed back to the workpiece general pipeline intelligent body, institute
It states workpiece general pipeline intelligent body and issues the infeasible prompt of production plan.
11. according to the method described in claim 9, it is characterized in that, step 40 further include:
Step 401, the workpiece are in charge of after intelligent body receives production task information, are in charge of intelligent body from the equipment and obtain life
Facility information is produced, and task model is established according to the production equipment information and the production task information.
12. according to the method described in claim 9, it is characterized in that, step 50 further include:
Step 501, workpiece intelligent body carry out parallel computation to the task model received and the scheduling strategy and obtain most
Excellent scheduling scheme, and the optimal scheduling scheme is sent to the workpiece and is in charge of intelligent body；
Step 502, the workpiece are in charge of intelligent body and the optimal scheduling scheme are sent to the workpiece general pipeline intelligent body, described
Workpiece general pipeline intelligent body redistributes the production task according to the optimal scheduling scheme received, and to described
The production task redistributed carries out feasibility judgement；
Step 503, in the case of the optimal scheduling concept feasible, the workpiece general pipeline intelligent body sends feasibility instruction and arrives
The workpiece is in charge of intelligent body, and the workpiece is in charge of intelligent body and feasibility instruction is sent to the workpiece intelligent body, institute
It states the workpiece intelligent body reception feasibility to instruct and send assignment instructions to the physical-distribution intelligent body, the physical-distribution intelligent body connects
It receives and executes the assignment instructions；
Step 504, in the case of the optimal scheduling scheme is infeasible, the workpiece general pipeline intelligent body issues production plan not
Feasible prompt.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611100675.9A CN106527373B (en) | 2016-12-05 | 2016-12-05 | Workshop Autonomous Scheduling system and method based on multiple agent |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611100675.9A CN106527373B (en) | 2016-12-05 | 2016-12-05 | Workshop Autonomous Scheduling system and method based on multiple agent |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106527373A CN106527373A (en) | 2017-03-22 |
| CN106527373B true CN106527373B (en) | 2019-02-01 |
Family
ID=58354799
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611100675.9A Active CN106527373B (en) | 2016-12-05 | 2016-12-05 | Workshop Autonomous Scheduling system and method based on multiple agent |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106527373B (en) |
Families Citing this family (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106873564B (en) * | 2017-04-26 | 2019-09-20 | 南京航空航天大学 | Flow-type multiple agent real-time scheduling method based on intelligent workshop |
| CN109213079B (en) * | 2017-06-29 | 2020-10-23 | 深圳模德宝科技有限公司 | Control method and device for automatic production |
| CN109213080B (en) * | 2017-06-29 | 2020-10-23 | 深圳模德宝科技有限公司 | Machine tool control method and device |
| WO2019037122A1 (en) * | 2017-08-25 | 2019-02-28 | 深圳市得道健康管理有限公司 | Artificial intelligence terminal and behavior control method therefor |
| CN107479428A (en) * | 2017-10-09 | 2017-12-15 | 上海德衡数据科技有限公司 | A kind of integrated data center operational system information processing method based on multiple agent |
| CN108181810B (en) * | 2017-12-15 | 2020-05-12 | 清华大学 | Robust single machine scheduling method based on interval uncertainty |
| CN108549977B (en) * | 2018-03-29 | 2022-07-26 | 华南理工大学 | Multi-Agent-based order-oriented flexible production dynamic scheduling system |
| CN108469798A (en) * | 2018-05-15 | 2018-08-31 | 哈尔滨理工大学 | A kind of Job-Shop system based on line side library feeding |
| CN108764580A (en) * | 2018-05-28 | 2018-11-06 | 延安大学 | A kind of assembly shop scheduling system |
| CN109190995B (en) * | 2018-09-18 | 2021-06-08 | 北京航空航天大学 | Multi-agent-based maintenance resource bidirectional joint scheduling strategy decision method |
| CN109255501B (en) * | 2018-10-29 | 2021-09-24 | 中国南方电网有限责任公司 | Multi-Agent artificial fish swarm algorithm-based cascade library long-term optimization scheduling algorithm |
| CN109765862B (en) * | 2018-12-26 | 2021-07-16 | 同济大学 | Mixed flow workshop sustainable scheduling control method based on adaptive genetic algorithm |
| CN110764472B (en) * | 2019-09-25 | 2022-04-05 | 南京航空航天大学 | Workshop real-time scheduling method and device based on composite agent |
| CN111260181B (en) * | 2019-12-31 | 2023-07-07 | 同济大学 | Workshop self-adaptive production scheduling device based on distributed intelligent manufacturing unit |
| CN111650895B (en) * | 2020-04-08 | 2023-07-04 | 上海航天精密机械研究所 | Automatic line production task dynamic scheduling method |
| CN113568374A (en) * | 2020-09-14 | 2021-10-29 | 宁波舜宇智能科技有限公司 | Method, device, electronic device and storage medium for controlling production process of product |
| CN112434440A (en) * | 2020-12-02 | 2021-03-02 | 武汉工程大学 | Intelligent parking method and system based on multi-Agent self-adaptive online verification |
