CN108829036A - A kind of Optimization Scheduling of metal slab shaping by stock removal process - Google Patents
A kind of Optimization Scheduling of metal slab shaping by stock removal process Download PDFInfo
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- 238000005058 metal casting Methods 0.000 claims description 4
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/406—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by monitoring or safety
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/37—Measurements
- G05B2219/37616—Use same monitoring tools to monitor tool and workpiece
Abstract
The present invention relates to a kind of Optimization Schedulings of metal slab shaping by stock removal process, belong to machining production process intelligent optimization dispatching technique field.The present invention passes through the scheduling model and optimization aim for determining metal slab shaping by stock removal process in factory, and is optimized using the Optimization Scheduling based on improved multiple target grey wolf optimization algorithm to target；Wherein, scheduling model is established according to process number, process time and machine speed of the every kind of metal slab in every equipment, and the first optimization aim is minimizes Maximal Makespan in optimization aim, and second optimization aim is total carbon emissions amount.The present invention can reduce the production cost and carbon emission amount of factory, the production efficiency of factory can be improved, the problem of green production mode for pushing factory can effectively solve metal slab shaping by stock removal process since the improper caused factory cost of Machining Sequencing wastes, and the economic benefit is not high and environmental pollution.
Description
Technical field
The present invention relates to a kind of Optimization Schedulings of metal slab shaping by stock removal process, belong to machining production
Process intelligent optimization dispatching technique field.
Background technique
For a long time, machinery manufacturing industry is the pillar industry of Chinese national economy, is made of various components
Mechanized equipment, meet people's lives and demand for labour, improve labor efficiency, reduce production cost, promote people
The prosperity and development of class social civilization.Component of machine all has certain shape, different by specific one or multiple properties
Surface composition, such as spherical surface, plane.
Meet mechanized equipment requirement to obtain, is required with specific shape, size and surface roughness aspect
Component of machine, machinery manufacturing industry is frequently with cutting working method, to meet the needs of large-scale industrial production.Machining is just
It is with specific cutting element by material removal extra on metal slab, final obtain meets the use of mechanized equipment various aspects
It is required that component of machine product technology method.It is the important link in current manufacturing technology.Metal slab
Shaping by stock removal is processed based on batch production mode, different metal casting blank specifications and technique requirement, so that each metal slab exists
The process time in each stage, there are larger differences.Different metal slab job sequences can be to the complete working hour of the batch production plan
Between and total carbon emission produce bigger effect, this will directly affect the production cost and economic benefit of factory.At this stage, factory's tune
Degree person is mainly scheduled using the allocation rule based on minimum makespan, i.e., is risen according to each metal slab completion date
Sequence arrangement, in this, as job sequence.This mode can reduce the completion date and carbon emission of production plan to a certain extent
Amount, but can not be in view of the coupling between metal slab job sequence, and scheduling scheme is single, is unable to meet production meter
The unexpected incidents drawn and multifarious demand.Therefore, just seem especially to the Optimized Operation of metal slab forming process
Important, a good scheduling scheme can largely reduce the production cost of factory, improve the economic benefit of factory.
The present invention uses arranged model, designs a kind of Optimized Operation side based on improved multiple target grey wolf optimization algorithm
Method can obtain the approximate optimal solution of metal slab shaping by stock removal process scheduling problem, to reduce work within a short period of time
The production cost and carbon emission amount of factory, improve the economic benefit of factory.
Summary of the invention
The purpose of the present invention is directed to metal slab shaping by stock removal process scheduling problem, proposes a kind of based on improved more
The Optimization Scheduling of the metal slab shaping by stock removal process of target grey wolf optimization algorithm, is cut with solving existing metal slab
Cutting in process leads to that factory cost waste, economic benefit is low and environmental pollution is serious since Machining Sequencing is improper.
