CN106774193B - Manufacture system dynamic coordinate method based on hormone response diffusion principle - Google Patents
Manufacture system dynamic coordinate method based on hormone response diffusion principle Download PDFInfo
- Publication number
- CN106774193B CN106774193B CN201611069302.XA CN201611069302A CN106774193B CN 106774193 B CN106774193 B CN 106774193B CN 201611069302 A CN201611069302 A CN 201611069302A CN 106774193 B CN106774193 B CN 106774193B
- Authority
- CN
- China
- Prior art keywords
- hormone
- production
- resource
- route
- process route
- 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
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 manufacture system dynamic coordinate method based on hormone response diffusion principle that the invention discloses a kind of, this method is mainly the inspiration by endocrine system hormone response flooding mechanism, realize a kind of manufacture system dynamic coordinate method based on hormone response diffusion principle, devising two kinds can be with interactional hormone, carry out rapid optimizing, wherein in order to expand the solution space of optimizing, the method of genetic cross variation is also used to operate to feasible solution, resource more reasonably to be selected and be distributed according to production task in the case where multi-process routes；It is responded simultaneously for the emergency event in manufacture system, the agility of system can be improved.
Description
Technical field
The manufacture system dynamic coordinate method based on hormone response diffusion principle that the present invention relates to a kind of, belongs to manufacture system
Dynamic coordinate Optimal Decision-making field.
Background technique
As the expansion of field of artificial intelligence research is with deeply, the model of mind of human body information treatment mechanism is increasingly becoming one
A new research hotspot, system structure, the diversity of function and its regulatory mechanism, complexity, reliability, adaptability and efficiently
Property etc. is worth us to use for reference and refer to when studying manufacture system.And endocrine system is even more in human body information processing system
Core, wherein complicated and unique based on hormone response flooding mechanism information processing manner can to give researcher very much
It inspires.This coordination approach based on endocrine system hormone response flooding mechanism is a kind of implicit dynamic coordinate method, root
According to the adjustment effect that hormone concentration in body fluid changes, it can rapidly guide coordination total to current system numerous independent individuals
In the most desirable work of body institute, to realize the comprehensive coordinate and cooperation between system internal resources.Endocrine system passes through
The Reaction-diffusion terms of hormone realize regulating and controlling effect, and the traffic is small, Fast synchronization coordination and cooperation is able to achieve, by stimulating or pressing down
The secretory activity of other endocrine cells is made to keep the stabilization of organismic internal environment, to reach the mesh of body function total optimization
's.Also, this is-not symbol formula communication means based on hormone response flooding mechanism is known as the implicit coordination system, with based on system
It makes common LR, Petri Net in system control sytsem to compare with display coordination systems such as CNP, there is the information traffic
It is small, coordinate simple, the advantages that being easily achieved.It is devised based on this by the inspiration of hormone response flooding mechanism in endocrine system
A kind of task coordinate optimization algorithm based on hormone secretion Principles of Regulation carries out real-time optimization distribution to production task, and can
Fast reaction is carried out for various emergency events, so that device resource is rationally utilized.
Summary of the invention
It is dynamic that in order to solve the above-mentioned technical problems, the present invention provides a kind of manufacture systems based on hormone response diffusion principle
State coordination approach.
In order to achieve the above object, the technical scheme adopted by the invention is that:
Manufacture system dynamic coordinate method based on hormone response diffusion principle, includes the following steps,
Step 1, analyze Discrete manufacturing system production task and resource coordination optimization process, founding mathematical models and its
Constraint condition；
Step 2, it is inspired by endocrine system hormone control mechanism, on the basis of established mathematical model, constructs workshop
The production task hormone information of management level constructs the resource feedback hormone information of process route；
Step 3, production capacity table is established to the production capacity of all resources, and assesses its production capacity, establish hormone letter
Node is ceased, it is stored for the corresponding hormone secretion amount of various production tasks, constitutes the Discrete manufacturing system that can take hormone in
System " body fluid " interior environment；
Step 4, after production task reaches, production breakdown is carried out, several techniques are generated according to workshop real resource situation
Then route is verified by constraint condition, be adjusted to actual conditions, and generates produce on each processing route at random
Piece count；
Step 5, production task hormone is generated, and is released into public environment；
Step 6, Workshop Production resource layer perceives production task hormone, according to the virtual condition of each process route to it
It is responded, when the resource production cost on certain process route is low, and meets the requirement of constraint condition, then increase its hormone point
The amount of secreting；It is on the contrary then reduce its secretory volume；
Step 7, it generates resource and feeds back hormone, and release into public environment；
Step 8, Shop floor control layer senses that resource feeds back hormone, carries out further according to information therein to production task hormone
It updates, adjusts the piece count produced on wherein each processing route, and global optimization is carried out by genetic cross variation method；
Step 9, the new allocation plan after cross and variation is calculated according to objective function, and all solutions is successively carried out
Sequence, screening, elimination of the last one calculate solution space matrix, and generate resource feedback hormone with this；
Step 10, cycling condition is set, circulate operation is carried out according to the method in step 5~9, is solved in Alternative
In the case where route, according to Discrete manufacturing system model target, most reasonably selection and the distribution of task and resource.
