CN107730029A - Manufacturing process optimization method and apparatus based on quantum-behaved particle swarm optimization - Google Patents

Manufacturing process optimization method and apparatus based on quantum-behaved particle swarm optimization Download PDF

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CN107730029A
CN107730029A CN201710867441.5A CN201710867441A CN107730029A CN 107730029 A CN107730029 A CN 107730029A CN 201710867441 A CN201710867441 A CN 201710867441A CN 107730029 A CN107730029 A CN 107730029A
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CN107730029B (en
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姜雪松
王润泽
逄焕君
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Qilu University of Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06316—Sequencing of tasks or work
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Abstract

The invention discloses a kind of manufacturing process optimization method based on quantum-behaved particle swarm optimization, comprise the following steps：Using air pressure in stove on oxygen consumption, the consumption of heavy oil, the consuming cost of machine and production line as optimization aim, using time and resource constraint as constraints, multiple target manufacturing process Optimized model is built；The multiple target manufacturing process Optimized model is solved using quantum-behaved particle swarm optimization.The present invention provides a kind of quantitative mode to produce the optimization of manufacturing process, and experiential adjustment mode is more reasonable than before, and accuracy is high, helps to optimize allocation of resources.

Description

Manufacturing process optimization method and apparatus based on quantum-behaved particle swarm optimization
Technical field
The invention belongs to manufacture Optimized Operation field, more particularly to a kind of life based on quantum-behaved particle swarm optimization Produce manufacturing process optimization method and device.
Background technology
Manufacturing industry directly represent a national productivity level, be distinguish developing country and developed country important Factor, manufacturing industry occupy important share in the national economy of World Developed Countries.Meanwhile manufacturing industry is the force at the core in China With support industry, the development of manufacturing in China is rapid, but it is low to still suffer from productivity ratio, the deficiencies of serious is wasted, as economic society The important base that can develop, manufacturing industry are main channel and the concentrated reflection of international competitiveness of China cities and towns employment.《China Manufacture 2025》Under proposition, manufacturing industry needs transition badly, and production scheduling is most exactly urgently to accomplish to save on the premise of interests at present Emission reduction, innovation of depending on science and technology, reduces the discharge of pollutant.Usually need to consider multiple targets for the scheduling problem in many fields Optimization, if Business Economic Benefit, ecological benefits, social benefit are to obtain to the best embodiment of enterprise, this just needs Multi-objective planning method is used to solve problem.At present, the present production technology in China not yet reaches production greenization, still exists Substantial amounts of production problem.During actual Workshop Production, the problem of being primarily present, is as follows：(1) complex production process, workshop life Production is flexible poor；(2) creation data of record does not optimize processing, and raw material energy is adjusted according to conventional experience The input quantity in source etc., do not reach the optimization requirement of production, cause the unreasonable configuration and waste of resource.
Therefore, the optimization of actual production manufacturing process how is carried out, economic and ecology maximizing the benefits is realized, is ability The technical problem that field technique personnel urgently solve at present.
The content of the invention
To overcome above-mentioned the deficiencies in the prior art, the invention provides a kind of production based on quantum-behaved particle swarm optimization Manufacturing process optimization method and device.Based on actual production manufaturing data, multiple object functions and constraints, structure system are determined Process Model for Multi-Objective Optimization is made, then carries out model solution using based on quantum-behaved particle swarm optimization.
To achieve the above object, the present invention adopts the following technical scheme that：
A kind of manufacturing process optimization method based on quantum-behaved particle swarm optimization, comprises the following steps：
Step 1：Using air pressure in stove on oxygen consumption, the consumption of heavy oil, the consuming cost of machine and production line to be excellent Change target, using time and resource constraint as constraints, build multiple target manufacturing process Optimized model；
Step 2：The multiple target manufacturing process Optimized model is solved using quantum-behaved particle swarm optimization.
Further, the multiple target manufacturing process Optimized model is：
Miny=F (x)={ f1(x),f2(x),f3(x),f4(x)}
The f1(x),f2(x),f3(x),f4(x) consumption of oxygen consumption, the consumption of heavy oil, machine is represented respectively Four object functions of air pressure in stove in cost and production line.
