CN108492025A - High-end equipment manufacturing coordinated dispatching method based on mixing difference genetic algorithm - Google Patents
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Abstract
The present invention provides a kind of high-end equipment manufacturing coordinated dispatching methods based on mixing difference genetic algorithm, including：Obtain mixing differential evolution and genetic algorithm parameter；Q chromosome is defined, each chromosome includes multiple genes, with initialization population vector；Code modification operation is executed based on the Q chromosome；It will go out to produce and process at workpiece arrangement to each manufacturer according to revised gene, calculate the manufacture span of each scheme；The chromosome that fitness value minimum is screened from Q chromosome, obtains current globally optimal solution gbest；Make great efforts crossover operation using shellfish and updates the population vector；The population vector is updated using Immigrant strategy, until output globally optimal solution gbest is as optimal height scheme.The present invention can successfully solve the problems, such as the production and transport cooperative scheduling in distributed manufacturing process, be conducive to the overall efficiency and service level that improve production system.
Description
Technical field
The present invention relates to the production scheduling technical fields towards high-end equipment, more particularly to one kind is based on mixing difference heredity
The high-end equipment manufacturing coordinated dispatching method of algorithm.
Background technology
Distributed production towards high-end equipment be it is a kind of being different from conventionally manufactured mode, the decentralized mode of production,
Its production process is typically to be completed by the different manufacturers cooperation for being distributed in different zones, therefore, in entire production process, no
Cooperative scheduling between the different production links being responsible for manufacturer, for improving whole production efficiency, saving production, transport
And carrying cost, optimization product quality, etc., important influence will be generated.But existing research and solution, Duo Shuoshi
Optimization to single manufacturer or production link in production process, and due in distributed production environment, each production link
Between taken by area distribution, production, transportation cost etc. multiple factors are influenced, existing solution is difficult to limited
The optimal solution of cooperative scheduling is obtained in time.
Invention content
For the defects in the prior art, the present invention provides a kind of high-end equipment systems based on mixing difference genetic algorithm
Process synergic dispatching method is made, for solving technical problem present in the relevant technologies.
In a first aspect, an embodiment of the present invention provides a kind of high-end equipment manufacturings based on mixing difference genetic algorithm
Coordinated dispatching method, the method includes：
Obtain mixing differential evolution and genetic algorithm parameter；
Q chromosome is defined, each chromosome includes multiple genes, with initialization population vector；
Code modification operation is executed based on the Q chromosome；
It will go out to produce and process at workpiece arrangement to each manufacturer according to revised gene, calculate the system of each scheme
Make span；
The chromosome that fitness value minimum is screened from Q chromosome, obtains current globally optimal solution gbest；
Make great efforts crossover operation using shellfish and updates the population vector；
The population vector is updated using Immigrant strategy, until output globally optimal solution gbest is as optimal height scheme.
Optionally, executing code modification operation based on the Q chromosome includes：
301, obtain newly generated Q chromosome in population vectorI=1,2 ..., Q；
302, setup parameter d=1；
303, judge gene in i-th of chromosomeIt is whether true；If so, then enable
304, judgeIt is whether true, if so, then enable
305, set geneRound (x) is indicated to x rounds；
306, judge whether I ＞ n are true, if so, then makeover process is completed, and otherwise, enables I=I+1, return to step 303；
Optionally, the fitness value for calculating each chromosome in the Q chromosome includes：
401, the workpiece at each manufacturer is rearranged according to process time non-increasing；
402, it is processed successively according to the workpiece at the new each manufacturer of sequence pair.
403, defined variable l, tl, and initialize l=1；
404, initialize tl=0,；
405, the completion date of each workpiece at each manufacturer is assigned to t successivelyl, and by tl+TlIt is assigned to tl；
406, whether Rule of judgment l≤m meets, if satisfied, then return to step 404；Otherwise the complete of each manufacturer is obtained
Between working hour, set t={ t1,…,tl,…,tm, and return to step 407；
407, enable Cmax=max1≤l≤m(tl), wherein and CmaxIt is assigned to fit (Xi), it is denoted as chromosome xiFitness
Value.