The mathematical model of Discrete manufacturing system is,
minCtotal(P)=CP(P)+CT(P)
Constraint condition is,
Wherein, CtotalIt (P) is total cost of production, CPIt (P) is work piece production expense, CTIt (P) is workpiece transport expense, RPFor
The process route sum that can be produced, NrpFor the production quantity of the workpiece P on process route rp, CrpFor on process route rp
The production cost of single workpiece P, CTrpFor the transportation cost of the workpiece P on process route rp, NPThe total amount of workpiece P is produced for generation,
TrpFor the production time of the workpiece P on process route rp, TTrpFor the haulage time of the workpiece P on process route rp, T is completion
Time, DPFor the delivery date of production task.
Production task hormone triple hx(Job_id, Num, Info) building, resource feed back hormone four-tuple hy
(Routh_id, c, t, ρ) building；Wherein, Job_id indicates the number for production task, and Num indicates the quantity of workpiece, Info table
Show the related process information of production task, Routh_id indicates process route number, and c indicates the cost information of the processing route, t
Indicate the process time on the processing route, ρ indicates hormone secretion amount.
Hormone secretion amount more new formula:
ρrp(t+1)=α ρrp(t)+Δρ
Wherein, ρrpFor two amount of hormone secretion on process route rp, α is the retention rate of hormone, and Q is known fixed constant
Amount, t indicate current time, and t+1 indicates next workpiece arrival time.
In process of production, when there is emergency event, specific dynamic coordinate process is Discrete manufacturing system,
S1, suitably processable equipment is chosen from resources bank according to the production technology of emergency event；
S2, the hormone amount remained in each resource hormone information node in environment is taken according to hormone, superposition obtains each
Hormone secretion amount in the existing resource feedback hormone of process route；
Specific formula is,
Wherein, ρiHormone secretion amount is remained on the resource i selected in process route, n indicates process route length；
S3, the production task hormone information in update Discrete manufacturing system and resource feed back hormone information；
S4, the existing hormone dynamic coordinate algorithm of operation, i.e. step 5~9 coordinate the suitable money of matching for emergency event
Source.
Advantageous effects of the invention: the present invention is mainly opened by endocrine system hormone response flooding mechanism
Hair, realizes a kind of manufacture system dynamic coordinate method based on hormone response diffusion principle, devising two kinds can mutual shadow
Loud hormone carrys out rapid optimizing, wherein in order to expand the solution space of optimizing, the method for also using genetic cross variation is come to can
Row solution is operated, can more reasonably to be selected resource in the case where multi-process routes according to production task
And distribution；The present invention responds the emergency event in manufacture system simultaneously, and the agility of system can be improved.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Fig. 2 is multi-process routes digraph.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention
Technical solution, and not intended to limit the protection scope of the present invention.
As shown in Figure 1, the manufacture system dynamic coordinate method based on hormone response diffusion principle, comprising the following steps:
Step 1, analyze Discrete manufacturing system production task and resource coordination optimization process, founding mathematical models and its
Constraint condition.
Discrete Manufacturing Systems production task can be described as with resource coordination optimization problem: quantity NPFor workpieces processing
P, after production task therein is carried out production breakdown, the production task of an available multi-process routes as shown in Figure 2
Optional resource process digraph corresponding with resource, 3 executive process routes, Sp1~Sp9Indicate the processing for being directed to workpiece p
In available resource.