Further, wherein, oxygen consumption：f1(x)=minXijkWijk；
Heavy oil consumption amount：f2(x)=minXijkHijk；
Machine consumes power：f3(x)=XijkPijk；
Air pressure in reacting furnace：f4(x)=minXijkQijk；
XijkRepresent that workpiece i jth procedure performs on machine k；WijkRepresent workpiece i jth procedure in machine The amount of oxygen consumed on device k；HijkRepresent the heavy oil that workpiece i jth procedure consumes on machine k；PijkRepresent the of workpiece i The energy of the j procedures in machine k consumption；QijkRepresent air pressure of the workpiece i jth procedure when being produced on machine k.
Further, wherein,
The time-constrain is：The beginning process time of the adjacent inter process of same workpiece has successively；
The resource constraint is：Current task, any machine must be completed before starting next task on same board Two workpiece of identical or different processes can not be processed simultaneously.
Further, the time-constrain is expressed as：
In formula, process process time tijkRepresent workpiece i jth procedure required for being processed on kth platform machine when Between, SijkRepresent that workpiece i jth procedure starts the time of processing on kth board.Whole formula represents workpiece i jth -1 Procedure must complete before jth procedure.
Further, the resource constraint is expressed as：
xijk=xmnk=1 and Rijmnq=1
Xijk=1 represents process VijPerformed on machine k, RijmnqRepresent workpiece i jth procedure and workpiece m on machine q The processing sequencing of n-th procedure, Rijmnq=1 represents process j prior to process n.
Further, the quantum-behaved particle swarm optimization concretely comprises the following steps：
(1) initialization algorithm parameter：Particle populations X, dimension size R, particle i position, maximum iteration MAXITER, optimal solution set L；
Four object function regions in the region that particle i can be reached are arranged to：Oxygen consumption O (i), heavy oil consumption G (i) four target areas, kiln furnace pressure S (i), are defined as four matrixes, so as to next iteration by, machine consumption power M (i) The renewal of particle；
(2) according to object function, the adaptive value of each particle is calculated；
(3) for each particle, searching route is selected：Particle i (i=1,2 ..., R) is according to particle evolution equation in matrix The renewal the to be reached point of selection next step in O (i), G (i), M (i) and S (i)；The particle evolution equation is：
Wherein, α is compression-broadening factor, and t is current iteration number, and u is generally evenly distributed in the random number between 0 and 1； LijFor the characteristic length of δ potential wells；XijAnd X (t)ij(t+1) position before and after particle evolution is represented respectively, and M represents to dive in population In the colony of solution；
(4) to each particle, the position X of the particle is calculatedi(t) particle, is solved according to individual desired positions solution formula Individual desired positions Si(t), with preceding once particle individual desired positions Si(t-1) adaptive value is compared, if more It is good, then by Si(t) as new locally optimal solution；The individual desired positions solution formula is：
Wherein, f () represents the current position of particle；
(5) to each particle, its adaptive value is made comparisons with the desired positions Sbest that it passes through, if more preferably, ought Front position is as current globally optimal solution；
(6) (2)-(5) are repeated；
(7) end condition：Iterations reaches the maximum iteration of setting or completes the calculating in object function region.
According to the third object of the present invention, present invention also offers a kind of production system based on quantum-behaved particle swarm optimization Make process optimization device, including memory, processor and storage are on a memory and the computer journey that can run on a processor Sequence, the described manufacturing process optimization based on quantum-behaved particle swarm optimization is realized during the computing device described program Method.
According to the third object of the present invention, present invention also offers a kind of computer-readable recording medium, it is stored thereon with Computer program, the described manufacturing process based on quantum-behaved particle swarm optimization is performed when the program is executed by processor Optimization method.
Beneficial effects of the present invention
1st, the present invention using on oxygen consumption, the consumption of heavy oil, the consuming cost of machine and production line in stove air pressure as Optimization aim, using time and resource constraint as constraints, Optimized model is established, is provided to produce the optimization of manufacturing process A kind of quantitative mode, experiential adjustment mode is more reasonable than before, and accuracy is high；
2nd, the present invention carries out model solution using QPSO, and by experimental verification, its efficiency is substantially better than genetic algorithm and grain Swarm optimization, solves the defects of PSO algorithms are easily trapped into locally optimal solution.
The present invention just in production energy control design, be not related to glass production when raw material (for Silica, aluminum oxide, calcium oxide etc.), comparatively, implement simply, to meet reality.