The technical scheme is that：A kind of Optimization Scheduling of metal slab shaping by stock removal process, by true
The scheduling model and optimization aim of deposit category slab shaping by stock removal process in factory, and using based on improved multiple target
The Optimization Scheduling of grey wolf optimization algorithm optimizes target；Wherein, scheduling model is according to every kind of metal slab at every
Process number, process time and machine speed in equipment are established, in optimization aim the first optimization aim be minimize it is maximum complete
F between working hour1=Cmax(π), second optimization aim are total carbon emissions amount f2=TCE：
min{f1,f2}=min { Cmax(π),TCE}
Cmax(π)=Cπ(n),m
Cπ(i),j=max { Cπ(i-1),j,Cπ(i),j-1}+pπ(i),j/Vπ(i),j
Cπ(1),j=Cπ(1),j-1+pπ(1),j/Vπ(1),j
Cπ(i),1=Cπ(i-1),1+pπ(i),1/Vπ(i),1
Cπ(1),1=pπ(1),1/Vπ(1),1
In formula, n indicates metal slab number；M indicates number of machines；S indicates machining sets of speeds, and every machine has s
Kind process velocity is available；After each process operation speed of machine is selected, it can not be changed before process operation completion, it should
One solution of Optimal Scheduling is π={ π (1), π (2) ..., π (n) }, and π indicates the job sequence of slab in factory, π (i)
Indicate the slab of i-th of position in π；Oi,jIndicate metal slab i in the operation on machine j and will not after providing that the operation starts
Allow to interrupt, each operation Oi,jThere is corresponding standard process time Pi,j, when machine j is with speed Vi,jProcess operation Oi,j,
Process operation O at this timei,jProcess time become Pi,j/Vi,j, the consumption of unit time manufactured energy is PPj,v, each process operation
Oi,jCompletion date be Ci,j；When machine is in idle condition, machine will be in standby mode, SPjIndicate that the unit time waits for
Machine energy consumption；Regulation is for the same operation Oi,j, process time is accordingly reduced if machine speed increases, and energy consumption is corresponding
Increase, i.e., machine speed increases to u by v, then needs to meet p simultaneouslyi,j/ u < pi,j/ v, ppj,u·pi,j/ u > ppj,v·pi,j/v；
Xj(t) it is in machining state in t moment for 1 expression machine j, 0 indicates to be in standby；ε indicate energy consumption and carbon emission amount it
Between conversion coefficient, usually take 0.7559；
The Optimization Scheduling based on improved multiple target grey wolf optimization algorithm is specially：
Step1, initialization of population：Initialization population Initpop is generated using random device, until the quantity of initial solution reaches
To the requirement of population scale；Wherein, population scale NP；
Step2, Archive initialization of population：Non-domination solution in initial population constitutes Archive population, setting
The maximum number of individuals of Archive population is AP；
Step3, population recruitment：Each of population individual is a solution, using roulette strategy to Archive population
It is grouped, selects 3 non-domination solutions, i.e., wolf where optimal solution, excellent solution, suboptimal solution (it is respectively labeled as α, β, δ wolf, remaining
Individual mark is ω wolf), during hunting, wolf pack updates respective positions under the guidance of α, β, δ wolf, (complete to food position
Office's optimal solution) it approaches, guidance equation is as follows：
Since improved multiple target grey wolf optimization algorithm is based on continuous real number field, and metal slab shaping by stock removal process
It based on discrete variable, therefore uses and real coding is carried out based on Operation Sequencing of the random code mode to metal slab, then root
The mapping relations one by one between real coding and integer coding are established according to LOV rule, and then are realized from real coding to metal casting
The conversion of blank process sequence；DpIndicate the position after grey wolf approaches to food；Position (the X of X expression grey wolfi=[xi,1,xi,1,…,
xi,n]), XpIndicate the guide position (i.e. the position of α, β, δ wolf) of prey；T indicates the number of iterations；α indicates control parameter；Maxt
Indicate maximum number of iterations；C and A indicates guidance coefficient；γ1、γ2It is the random number in [0,1] range；
Step4, Archive population recruitment：The target function value for calculating each individual in wolf pack, is selected non-dominant in wolf pack
It solves and is compared one by one with the individual in Archive population, dominate the solution in Archive population if there is non-domination solution,
Then Archive population is updated；
Step5, the local search based on Swap and Insert：All individuals in Archive population are successively executed
" Swap " and " Insert " operation is replaced if the individual that local search obtains is better than current individual, and by contemporary kind
Group is used as a new generation Archive population；
Step6, termination condition：Termination condition is set as Riming time of algorithm T=50 × n, if algorithm meets maximum and changes
Generation number Maxt or running time T then export " optimal solution "；Otherwise step Step3 is gone to, is iterated, is terminated until meeting
Until condition.