Discrete manufacturing system and mathematical model and its constraint condition are established with the minimum target of production cost are as follows:
The mathematical model of Discrete manufacturing system is,
minCtotal(P)=CP(P)+CT(P)
Constraint condition are as follows: to guarantee that all production tasks can be with corresponding resource coordination, and it can be when defined
Interior completion, specifically,
Wherein, CtotalIt (P) is total cost of production, CPIt (P) is work piece production expense, CTIt (P) is workpiece transport expense, RPFor
The process route sum that can be produced, NrpFor the production quantity of the workpiece P on process route rp, CrpFor on process route rp
The production cost of single workpiece P, CTrpFor the transportation cost of the workpiece P on process route rp, NPThe total amount of workpiece P is produced for generation,
TrpFor the production time of the workpiece P on process route rp, TTrpFor the haulage time of the workpiece P on process route rp, T is completion
Time, DPFor the delivery date of production task.
Step 2, it is inspired by endocrine system hormone control mechanism, on the basis of established mathematical model, constructs workshop
The production task hormone information of management level constructs the resource feedback hormone information of process route.
Production task hormone triple hx(Job_id, Num, Info) building, resource feed back hormone four-tuple hy
(Routh_id, c, t, ρ) building；Wherein, Job_id indicates the number for production task, and Num indicates the quantity of workpiece, Info table
Show the related process information of production task, Routh_id indicates process route number, and c indicates the cost information of the processing route, t
Indicate the process time on the processing route, ρ indicates hormone secretion amount.
Step 3, production capacity table is established to the production capacity of all resources, and assesses its production capacity, establish hormone letter
Node is ceased, it is stored for the corresponding hormone secretion amount of various production tasks, constitutes the Discrete manufacturing system that can take hormone in
System " body fluid " interior environment.
Step 4, after production task reaches, production breakdown is carried out, several techniques are generated according to workshop real resource situation
Then route is verified by constraint condition, be adjusted to actual conditions, and generates produce on each processing route at random
Piece count.
Step 5, production task hormone is generated, and is released into public environment.
Step 6, Workshop Production resource layer perceives production task hormone, according to the virtual condition of each process route to it
It is responded, when the resource production cost on certain process route is low, and meets the requirement of constraint condition, then increase its hormone point
The amount of secreting；It is on the contrary then reduce its secretory volume.
Judge whether the resource production cost on process route low, by by process route resource production cost with set
Fixed threshold value is compared, low lower than the then judgement production cost of threshold value.
Hormone secretion amount more new formula:
ρrp(t+1)=α ρrp(t)+Δρ
Wherein, ρrpFor two amount of hormone secretion on process route rp, α is the retention rate of hormone, and Q is known fixed constant
Amount, t indicate current time, and t+1 indicates next workpiece arrival time.
Step 7, it generates resource and feeds back hormone, and release into public environment.
Step 8, Shop floor control layer senses that resource feeds back hormone, carries out further according to information therein to production task hormone
It updates, adjusts the piece count produced on wherein each processing route, and global optimization is carried out by genetic cross variation method.
In order to expand the Searching Resolution Space range of feasible solution, herein according to the digraph of multi-process routes resource, m kind is chosen
Feasible allocation plan is solution space Xm, matrix is expressed as:
Every a line x in matrixiThe dynamic candidate group of solution space is constituted, is carried out by the means of hereditary variation global excellent
Change.In the selection process, candidate solution each in feasible solution is calculated according to the mathematical model of Discrete manufacturing system, is pressed
The probability h that formula obtainsriTwo feasible solutions chosen in solution space carry out cross and variation operation；
Wherein, CTotal(P, i) indicates the production task totle drilling cost of i-th of solution in solution space.Each it can be seen from formula
In a feasible solution, production cost is smaller, then its probability for being selected progress cross and variation is smaller, because the feasible solution is more
Adjunction is bordering on optimal solution, is suitble to retain.