Brief description of the drawings
The Figure of description for forming the part of the application is used for providing further understanding of the present application, and the application's shows Meaning property embodiment and its illustrate be used for explain the application, do not form the improper restriction to the application.
Fig. 1 is the manufacturing process optimization method flow diagram of the invention based on quantum-behaved particle swarm optimization；
Fig. 2 is the flow chart of quantum-behaved particle swarm optimization of the present invention.
Embodiment
It is noted that described further below is all exemplary, it is intended to provides further instruction to the application.It is unless another Indicate, all technologies used herein and scientific terminology are with usual with the application person of an ordinary skill in the technical field The identical meanings of understanding.
It should be noted that term used herein above is merely to describe embodiment, and be not intended to restricted root According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singulative It is also intended to include plural form, additionally, it should be understood that, when in this manual using term "comprising" and/or " bag Include " when, it indicates existing characteristics, step, operation, device, component and/or combinations thereof.
In the case where not conflicting, the feature in embodiment and embodiment in the application can be mutually combined.
Embodiment one
Present embodiment discloses a kind of manufacturing process optimization method based on quantum-behaved particle swarm optimization, including with Lower step：
Step 1：Using air pressure in stove on oxygen consumption, the consumption of heavy oil, the consuming cost of machine and production line to be excellent Change target, using time and resource constraint as constraints, build multiple target manufacturing process Optimized model；
Operation flow is exactly to reach target, and completed jointly by multiple main bodys one group interrelated, interdependent, in proper order gradually The active process entered, Work Flow Optimizing be exactly to existing flow carry out constantly modification and it is perfect, according to Work Flow Optimizing Analysis of its Critical Successful Factors, it is main to include four aspects, be time, cost, quality and flexibility respectively.When time includes performing Between and movable stand-by period, cost mainly include information costs, cost of labor and resources costs, quality mainly including qualification rate, Compliance rate, reliability, service integrity etc. are serviced, it is flexible that flexibility includes flexible time, Quantity Flexible and market.
Object function：
With the development of actual production, the optimization of single target is difficult to meet actual production needs, it usually needs simultaneously Consider multiple targets, i.e., improve the performance of any one target as far as possible in the case where not damaging other target capabilities, herein Manufacturing resources optimization regulation goal towards flexible job shop is to make the manufacturing process of whole task optimal, is being analyzed herein Four object functions are provided with the basis of operation flow Critical Success Factors：
1st, oxygen consumption：f1(x)；
2nd, the consumption of heavy oil：f2(x)；
3rd, the consuming cost of machine：f3(x)；
4th, air pressure in stove on production line：f4(x)。
Specifically, above there is provided four object functions in the fabrication process to be designed to function.
Oxygen consumption：f1(x)=minXijkWijk
Heavy oil consumption amount：f2(x)=minXijkHijk
Machine consumes power：f3(x)=XijkPijk
Air pressure in reacting furnace：f4(x)=minXijkQijk
XijkRepresent that workpiece i jth procedure performs on machine k；WijkRepresent workpiece i jth procedure in machine The amount of oxygen consumed on device k；HijkRepresent the heavy oil that workpiece i jth procedure consumes on machine k；PijkRepresent the of workpiece i The energy of the j procedures in machine k consumption；QijkRepresent air pressure of the workpiece i jth procedure when being produced on machine k；
Multi-objective optimization question is also known as multi-objective optimization question.Do not lose it is general in the case of, have d decision-makings become The multi-objective optimization question of amount and R target variables can be expressed as：
Miny=F (x)={ f1(x),f2(x), x3(x),...fn(x)}
In objective function Equation, (x1, x2 ... are x) R dimension decision vectors to x=, and X is the decision space of R dimensions.Object function f(x)Define four mapping functions from decision space to object space.
Constraints：
Divided according to constraint, the common constraints of Job-Shop there are the resources of production (energy, raw material, equipment etc.), caches Capacity, due date, product process flow, batch size, cost limitation etc..Constraints involved by this paper is main sometimes Between constraint and resource constraint.
Time-constrain：The beginning process time of the adjacent inter process of same workpiece of technological requirement will have successively
In formula, process process time tijkRepresent workpiece i jth procedure required for being processed on kth platform machine when Between, SijkRepresent that workpiece i jth procedure starts the time of processing on kth board.Whole formula represents workpiece i jth -1 Procedure must complete before jth procedure.