The beneficial effects of the invention are as follows：The present invention propose a kind of metal slab shaping by stock removal process scheduling model and
Optimization aim can obtain the excellent solution of metal slab shaping by stock removal process scheduling problem in a short time, can reduce factory
Production cost and carbon emission amount, the production efficiency of factory can be improved, push the green production mode of factory, can effectively solve gold
Belong to slab shaping by stock removal process due to the improper caused factory cost waste of Machining Sequencing, the economic benefit is not high and environment is dirty
The problem of dye.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is Optimization Scheduling flow chart of the invention；
Fig. 3 is the expression schematic diagram of solution in the present invention；
Fig. 4 is that the basic field " Insert " of the invention changes schematic diagram；
Fig. 5 is that the basic field " Swap " of the invention changes schematic diagram.
Specific embodiment
Embodiment 1：As shown in Figs. 1-5, a kind of Optimization Scheduling of metal slab shaping by stock removal process, by true
The scheduling model and optimization aim of deposit category slab shaping by stock removal process in factory, and using based on improved multiple target
The Optimization Scheduling of grey wolf optimization algorithm optimizes target；Wherein, scheduling model is according to every kind of metal slab at every
Process number, process time and machine speed in equipment are established, in optimization aim the first optimization aim be minimize it is maximum complete
F between working hour1=Cmax(π), second optimization aim are total carbon emissions amount f2=TCE：
min{f1,f2}=min { Cmax(π),TCE}
Cmax(π)=Cπ(n),m
Cπ(i),j=max { Cπ(i-1),j,Cπ(i),j-1}+pπ(i),j/Vπ(i),j
Cπ(1),j=Cπ(1),j-1+pπ(1),j/Vπ(1),j
Cπ(i),1=Cπ(i-1),1+pπ(i),1/Vπ(i),1
Cπ(1),1=pπ(1),1/Vπ(1),1
In formula, n indicates metal slab number；M indicates number of machines；S indicates machining sets of speeds, and every machine has s
Kind process velocity is available；After each process operation speed of machine is selected, it can not be changed before process operation completion, it should
One solution of Optimal Scheduling is π={ π (1), π (2) ..., π (n) }, and π indicates the job sequence of slab in factory, π (i)
Indicate the slab of i-th of position in π；Oi,jIndicate metal slab i in the operation on machine j and will not after providing that the operation starts
Allow to interrupt, each operation Oi,jThere is corresponding standard process time Pi,j, when machine j is with speed Vi,jProcess operation Oi,j,
Process operation O at this timei,jProcess time become Pi,j/Vi,j, the consumption of unit time manufactured energy is PPj,v, each process operation
Oi,jCompletion date be Ci,j；When machine is in idle condition, machine will be in standby mode, SPjIndicate that the unit time waits for
Machine energy consumption；Regulation is for the same operation Oi,j, process time is accordingly reduced if machine speed increases, and energy consumption is corresponding
Increase, i.e., machine speed increases to u by v, then needs to meet p simultaneouslyi,j/ u < pi,j/ v, ppj,u·pi,j/ u > ppj,v·pi,j/v；
Xj(t) it is in machining state in t moment for 1 expression machine j, 0 indicates to be in standby；ε indicate energy consumption and carbon emission amount it
Between conversion coefficient, usually take 0.7559；
The Optimization Scheduling based on improved multiple target grey wolf optimization algorithm is specially：
Step1, initialization of population：Initialization population Initpop is generated using random device, until the quantity of initial solution reaches
To the requirement of population scale；Wherein, population scale NP；
Step2, Archive initialization of population：Non-domination solution in initial population constitutes Archive population, setting
The maximum number of individuals of Archive population is AP；
Step3, population recruitment：Each of population individual is a solution, using roulette strategy to Archive population
It is grouped, selects 3 non-domination solutions, i.e., wolf where optimal solution, excellent solution, suboptimal solution (it is respectively labeled as α, β, δ wolf, remaining
Individual mark is ω wolf), during hunting, wolf pack updates respective positions under the guidance of α, β, δ wolf, (complete to food position
Office's optimal solution) it approaches, guidance equation is as follows：