In crossover operation, if xiAnd xjFor two feasible solutions for carrying out crossover operation, xiAnd xjFor two rows in matrix,
Practical crossover probability is pc=PC×hri, PCFor crossover probability as defined in system.Feasible solution relatively high for cost in this way is come
It says, the probability intersected is with regard to bigger.P ∈ [0,1] is randomly generated, if p ﹥ pc, then crossover operation is carried out.Similarly, becoming
Different stage, practical mutation probability are pm=PM×hri, PMFor mutation probability as defined in system.It is same using such mutation probability
Sample can allow the variable of more excellent solution is more to be saved.P ∈ [0,1] is randomly generated, if p ﹥ pm, then variation behaviour is carried out
Make.
Step 9, the new allocation plan after cross and variation is calculated according to objective function, and all solutions is successively carried out
Sequence, screening, elimination of the last one calculate solution space matrix, and generate resource feedback hormone with this；
Step 10, cycling condition is set, circulate operation is carried out according to the method in step 5~9, is solved in Alternative
In the case where route, according to Discrete manufacturing system model target, most reasonably selection and the distribution of task and resource.
In dynamicalmanufacturing environment up in air, emergency event occurs often meaning that each in Discrete manufacturing system
Kind resource may have production plan and use, then the arrangement to emergency event just has to each resource in consideration system
Then actual working state reasonably carries out resource selection and coordinated allocation according to the arrangement of process route in emergency event.
Assuming that in emergency event only comprising a type of product I need produce (multiple product combination production task can
And so on), production technology feature are as follows: I1 → I2 →... ... → In (indicates the works such as vehicle, milling, the mill during manufacture product I
Sequence, n indicate its required process quantity, i.e. process route length).
Discrete manufacturing system in process of production, when there is emergency event, specific dynamic coordinate process are as follows:
S1, suitably processable equipment is chosen from resources bank according to the production technology of emergency event；
S2, the hormone amount remained in each resource hormone information node in environment is taken according to hormone, superposition obtains each
Hormone secretion amount in the existing resource feedback hormone of process route；
Specific formula is,
Wherein, ρiHormone secretion amount is remained on the resource i selected in process route, n indicates process route length；
S3, the production task hormone information in update Discrete manufacturing system and resource feed back hormone information；
S4, the existing hormone dynamic coordinate algorithm of operation, i.e. step 5~9 coordinate the suitable money of matching for emergency event
Source.
The above method is mainly the inspiration by endocrine system hormone response flooding mechanism, is realized a kind of anti-based on hormone
The manufacture system dynamic coordinate method for answering diffusion principle, rapid optimizing can be carried out with interactional hormone by devising two kinds, wherein
In order to expand the solution space of optimizing, the method for genetic cross variation is also used to operate to feasible solution, so as in multiplexing
In the case where skill route, resource more reasonably can be selected and be distributed according to production task.Simultaneously the above method for
Emergency event in manufacture system is responded, and the agility of system can be improved.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformations can also be made, these improvement and deformations
Also it should be regarded as protection scope of the present invention.
Claims (4)
1. the manufacture system dynamic coordinate method based on hormone response diffusion principle, it is characterised in that: include the following steps,
Step 1, the production task and resource coordination optimization process, founding mathematical models and its constraint of Discrete manufacturing system are analyzed
Condition；
Step 2, it is inspired by endocrine system hormone control mechanism, on the basis of established mathematical model, constructs workshop management
The production task hormone information of layer constructs the resource feedback hormone information of process route；
Step 3, production capacity table is established to the production capacity of all resources, and assesses its production capacity, establish hormone information section
Point stores it for the corresponding hormone secretion amount of various production tasks, constitutes the Discrete manufacturing system " body that can take hormone in
The interior environment of liquid "；
Step 4, after production task reaches, production breakdown is carried out, several process routes are generated according to workshop real resource situation,
Then it is verified by constraint condition, actual conditions is adjusted, and generate the work produced on each processing route at random
Number of packages amount；