Resource constraint：Current task must be completed before starting next task on same board, any machine can not be same Shi Jiagong is identical or two workpiece of different processes.
Wherein xijk=xmnk=1 and Rijmnq=1.
Xijk=1 represents process VijPerformed on machine k, RijmnqRepresent workpiece i jth procedure and workpiece m on machine q The processing of n-th procedure is successively along Rijmnq=1 represents process j prior to process n.
Step 2：The multiple target manufacturing process Optimized model is solved using quantum-behaved particle swarm optimization.
Quantum-behaved particle swarm optimization：
In view of the following shortcoming of particle cluster algorithm in itself.(1) optimal solution searched out be probably locally optimal solution without It is globally optimal solution.(2) algorithm search fast convergence rate at initial stage and to search for late convergence slack-off.(3) parameter selection with Machine.SUN et al. proposed a kind of new PSO algorithm models in 2004 from quantum-mechanical angle, this model with Based on DELTA potential wells, it is believed that particle has quantum behavior, and proposes the population based on quantum behavior according to this model Optimized algorithm.In vector subspace, particle can be scanned in whole solution space, thus the overall situation of QPSO algorithms is searched PSO algorithm can be far superior to without hesitation.QPSO algorithms describe the state of particle by wave function, and stunned by solving Xue Ding Equation obtains the probability density function that particle occurs in space certain point, then obtains particle by MonetCarfo stochastic simulations Position equation.
QPSO algorithms are made up of the individual populations for representing potential problems solution of R in the search space of a D dimension target, this Colony is expressed as X=(x1,x2,...xi)TI=1,2 ... R, (3.8)
In the position of i-th of particle of t:
Xi(t)={ Xi,1(t),Xi,2(t),...,Xi,D}, (t) i=1,2 ... 3, R (3.9)
Particle does not have velocity vector, the preferably individual positional representation P of particle in quantum-behaved particle swarm optimizationi(t)=[Pi,1 (t),Pi,2(t),...,Pi,D(t)] (3.10)
For optimization problem, target function value is smaller, and corresponding adaptive value is better.Particle i individual desired positions Sbest is determined by below equation：
Group position is expressed as：
L (t)=[L1(t),L2(t),...,LD(t)] (3.11)
When g is that position is optimal, L (t)=Sg(t),g∈{1,2,...,R}。
In actual algorithm operation, operation each time will carry out once more global desired positions, if ith is run After Si(t) value is better than Si(t-1) then by Li(t) update.
Order
The evolution equation of particle is：
Wherein u is generally evenly distributed in the random number between 0 and 1.In QPSO algorithms, the position of the state description of particle to Amount, and only has a dominant vector α in algorithm, and this is the convergent-divergent coefficient in algorithm, is unique Optimization about control parameter, valency Value is usually below equation
α=0.5+ (1-0.5) * (MAXITER-t)/MAXITER, MAXITER is maximum iteration, and t is current iteration Number.
The solution procedure of the quantum behavior population is as follows：
(1) initialization algorithm parameter：Particle populations X, dimension size R, particle i position, maximum iteration MAXITER, optimal solution set L (including locally optimal solution and globally optimal solution).Four target letters in the region that particle i can be reached Number region is arranged to：Oxygen consumption O (i), heavy oil consumption G (i), machine consumption power M (i), kiln furnace pressure S (i), four Target area is defined as four matrixes, so as to the renewal of next iteration particle；
(2) according to object function, the adaptive value of each particle is calculated；
(3) for each particle, searching route is selected：Particle i (i=1,2 ..., R) is according to particle evolution equation in matrix The renewal the to be reached point of selection next step in O (i), G (i), M (i) and S (i)；The particle evolution equation is：
Wherein, α is compression-broadening factor, and t is current iteration number, and u is generally evenly distributed in the random number between 0 and 1； LijFor the characteristic length of δ potential wells；XijAnd X (t)ij(t+1) position before and after particle evolution is represented respectively, and M represents to dive in population In the colony of solution；
(4) to each particle, the position X of the particle is calculatedi(t) particle, is solved according to individual desired positions solution formula Individual desired positions Si(t), with preceding once particle individual desired positions Si(t-1) adaptive value is compared, if more It is good, then by Si(t) as new locally optimal solution；The individual desired positions solution formula is：
Wherein, f () represents the current position of particle；
(5) to each particle, its adaptive value is made comparisons with the desired positions Sbest that colony particle passes through, if more preferably, Then using current location as current globally optimal solution；
(6) (2)-(5) are repeated；
(7) end condition：Iterations reaches the maximum iteration of setting or reaches four object functions of setting Total quantity.