Optionally, before making great efforts the crossover operation update population vector using shellfish, the method further includes：
According to the weight of each chromosome described in the fitness value calculation of each chromosome；
Two parent chromosomes of roulette selection, and intersected using described two parent chromosomes and generate a new filial generation
Chromosome, until generate Q filial generation genome at progeny population vector it is vectorial to substitute the population.
Optionally, two parent chromosomes of roulette selection include：
501, obtain the weight vectors W={ W of population chromosome1,W2,...,Wi,...,Wn}；
502, setting variable sum=0, h=1.
503, enable r=rand (0,1), wherein rand (0,1) indicates the random number between 0 to 1.
504, enable sum=sum+Wh, judge whether sum >=r is true, if so, h is then exported, selection is terminated；Otherwise, h is enabled
=h+1 and return to step 503.
Optionally, updating the population vector using Immigrant strategy includes：
601, two chromosomes are randomly selected from updated population vectorWith, calculate
VhIt is and XhThe identical solution vector of structure；
602, it is based on Xh,VhIt executes shellfish effort crossover operation and obtains Uh, calculate
Wherein, rand (0,1) indicates the random number between 0 to 1, obtains new chromosome vector
By UhIt is assigned to Xh；
603, h=h+1 is enabled, judges whether h≤Q is true, if so, then return to step 601；Otherwise, step 604 is executed；
604, fitness is calculated, globally optimal solution is updated；And ε after fitness value in population vector sorts from small to large
N chromosome is substituted with RANDOM SOLUTION.
As shown from the above technical solution, the embodiment of the present invention is directed under the distributed manufacturing environment towards high-end equipment and produces
New meta-heuristic algorithm is devised with transport cooperative scheduling problem, and the object function of span as an optimization will be manufactured, effectively
Reduce the cooperation cost between enterprise.
Scheduling of production is first carried out to all workpiece according to coding in the present embodiment, task is defined to each manufacture
Quotient；Solution is optimized further according to the evolution laws of population, combines the manufacture span and crossover operator of kinds of schemes, realization pair
Problem target continues to optimize to obtain optimal solution or approximate optimal solution.
Differential evolution is mixed in the present embodiment and genetic algorithm not only allows for the natural evolvement rule of population, more by difference
The principle of evolution is applied to that population is instructed to develop, and is effectively guaranteed the quality of optimum results, further improves the essence of solution
Degree.
The present embodiment can successfully solve the problems, such as the production and transport cooperative scheduling in distributed manufacturing process, be conducive to improve
The overall efficiency and service level of production system.
It should be understood that above general description and following detailed description is only exemplary and explanatory, not
The disclosure can be limited.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Other attached drawings are obtained according to these figures.
Fig. 1 is that the high-end equipment manufacturing based on mixing difference genetic algorithm that one embodiment of the invention provides cooperates with tune
The flow diagram of degree method；
Fig. 2 is that the high-end equipment manufacturing based on mixing difference genetic algorithm that one embodiment of the invention provides cooperates with tune
The detail flowchart of degree method；
Fig. 3 is the flow diagram for the code modification operation that one embodiment of the invention provides；
Fig. 4 is the flow diagram that the fitness that one embodiment of the invention provides calculates；
Fig. 5 is the flow diagram for the roulette selection that one embodiment of the invention provides；
Fig. 6 is the flow diagram for the Immigrant strategy Population Regeneration vector that one embodiment of the invention provides.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Fig. 1 is that the high-end equipment manufacturing based on mixing difference genetic algorithm that one embodiment of the invention provides cooperates with tune
The method flow schematic diagram of degree method.Fig. 2 is the high-end dress based on mixing difference genetic algorithm that one embodiment of the invention provides
Prepare the detail flowchart for making process synergic dispatching method.It referring to Fig. 1 and Fig. 2, should be based on the high-end of mixing difference genetic algorithm
Equipment manufacturing coordinated dispatching method includes：
101, obtain mixing differential evolution and genetic algorithm parameter；
102, Q chromosome is defined, each chromosome includes multiple genes, with initialization population vector；
103, code modification operation is executed based on the Q chromosome；
104, it will go out to produce and process at workpiece arrangement to each manufacturer according to revised gene, calculate each scheme
Manufacture span；
105, the chromosome of fitness value minimum is screened from Q chromosome, obtains current globally optimal solution gbest；
106, make great efforts crossover operation using shellfish and updates the population vector；
107, the population vector is updated using Immigrant strategy, until output globally optimal solution gbest is as optimal height
Scheme.