Step 5, production task hormone is generated, and is released into public environment；
Step 6, Workshop Production resource layer perceives production task hormone, is carried out according to the virtual condition of each process route to it
Response when the resource production cost on certain process route is low, and meets the requirement of constraint condition, then increases its hormone secretion
Amount；It is on the contrary then reduce its secretory volume；
Step 7, it generates resource and feeds back hormone, and release into public environment；
Step 8, Shop floor control layer senses that resource feeds back hormone, carries out more further according to information therein to production task hormone
Newly, the piece count produced on wherein each processing route is adjusted, and global optimization is carried out by genetic cross variation method；
Global optimization procedure is,
According to the digraph of multi-process routes resource, the selection feasible allocation plan of m kind is solution space Xm, matrix is expressed as:
Every a line x in matrixiThe dynamic candidate group of solution space is constituted, carries out global optimization by the means of hereditary variation,
Middle RPFor the process route sum that can be produced；
In the selection process, candidate solution each in feasible solution is calculated according to the mathematical model of Discrete manufacturing system, is pressed
The probability h that following formula obtainsriTwo feasible solutions chosen in solution space carry out cross and variation operation；
Wherein, CTotal(P, i) indicates the production task totle drilling cost of i-th of solution in solution space；
In crossover operation, if xiAnd xjFor two feasible solutions for carrying out crossover operation, xiAnd xjIt is practical to hand over for two rows in matrix
Fork probability is pc=PC×hri, PCFor crossover probability as defined in system；
P ∈ [0,1] is randomly generated, if p ﹥ pc, then crossover operation is carried out；
In the variation stage, practical mutation probability is pm=PM×hri, PMFor mutation probability as defined in system；
P ∈ [0,1] is randomly generated, if p ﹥ pm, then mutation operation is carried out；
Step 9, the new allocation plan after cross and variation is calculated according to objective function, and all solutions is successively arranged
Sequence, screening, elimination of the last one calculate solution space matrix, and generate resource feedback hormone with this；
Step 10, cycling condition is set, circulate operation is carried out according to the method in step 5~9, is solved in multi-process routes
In the case where, according to Discrete manufacturing system model target, most reasonably selection and the distribution of task and resource；
In process of production, when there is emergency event, specific dynamic coordinate process is Discrete manufacturing system,
S1, suitably processable equipment is chosen from resources bank according to the production technology of emergency event；
S2, the hormone amount remained in each resource hormone information node in environment is taken according to hormone, superposition obtains each work
Hormone secretion amount in the existing resource feedback hormone of skill route；
Specific formula is,
Wherein, ρiHormone secretion amount is remained on the resource i selected in process route, n indicates process route length；ρrpFor
Hormone secretion amount on process route rp；
S3, the production task hormone information in update Discrete manufacturing system and resource feed back hormone information；
S4, the existing hormone dynamic coordinate algorithm of operation, i.e. step 5~9, coordinate to match suitable resource for emergency event.
2. the manufacture system dynamic coordinate method according to claim 1 based on hormone response diffusion principle, feature exist
It is in: the mathematical model of Discrete manufacturing system,
minCtotal(P)=CP(P)+CT(P)
Constraint condition is,
Wherein, CtotalIt (P) is total cost of production, CPIt (P) is work piece production expense, CTIt (P) is workpiece transport expense, RPFor can be with
The process route sum of production, NrpFor the production quantity of the workpiece P on process route rp, CrpIt is single on process route rp
The production cost of workpiece P, CTrpFor the transportation cost of the workpiece P on process route rp, NPFor the total amount of generation production workpiece P, TrpFor
The production time of workpiece P, T on process route rpTrpFor the haulage time of the workpiece P on process route rp, T is completion date,
DPFor the delivery date of production task.
3. the manufacture system dynamic coordinate method according to claim 2 based on hormone response diffusion principle, feature exist
In: production task hormone triple hx(Job_id, Num, Info) building, resource feed back hormone four-tuple hy(Routh_
Id, c, t, ρ) building；Wherein, Job_id indicates the number for production task, and Num indicates the quantity of workpiece, and Info indicates production
The related process information of task, Routh_id indicate process route number, and c indicates the cost information of the processing route, and t is indicated should
Process time on processing route, ρ indicate hormone secretion amount.