Embodiment two
The purpose of the present embodiment is to provide a kind of computing device.
A kind of manufacturing process optimization device based on quantum-behaved particle swarm optimization, including memory, processor and Storage is realized below on a memory and the computer program that can run on a processor, during the computing device described program Step, including：
Step 1：Using air pressure in stove on oxygen consumption, the consumption of heavy oil, the consuming cost of machine and production line to be excellent Change target, using time and resource constraint as constraints, build multiple target manufacturing process Optimized model；
Step 2：The multiple target manufacturing process Optimized model is solved using quantum-behaved particle swarm optimization.
Embodiment three
The purpose of the present embodiment is to provide a kind of computer-readable recording medium.
A kind of computer-readable recording medium, is stored thereon with computer program, should for manufacturing the optimization of process Following steps are performed when program is executed by processor：
Step 1：Using air pressure in stove on oxygen consumption, the consumption of heavy oil, the consuming cost of machine and production line to be excellent Change target, using time and resource constraint as constraints, build multiple target manufacturing process Optimized model；
Step 2：The multiple target manufacturing process Optimized model is solved using quantum-behaved particle swarm optimization.
Each step being related in above example two and three is corresponding with embodiment of the method one, and embodiment can be found in The related description part of embodiment one.Term " computer-readable recording medium " is construed as including one or more instruction set Single medium or multiple media；Any medium is should also be understood as including, any medium can be stored, encodes or held Carry for the instruction set by computing device and make the either method in the computing device present invention.
Experimental result
The present invention is directed to actual production data (the actual production data of certain glass company), utilizes the grain in swarm intelligence algorithm Swarm optimization and quantum-behaved particle swarm optimization are optimized to data, and the data after processing and actual production data are carried out pair Than, it will be apparent that it can be seen that the data edge after optimization.
The actual production data for choosing certain glass fibre company carry out experimental analysis, and experimental data is modified, and use MATLAB carries out emulation experiment, so as to draw the actual conditions of result of the present invention.To the effect that air pressure, the energy of experimental data Source, machine consumption and waste gas discharge, and from energy angle Selection water consumption and the summation of coal consumption.This test have chosen four target letters 1000 several logs, as actual production data, experimental data of every 10 seconds records.First, tested in matlab Four object functions of middle reading, suffix name are a .dat files, and generator matrix identifies for matlab.
Function Data=De ()
% reads in data
Filename='Energy.dat'；
Filename='Machine.dat'；
Filename='kw.dat'；
Filename='Waste.dat'；
NRow=1000；
NColumn=1；
Fid=fopen (filename, ' r')；
Temp=fscanf (fid, ' %f')；
fclose(fid)；
Data=reshape (temp, [nRow nColumn]) '；
This paper actual production data carry out computing in PSO algorithms and QPSO algorithms, while set maximum iteration MAXITER=1000, population popsize=50, dimension dimension=50, number of run runmax=30, chosen position X range of variables 0-10000.In PSO algorithms, if c1=c2=2, r1,i,j(t)=0.9, r2,i,j(t)=0.4, Vmax= 6, due to no speed variables in QPSO algorithms, so not considering velocity variations herein；This time experiment output txt documents, text Shelves include average value average value, best values best value for each iteration of each iteration, variance Variance and globally optimal solution Global optimal solution, pass through actual production data, the data of pso processing The data comparison treated with QPSO：In actual production data, the actual consumption of ten tons of certain species glasses of actual production is：S (x)={ 2216.698204,3757.072245,29105.1684,1.0409 }, the result after processing are：F (x)= {2.3157365e+03,3.3084281e+03,2.7113943e+04,1.1022318}.It may be concluded that the consumption of oxygen Amount increase.Using all-oxygen combustion, oxygen-enriched combusting, while increase the air pressure in stove, utilize glass fibre tank furnace auxiliary electrical heater heat The characteristics of efficiency high, few environmental pollution, accordingly increases the consumption of electric energy, can significantly reduce the burning to heavy oil and use, Reduce the discharge of pernicious gas.