Since improved multiple target grey wolf optimization algorithm is based on continuous real number field, and metal slab shaping by stock removal process
It based on discrete variable, therefore uses and real coding is carried out based on Operation Sequencing of the random code mode to metal slab, then root
The mapping relations one by one between real coding and integer coding are established according to LOV rule, and then are realized from real coding to metal casting
The conversion of blank process sequence；DpIndicate the position after grey wolf approaches to food；Position (the X of X expression grey wolfi=[xi,1,xi,1,…,
xi,n]), XpIndicate the guide position (i.e. the position of α, β, δ wolf) of prey；T indicates the number of iterations；α indicates control parameter；Maxt
Indicate maximum number of iterations；C and A indicates guidance coefficient；γ1、γ2It is the random number in [0,1] range；
Step4, Archive population recruitment：The target function value for calculating each individual in wolf pack, is selected non-dominant in wolf pack
It solves and is compared one by one with the individual in Archive population, dominate the solution in Archive population if there is non-domination solution,
Then Archive population is updated；
Step5, the local search based on Swap and Insert：All individuals in Archive population are successively executed
" Swap " and " Insert " operation is replaced if the individual that local search obtains is better than current individual, and by contemporary kind
Group is used as a new generation Archive population；
Step6, termination condition：Termination condition is set as Riming time of algorithm T=50 × n, if algorithm meets maximum and changes
Generation number Maxt or running time T then export " optimal solution "；Otherwise step Step3 is gone to, is iterated, is terminated until meeting
Until condition.
Population scale NP is set as 50, Archive population scale AP and is set as 10, machine speed gear be set as S=1,
1.1,1.2,1.3,1.4 }, PPj,v=4 × v2, SPj=1, maximum number of iterations Maxt is set as 100.Table 1 gives difference and asks
Obtained target function value under topic scale.
Obtained target function value under the different problem scales of table 1
| n×m | 30×5 | 30×10 | 50×5 | 50×10 | 70×5 | 70×10 |
| Cmax(π) | 837 | 770 | 1756 | 1053 | 2743 | 2032 |
| TCE | 8971 | 8432 | 15374 | 12980 | 29564 | 25883 |
Above in conjunction with attached drawing, the embodiment of the present invention is explained in detail, but the present invention is not limited to above-mentioned
Embodiment within the knowledge of a person skilled in the art can also be before not departing from present inventive concept
Put that various changes can be made.
Claims (1)
1. a kind of Optimization Scheduling of metal slab shaping by stock removal process, it is characterised in that：By determining metal slab
The scheduling model and optimization aim of shaping by stock removal process in factory, and calculated using based on the optimization of improved multiple target grey wolf
The Optimization Scheduling of method optimizes target；Wherein, work of the scheduling model according to every kind of metal slab in every equipment
Ordinal number, process time and machine speed are established, and the first optimization aim is minimizes Maximal Makespan f in optimization aim1=
Cmax(π), second optimization aim are total carbon emissions amount f2=TCE：
min{f1,f2}=min { Cmax(π),TCE}
Cmax(π)=Cπ(n),m
Cπ(i),j=max { Cπ(i-1),j,Cπ(i),j-1}+pπ(i),j/Vπ(i),j
Cπ(1),j=Cπ(1),j-1+pπ(1),j/Vπ(1),j
Cπ(i),1=Cπ(i-1),1+pπ(i),1/Vπ(i),1
Cπ(1),1=pπ(1),1/Vπ(1),1
In formula, n indicates metal slab number；M indicates number of machines；S indicates machining sets of speeds, and every machine has s kind to add
Work speed is available；After each process operation speed of machine is selected, it can not be changed before process operation completion, the optimization
One solution of scheduling problem is π={ π (1), π (2) ..., π (n) }, and π indicates the job sequence of slab in factory, and π (i) indicates π
In i-th of position slab；Oi,jIn indicating that metal slab i would not allow in the operation on machine j and after providing that the operation starts
It is disconnected, each operation Oi,jThere is corresponding standard process time Pi,j, when machine j is with speed Vi,jProcess operation Oi,j, at this time plus
Work operates Oi,jProcess time become Pi,j/Vi,j, the consumption of unit time manufactured energy is PPj,v, each process operation Oi,jIt is complete