4. the manufacture system dynamic coordinate method according to claim 3 based on hormone response diffusion principle, feature exist
In: hormone secretion amount more new formula:
ρrp(t+1)=α ρrp(t)+Δρ
Wherein, ρrpFor the hormone secretion amount on process route rp, α is the retention rate of hormone, and Q is known fixed constant amount, and t is indicated
Current time, t+1 indicate next workpiece arrival time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611069302.XA CN106774193B (en) | 2016-11-29 | 2016-11-29 | Manufacture system dynamic coordinate method based on hormone response diffusion principle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611069302.XA CN106774193B (en) | 2016-11-29 | 2016-11-29 | Manufacture system dynamic coordinate method based on hormone response diffusion principle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106774193A CN106774193A (en) | 2017-05-31 |
| CN106774193B true CN106774193B (en) | 2019-04-16 |
Family
ID=58904250
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611069302.XA Active CN106774193B (en) | 2016-11-29 | 2016-11-29 | Manufacture system dynamic coordinate method based on hormone response diffusion principle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106774193B (en) |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE3602457A1 (en) * | 1985-09-17 | 1987-03-26 | Mauser Werke Oberndorf | Computer-controlled production system |
| CN101782771B (en) * | 2010-03-17 | 2011-12-14 | 东华大学 | Spinning process intelligent optimized design method based on immune neural network |
| US9141730B2 (en) * | 2011-09-12 | 2015-09-22 | Applied Materials Israel, Ltd. | Method of generating a recipe for a manufacturing tool and system thereof |
| CN104077630B (en) * | 2014-05-26 | 2017-04-12 | 浙江工业大学 | Workshop layout method for complex job of simulating human cell evolution |
| CN103996080B (en) * | 2014-05-30 | 2018-01-09 | 北京理工大学 | A kind of manufacture system configuration optimization method with maximum connectivity |
-
2016
- 2016-11-29 CN CN201611069302.XA patent/CN106774193B/en active Active
Non-Patent Citations (1)
| Title |
|---|
| 基于内分泌调节原理的制造任务与资源动态协调机制研究;顾文斌 等;《中国机械工程》;20150630;第26卷(第11期);第1471-1477页 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106774193A (en) | 2017-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104808627A (en) | Workshop layout method of discrete manufacturing system | |
| CN106600147B (en) | Towards the multiple no-manned plane method for allocating tasks and device of decomposable asymmetric choice net task | |
| CN111461464B (en) | Plant protection unmanned aerial vehicle cluster operation task allocation method and device | |
| CN103326353B (en) | Environmental economic power generation dispatching calculation method based on improved multi-objective particle swarm optimization algorithm | |
| CN104408589A (en) | AGV optimization scheduling method based on mixed particle swarm optimization | |
| CN107093046A (en) | Unmanned dispensing vehicle method for allocating tasks, system and unmanned dispensing vehicle | |
| CN106610654A (en) | Improved genetic algorithm for flexible workshop scheduling | |
| CN105225003A (en) | A kind of cuckoo searching algorithm solves the method for UAV multitask investigation decision problem | |
| CN107967586A (en) | A kind of power grid goods and materials storage optimization method | |
| CN106250583A (en) | Dynamic job shop scheduling rule optimization methods based on double population gene expression programmings | |
| CN103955409B (en) | Method for operation dispatching | |
| CN106611378A (en) | Priority encoding-based hybrid genetic algorithm for solving job-shop scheduling problem | |
| CN102968057A (en) | Multi-species multi-process multi-unit manufacturing scheduling method based on improved cellular machine | |
| CN111160732A (en) | Method suitable for comprehensive evaluation of multi-station fusion safety and benefit | |
| CN101256648A (en) | Genetic operation operator based on indent structure for producing quening system | |
| CN106774193B (en) | Manufacture system dynamic coordinate method based on hormone response diffusion principle | |
| CN101808325B (en) | Method and device for allocating frequency spectrum | |
| CN110837950B (en) | Dynamic scheduling method of RGV (traffic volume group) trolley | |
| CN105512726B (en) | Reliability allocation methods and device based on immune genetic optimization | |
| Jiang et al. | An adaptive immune‐following algorithm for intelligent optimal schedule of multiregional agricultural machinery | |
| CN105976052A (en) | Improved quantum-behaved particle swarm optimization algorithm-based multi-region economic dispatch method | |
| CN115841230A (en) | Emergency resource overall planning and allocating method based on improved NSGA-II algorithm | |
| Zhao et al. | Emergency materials scheduling optimization based on modified GA | |
| Perumalsamy et al. | Predictive analytics using Genetic Algorithm for efficient supply chain inventory optimization | |
| CN102833864B (en) | Wireless resource allocation methods in a kind of multiple cell mobile communication system |
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 |