It will be understood by those skilled in the art that each module or each step of the invention described above can be filled with general computer Put to realize, alternatively, they can be realized with the program code that computing device can perform, it is thus possible to which they are stored Performed in the storage device by computing device, either they are fabricated to respectively each integrated circuit modules or by they In multiple modules or step be fabricated to single integrated circuit module to realize.The present invention be not restricted to any specific hardware and The combination of software.
Although above-mentioned the embodiment of the present invention is described with reference to accompanying drawing, model not is protected to the present invention The limitation enclosed, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not Need to pay various modifications or deformation that creative work can make still within protection scope of the present invention.

Claims (9)

  1. A kind of 1. manufacturing process optimization method based on quantum-behaved particle swarm optimization, it is characterised in that including following step Suddenly：
    Step 1：Using air pressure in stove on oxygen consumption, the consumption of heavy oil, the consuming cost of machine and production line as optimization mesh Mark, using time and resource constraint as constraints, build multiple target manufacturing process Optimized model；
    Step 2：The multiple target manufacturing process Optimized model is solved using quantum-behaved particle swarm optimization.
  2. 2. the manufacturing process optimization method based on quantum-behaved particle swarm optimization, its feature exist as claimed in claim 1 In the multiple target manufacturing process Optimized model is：
    Miny=F (x)={ f1(x),f2(x),f3(x),f4(x)}
    The f1(x),f2(x),f3(x),f4(x) respectively represent oxygen consumption, the consumption of heavy oil, machine consuming cost and Four object functions of air pressure in stove on production line.
  3. 3. the manufacturing process optimization method based on quantum-behaved particle swarm optimization, its feature exist as claimed in claim 2 In, wherein, oxygen consumption：f1(x)=minXijkWijk；
    Heavy oil consumption amount：f2(x)=minXijkHijk；
    Machine consumes power：f3(x)=XijkPijk；
    Air pressure in reacting furnace：f4(x)=minXijkQijk；
    XijkRepresent that workpiece i jth procedure performs on machine k；WijkRepresent workpiece i jth procedure on machine k The amount of oxygen of consumption；HijkRepresent the heavy oil that workpiece i jth procedure consumes on machine k；PijkRepresent workpiece i jth road work The energy of the sequence in machine k consumption；QijkRepresent air pressure of the workpiece i jth procedure when being produced on machine k.
  4. 4. the manufacturing process optimization method based on quantum-behaved particle swarm optimization, its feature exist as claimed in claim 1 In, wherein,
    The time-constrain is：The beginning process time of the adjacent inter process of same workpiece has successively；
    The resource constraint is：Current task must be completed before starting next task on same board, any machine can not Two workpiece of identical or different processes are processed simultaneously.
  5. 5. the manufacturing process optimization method based on quantum-behaved particle swarm optimization, its feature exist as claimed in claim 4 In the time-constrain is expressed as：
    <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <msub> <mi>S</mi> <mrow> <mi>i</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>&amp;GreaterEqual;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <mo>&amp;lsqb;</mo> <mrow> <mo>(</mo> <msub> <mi>S</mi> <mrow> <mi>i</mi> <mrow> <mo>(</mo> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> <mi>k</mi> </mrow> </msub> <msub> <mi>t</mi> <mrow> <mi>i</mi> <mrow> <mo>(</mo> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mrow> <mo>(</mo> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> <mi>k</mi> </mrow> </msub> </mrow>
    xijk=xi(j-1)k=1
    In formula, process process time tijkRepresent that workpiece i jth procedure processes required time, S on kth platform machineijk Represent that workpiece i jth procedure starts the time of processing on kth board.Whole formula represents the workpiece i procedure of jth -1 It must be completed before jth procedure.
  6. 6. the manufacturing process optimization method based on quantum-behaved particle swarm optimization, its feature exist as claimed in claim 4 In the resource constraint is expressed as：
    xijk=xmnk=1and Rijmnq=1
    Xijk=1 represents process VijPerformed on machine k, RijmnqRepresent workpiece i jth procedure and workpiece m n-th on machine q The processing sequencing of process, Rijmnq=1 represents process j prior to process n.