| CN113093673B (en) * | 2021-03-31 | 2022-03-29 | 南京大学 | Method for optimizing workshop operation schedule by using mean field action value learning |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001331462A (en) * | 2000-05-23 | 2001-11-30 | Yokogawa Electric Corp | Agent base production system |
| JP2003044548A (en) * | 2001-07-31 | 2003-02-14 | Amada Co Ltd | Outsourcing method and system for sheet-metal working |
| US7127310B1 (en) * | 2002-08-30 | 2006-10-24 | Advanced Micro Devices, Inc. | Method and apparatus for determining cost functions using parameterized components |
| JP2007334733A (en) * | 2006-06-16 | 2007-12-27 | Yokogawa Electric Corp | Working information management system |
| CN101706886A (en) * | 2009-08-28 | 2010-05-12 | 华南理工大学 | Order-driven Single-piece small-batch combined flow production method for processing workshop |
| CN102542411A (en) * | 2011-12-22 | 2012-07-04 | 浙江工业大学 | Method for carrying out dispatching control on multi-variety multi-process manufacturing enterprise workshop on basis of ACA (Automatic Circuit Analyzer) model |
| CN106155781A (en) * | 2015-04-08 | 2016-11-23 | 中国科学院软件研究所 | A kind of real-time task scheduling method in multiple agent platform |
-
2016
- 2016-12-05 CN CN201611100675.9A patent/CN106527373B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001331462A (en) * | 2000-05-23 | 2001-11-30 | Yokogawa Electric Corp | Agent base production system |
| JP2003044548A (en) * | 2001-07-31 | 2003-02-14 | Amada Co Ltd | Outsourcing method and system for sheet-metal working |
| US7127310B1 (en) * | 2002-08-30 | 2006-10-24 | Advanced Micro Devices, Inc. | Method and apparatus for determining cost functions using parameterized components |
| JP2007334733A (en) * | 2006-06-16 | 2007-12-27 | Yokogawa Electric Corp | Working information management system |
| CN101706886A (en) * | 2009-08-28 | 2010-05-12 | 华南理工大学 | Order-driven Single-piece small-batch combined flow production method for processing workshop |
| CN102542411A (en) * | 2011-12-22 | 2012-07-04 | 浙江工业大学 | Method for carrying out dispatching control on multi-variety multi-process manufacturing enterprise workshop on basis of ACA (Automatic Circuit Analyzer) model |
| CN106155781A (en) * | 2015-04-08 | 2016-11-23 | 中国科学院软件研究所 | A kind of real-time task scheduling method in multiple agent platform |
Non-Patent Citations (3)
| Title |
|---|
| 基于分层调度模型的生产计划和调度集成研究;林慧苹 等;《计算机集成制造系统-CIMS》;20020831;第8卷(第8期);第602～606页 * |
| 基于多Agent和CORBA的加工单元型制造车间调度系统;初红艳 等;《制造技术与机床》;20071031(第10期);第112～116页 * |
| 基于多智能体的制造系统生产控制建模研究;高世一;《计算机集成制造系统》;20070630;第13卷(第6期);第1066～1070页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106527373A (en) | 2017-03-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106527373B (en) | Workshop Autonomous Scheduling system and method based on multiple agent | |
| CN114449023B (en) | Double-front-split-platform type industrial Internet of things and control method thereof | |
| Yao et al. | Optimizing the scheduling of autonomous guided vehicle in a manufacturing process | |
| Bean | Genetic algorithms and random keys for sequencing and optimization | |
| CN107272629A (en) | A kind of intelligent plant system based on technology of Internet of things with industrial big data | |
| Huang et al. | An internet-of-things-based production logistics optimisation method for discrete manufacturing | |
| CN111680893A (en) | Digital twin system of multi-self-addressing robot picking system and scheduling method | |
| CN113177761B (en) | E-commerce storage intelligent scheduling early warning system considering timeliness | |
| Jha | Handbook of flexible manufacturing systems | |
| Doh et al. | Systems integration in the lean manufacturing systems value chain to meet industry 4.0 requirements | |
| Bauer et al. | Characterization of autonomous production by a stage model | |
| KR20220142176A (en) | Smart factory managing system and operating method thereof | |
| CN110516939A (en) | Warehouse homework dispatches system | |
| Tian et al. | Real-time shop floor scheduling method based on virtual queue adaptive control: Algorithm and experimental results | |
| CN111913483A (en) | Dispatching system and method for four-way shuttle | |
| CN112462702A (en) | Intelligent monitoring and intelligent scheduling system based on industrial internet | |
| CN116540584A (en) | Intelligent management and control system of unmanned production line of fusion-cast charging | |
| Majdzik | A feasible schedule for parallel assembly tasks in flexible manufacturing systems | |
| CN114418177B (en) | New product material distribution prediction method based on digital twin workshops for generating countermeasure network | |
| Zarrar et al. | Iot-enabled lean manufacturing: Use of iot as a support tool for lean manufacturing | |
| CN116415780A (en) | Intelligent ordering method and system | |
| CN116224926A (en) | Dynamic scheduling optimization method and device for single-piece small-batch flexible manufacturing workshops | |
| Trentesaux et al. | Introduction to shop-floor control | |
| Marinova-Kostova et al. | Application of Internet of Things in Industry 4.0 | |
| Costa et al. | Improving procedures for production and maintenance control towards industry 4.0 implementation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | 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 |