Each step is described in detail with reference to the accompanying drawings and examples.
First, the step of introducing 101, obtaining mixing differential evolution and genetic algorithm parameter.
In the present embodiment, setting mixing differential evolution and genetic algorithm parameter, including mutation probability M=0.1, migrate ratio
ε=0.1, scaling F, current iteration number t=1, maximum iteration tmax, globally optimal solution gbest, shellfish effort intersection
Probability MB=0.1+0.9e-t, wherein e indicates natural logrithm.
It will be appreciated that the numerical value of each parameter can also be configured according to concrete scene in the present embodiment.
Secondly, 102 are introduced, Q chromosome is defined, each chromosome includes multiple genes, with initialization population vector
Step.
In the present embodiment, consider to share Q chromosome, the gene of i-th of chromosome is defined as
WhereinIt indicates gene of i-th of the chromosome in d dimensions, i.e., will
D workpiece is assigned to roundIt is processed at manufacturer, wherein round (x) is indicated to x rounds.
Again, the step of introducing 103, code modification operation executed based on the Q chromosome.
Code modification operation includes the following steps in the present embodiment：
Step 301, newly generated Q chromosome in population vector is obtained
Step 302, setup parameter d=1；
Step 303, judge gene in i-th of chromosomeIt is whether true；If so, then enable
Step 304, judgeIt is whether true, if so, then enableIf not, then execute next step；
Step 305, gene is setRound (x) is indicated to x rounds；
Step 306, judge whether I ＞ n are true, if so, then makeover process is completed, and otherwise, enables I=I+1, return to step
303。
4th, 104 are introduced, will go out to produce and process at workpiece arrangement to each manufacturer according to revised gene,
The step of calculating the manufacture span of each scheme.
In the present embodiment, according to modified gene, work is arranged into respectively at different manufacturers, to obtain not
Same scheme, then calculates the manufacture span C of each schememax。
5th, 105 are introduced, the chromosome of fitness value minimum is screened from Q chromosome, obtains current globally optimal solution
The step of gbest.
In the present embodiment, the fitness value of each chromosome is obtained, including：
401, the workpiece at each manufacturer is rearranged according to process time non-increasing；
402, it is processed successively according to the workpiece at the new each manufacturer of sequence pair；
403, defined variable l, tl, and initialize l=1；
404, initialize tl=0,；
405, the completion date of each workpiece at each manufacturer is assigned to t successivelyl, and by tl+TlIt is assigned to tl；
406, whether Rule of judgment l≤m meets, if satisfied, l=l+1 is enabled, and return to step 404；Otherwise each system is obtained
Make the completion date of quotient, set t={ t1,…,tl,…,tm, and return to step 407；
407, enable manufacture span Cmax=max1≤l≤m(tl), wherein and CmaxIt is assigned to fit (Xi), it is denoted as chromosome xi's
Fitness value fiti。
On the basis of the above, the fitness value of more all dyeing is found out the chromosome of fitness minimum, is determined as
Current globally optimal solution gbest.
6th, 106 are introduced, makes great efforts the step that crossover operation updates the population vector using shellfish.
In the present embodiment, the weight of each chromosome is calculated,Wherein, fitiIndicate i-th of dyeing
The fitness of body.
Then, two parent chromosomes of roulette selection are used in above-mentioned population vector, and are handed over using parent chromosome
Fork generates a new child chromosome.Repeat this step, until generate Q genome at progeny population, replacement original seed
Group, setting variable h=1.
In one embodiment, roulette selection includes the following steps：
501, the weight vectors W={ W of population chromosome are obtained1,W2,...,Wi,...,Wn}；
502, setting variable sum=0, h=1.
503, r=rand (0,1) is enabled, wherein rand (0,1) indicates the random number between 0 to 1.