  7. 7. the manufacturing process optimization method based on quantum-behaved particle swarm optimization, its feature exist as claimed in claim 1 In the quantum-behaved particle swarm optimization concretely comprises the following steps：
    (1) initialization algorithm parameter：Particle populations X, dimension size R, particle i position, maximum iteration MAXITER, most Excellent disaggregation L；
    Four object function regions in the region that particle i can be reached are arranged to：Oxygen consumption O (i), heavy oil consumption G (i), machine Four target areas, kiln furnace pressure S (i), are defined as four matrixes, so as to next iteration particle by device consumption power M (i) Renewal；
    (2) according to object function, the adaptive value of each particle is calculated；
    (3) for each particle, searching route is selected：Particle i (i=1,2 ..., R) is according to particle evolution equation in matrix O (i), G (i), M (i) and the renewal the to be reached point of the middle selection next step of S (i)；The particle evolution equation is：
    <mrow> <msub> <mi>X</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>t</mi> <mo>+</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>L</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;PlusMinus;</mo> <mi>&amp;alpha;</mi> <mo>&amp;CenterDot;</mo> <mo>|</mo> <mfrac> <mn>1</mn> <mi>M</mi> </mfrac> <munderover> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <msub> <mi>L</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>X</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>|</mo> <mo>&amp;CenterDot;</mo> <mi>ln</mi> <mrow> <mo>&amp;lsqb;</mo> <mrow> <mn>1</mn> <mo>/</mo> <msub> <mi>u</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <mo>&amp;rsqb;</mo> </mrow> </mrow>
    Wherein, α is compression-broadening factor, and t is current iteration number, and u is generally evenly distributed in the random number between 0 and 1；LijFor δ The characteristic length of potential well；XijAnd X (t)ij(t+1) position before and after particle evolution is represented respectively, and M represents potential in population and asked The colony of the key to exercises；
    (4) to each particle, the position X of the particle is calculatedi(t) individual of particle, is solved according to individual desired positions solution formula Desired positions Si(t), with preceding once particle individual desired positions Si(t-1) adaptive value is compared, if more preferably, will Si(t) as new locally optimal solution；The individual desired positions solution formula is：
    <mrow> <msub> <mi>S</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>X</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mrow> <mi>i</mi> <mi>f</mi> </mrow> </mtd> <mtd> <mrow> <mi>f</mi> <mrow> <mo>&amp;lsqb;</mo> <mrow> <msub> <mi>X</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <mo>&amp;rsqb;</mo> </mrow> <mo>&lt;</mo> <mi>f</mi> <mrow> <mo>&amp;lsqb;</mo> <mrow> <msub> <mi>S</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> </mrow> <mo>&amp;rsqb;</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>S</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mrow> <mi>i</mi> <mi>f</mi> </mrow> </mtd> <mtd> <mrow> <mi>f</mi> <mo>&amp;lsqb;</mo> <msub> <mi>X</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;GreaterEqual;</mo> <mi>f</mi> <mrow> <mo>&amp;lsqb;</mo> <mrow> <msub> <mi>S</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mrow> <msub> <mi>t</mi> <mi>i</mi> </msub> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> </mrow> </mrow> <mo>&amp;rsqb;</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
    Wherein, f () represents the current position of particle；
    (5) to each particle, its adaptive value is made comparisons with the desired positions Sbest that it passes through, if more preferably, by present bit Put as current globally optimal solution；
    (6) (2)-(5) are repeated；
    (7) end condition：Iterations reaches the maximum iteration of setting or completes the calculating in object function region.
  8. 8. a kind of manufacturing process optimization device based on quantum-behaved particle swarm optimization, including memory, processor and deposit Store up the computer program that can be run on a memory and on a processor, it is characterised in that the computing device described program Manufacturing process optimization methods based on quantum-behaved particle swarm optimization of the Shi Shixian as described in claim any one of 1-7.