It is C between working houri,j；When machine is in idle condition, machine will be in standby mode, SPjIndicate that the unit time standby energy disappears
Consumption；Regulation is for the same operation Oi,j, process time is accordingly reduced if machine speed increases, and energy consumption is increase accordingly, i.e. machine
Device speed increases to u by v, then needs to meet p simultaneouslyi,j/ u < pi,j/ v, ppj,u·pi,j/ u > ppj,v·pi,j/v；XjIt (t) is 1 table
Show that machine j is in machining state in t moment, 0 indicates to be in standby；ε indicates the conversion system between energy consumption and carbon emission amount
Number, usually takes 0.7559；
The Optimization Scheduling based on improved multiple target grey wolf optimization algorithm is specially：
Step1, initialization of population：Initialization population Initpop is generated using random device, until the quantity of initial solution reaches kind
The requirement of group's scale；Wherein, population scale NP；
Step2, Archive initialization of population：Non-domination solution in initial population constitutes Archive population, and Archive kind is arranged
The maximum number of individuals of group is AP；
Step3, population recruitment：Each of population individual is a solution, is carried out using roulette strategy to Archive population
3 non-domination solutions are selected in grouping, i.e., the wolf where optimal solution, excellent solution, suboptimal solution (is respectively labeled as α, β, δ wolf, remaining individual
Labeled as ω wolf), during hunting, wolf pack updates respective positions under the guidance of α, β, δ wolf, (global most to food position
Excellent solution) it approaches, guidance equation is as follows：
Since improved multiple target grey wolf optimization algorithm is based on continuous real number field, and metal slab shaping by stock removal process is based on
Discrete variable, therefore use and real coding is carried out based on Operation Sequencing of the random code mode to metal slab, then according to LOV
Rule establishes the mapping relations one by one between real coding and integer coding, and then realizes from real coding to metal casting blank process
The conversion of sequence；DpIndicate the position after grey wolf approaches to food；Position (the X of X expression grey wolfi=[xi,1,xi,1,…,xi,n]),
XpIndicate the guide position (i.e. the position of α, β, δ wolf) of prey；T indicates the number of iterations；α indicates control parameter；Maxt is indicated most
Big the number of iterations；C and A indicates guidance coefficient；γ1、γ2It is the random number in [0,1] range；
Step4, Archive population recruitment：The target function value for calculating each individual in wolf pack selects in wolf pack non-domination solution simultaneously
It is compared one by one with the individual in Archive population, dominates the solution in Archive population if there is non-domination solution, then it is right
Archive population is updated；
Step5, the local search based on Swap and Insert：" Swap " successively is executed to all individuals in Archive population
" Insert " operation, if the individual that local search obtains better than being replaced if current individual, and using contemporary population as
Archive population of new generation；
Step6, termination condition：Termination condition is set as Riming time of algorithm T=50 × n, if algorithm meets greatest iteration time
Number Maxt or running time T, then export " optimal solution "；Otherwise step Step3 is gone to, is iterated, until meeting termination condition
Until.
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| CN109669423A (en) * | 2019-01-07 | 2019-04-23 | 福州大学 | The method that part processes optimal scheduling scheme is obtained based on multiple target grey wolf algorithm is improved |
| CN109669423B (en) * | 2019-01-07 | 2021-08-31 | 福州大学 | Method for obtaining optimal scheduling scheme of part machining based on improved multi-target wolf algorithm |
| CN109783968A (en) * | 2019-01-25 | 2019-05-21 | 山东大学 | The threedimensional FEM method of metal cutting process based on Simulation Based On Multi-step |
| CN109783968B (en) * | 2019-01-25 | 2021-02-12 | 山东大学 | Three-dimensional finite element simulation method of metal cutting process based on multiple process steps |
| CN111522315A (en) * | 2020-04-30 | 2020-08-11 | 昆明理工大学 | Optimized scheduling method for lithium battery lamination processing process |
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