  9. 9. a kind of computer-readable recording medium, is stored thereon with computer program, it is characterised in that the program is held by processor The manufacturing process optimization side based on quantum-behaved particle swarm optimization as described in claim any one of 1-7 is performed during row Method.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107479523A (en) * 2017-09-28 2017-12-15 齐鲁工业大学 Multiple agent based on QPSO manufactures process optimization method and apparatus
CN109507970A (en) * 2018-12-13 2019-03-22 合肥工业大学 Production scheduling method and apparatus based on particle swarm algorithm
CN109523065A (en) * 2018-10-29 2019-03-26 西安理工大学 A kind of micro- energy net Optimization Scheduling based on improvement quanta particle swarm optimization
CN109739087A (en) * 2018-12-29 2019-05-10 齐鲁工业大学 Multiple agent manufacturing process optimization method based on multi-objective particle swarm algorithm
CN111931419A (en) * 2020-07-30 2020-11-13 广东工业大学 Improved particle swarm algorithm-based ceramic roller kiln process parameter optimization method
CN113960971A (en) * 2021-10-27 2022-01-21 江南大学 Flexible workshop scheduling method based on behavioral decision network particle swarm optimization

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101604418A (en) * 2009-06-29 2009-12-16 浙江工业大学 Chemical enterprise intelligent production plan control system based on quanta particle swarm optimization
CN101727622A (en) * 2009-12-08 2010-06-09 华南理工大学 layering modeling and optimizing method targeting complicated manufacture system
CN101944201A (en) * 2010-07-27 2011-01-12 昆明理工大学 Multi-agent-based steelmaking workshop crane scheduling simulation method
CN103020730A (en) * 2012-11-01 2013-04-03 西安电子科技大学 Resource-constrained project scheduling method based on multi-agent evolutionary algorithm
CN102738835B (en) * 2012-07-17 2014-08-20 山西大学 Wind-fire-water co-scheduling method on basis of multi-agent system
CN104914835A (en) * 2015-05-22 2015-09-16 齐鲁工业大学 Flexible job-shop scheduling multi-objective method
CN105929689A (en) * 2016-04-22 2016-09-07 江南大学 Machine tool manufacturing system processing and energy saving optimization method based on particle swarm algorithm
CN107479523A (en) * 2017-09-28 2017-12-15 齐鲁工业大学 Multiple agent based on QPSO manufactures process optimization method and apparatus

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101604418A (en) * 2009-06-29 2009-12-16 浙江工业大学 Chemical enterprise intelligent production plan control system based on quanta particle swarm optimization
CN101727622A (en) * 2009-12-08 2010-06-09 华南理工大学 layering modeling and optimizing method targeting complicated manufacture system
CN101944201A (en) * 2010-07-27 2011-01-12 昆明理工大学 Multi-agent-based steelmaking workshop crane scheduling simulation method
CN102738835B (en) * 2012-07-17 2014-08-20 山西大学 Wind-fire-water co-scheduling method on basis of multi-agent system
CN103020730A (en) * 2012-11-01 2013-04-03 西安电子科技大学 Resource-constrained project scheduling method based on multi-agent evolutionary algorithm
CN104914835A (en) * 2015-05-22 2015-09-16 齐鲁工业大学 Flexible job-shop scheduling multi-objective method
CN105929689A (en) * 2016-04-22 2016-09-07 江南大学 Machine tool manufacturing system processing and energy saving optimization method based on particle swarm algorithm
CN107479523A (en) * 2017-09-28 2017-12-15 齐鲁工业大学 Multiple agent based on QPSO manufactures process optimization method and apparatus

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
RUNZE WANG: "Research on Multi Agent Manufacturing Process Optimization Method Based on", 《IEEE》 *
李阳: "基于多智能体量子粒子群算法的电网无功优化问题研究", 《中国优秀硕士学位论文全文数据库》 *
黄丽，等: "基于改进量子粒子群算法负荷优化分配研究", 《控制工程》 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107479523A (en) * 2017-09-28 2017-12-15 齐鲁工业大学 Multiple agent based on QPSO manufactures process optimization method and apparatus
CN109523065A (en) * 2018-10-29 2019-03-26 西安理工大学 A kind of micro- energy net Optimization Scheduling based on improvement quanta particle swarm optimization
CN109523065B (en) * 2018-10-29 2021-09-10 西安理工大学 Micro energy network optimization scheduling method based on improved quantum particle swarm algorithm
CN109507970A (en) * 2018-12-13 2019-03-22 合肥工业大学 Production scheduling method and apparatus based on particle swarm algorithm
CN109739087A (en) * 2018-12-29 2019-05-10 齐鲁工业大学 Multiple agent manufacturing process optimization method based on multi-objective particle swarm algorithm
CN111931419A (en) * 2020-07-30 2020-11-13 广东工业大学 Improved particle swarm algorithm-based ceramic roller kiln process parameter optimization method
CN113960971A (en) * 2021-10-27 2022-01-21 江南大学 Flexible workshop scheduling method based on behavioral decision network particle swarm optimization

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