504, sum=sum+W is enabledh, judge whether sum >=r is true, if so, h is then exported, selection is terminated；Otherwise, h is enabled
=h+1 and return to step 503.
Finally, 107 are introduced, the population vector is updated using Immigrant strategy, until output globally optimal solution gbest conducts
The step of optimal height scheme.
601, two chromosomes are randomly selected from updated population vectorWithIt calculates
VhIt is and XhThe identical solution vector of structure；
602, it is based on Xh,VhIt executes shellfish effort crossover operation and obtains Uh, calculateIts
In, rand (0,1) indicates the random number between 0 to 1, obtains new chromosome vectorIt will
UhIt is assigned to Xh；
603, h=h+1 is enabled, judges whether h≤Q is true, if so, then return to step 601；Otherwise, step 604 is executed；
604, fitness is calculated, globally optimal solution is updated；And ε after fitness value in population vector sorts from small to large
N chromosome is substituted with RANDOM SOLUTION.
Judge t≤tmaxIt is whether true, if so, otherwise return to step 103 terminates algorithm and exports globally optimal solution.
As shown from the above technical solution, the embodiment of the present invention cooperates with tune for the production under distributed manufacturing environment with transport
Degree problem devises new meta-heuristic algorithm, and will manufacture the object function of span as an optimization, effectively between reduction enterprise
Cooperate cost.
Scheduling of production is first carried out to all workpiece according to coding in the present embodiment, task is defined to each manufacture
Quotient；Solution is optimized further according to the evolution laws of population, combines the manufacture span and crossover operator of kinds of schemes, realization pair
Problem target continues to optimize to obtain optimal solution or approximate optimal solution.
Differential evolution is mixed in the present embodiment and genetic algorithm not only allows for the natural evolvement rule of population, more by difference
The principle of evolution is applied to that population is instructed to develop, and is effectively guaranteed the quality of optimum results, further improves the essence of solution
Degree.
The present embodiment can successfully solve the problems, such as the production and transport cooperative scheduling in distributed manufacturing process, be conducive to improve
The overall efficiency and service level of production system.
In the specification of the present invention, numerous specific details are set forth.It is to be appreciated, however, that the embodiment of the present invention can be with
It puts into practice without these specific details.In some instances, well known method, structure and skill is not been shown in detail
Art, so as not to obscure the understanding of this description.
Finally it should be noted that：The above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Present invention has been described in detail with reference to the aforementioned embodiments for pipe, it will be understood by those of ordinary skill in the art that：Its according to
So can with technical scheme described in the above embodiments is modified, either to which part or all technical features into
Row equivalent replacement；And these modifications or replacements, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The range of scheme should all cover in the claim of the present invention and the range of specification.
Claims (6)
1. a kind of high-end equipment manufacturing coordinated dispatching method based on mixing difference genetic algorithm, which is characterized in that described
Method includes：
Obtain mixing differential evolution and genetic algorithm parameter；
Q chromosome is defined, each chromosome includes multiple genes, with initialization population vector；
Code modification operation is executed based on the Q chromosome；
To go out to produce and process at workpiece arrangement to each manufacturer according to revised gene, calculate the manufacture of each scheme across
Degree；
The chromosome that fitness value minimum is screened from Q chromosome, obtains current globally optimal solution gbest；
Make great efforts crossover operation using shellfish and updates the population vector；
The population vector is updated using Immigrant strategy, until output globally optimal solution gbest is as optimal height scheme.
2. coordinated dispatching method according to claim 1, which is characterized in that execute coding based on the Q chromosome and repair
It just operates and includes：
301, obtain newly generated Q chromosome in population vectorI=1,2 ..., Q；
302, setup parameter d=1；Parameter d is cycle-index；
303, judge gene in i-th of chromosomeIt is whether true；If so, then enable
304, judgeIt is whether true, if so, then enable
305, set geneRound (x) is indicated to x rounds；
306, judge whether d ＞ n are true, if so, then makeover process is completed, and otherwise, is enabled
D=d+1, return to step 303.
3. coordinated dispatching method according to claim 1, which is characterized in that calculate and each dyed in the Q chromosome
The fitness value of body includes：
401, the workpiece at each manufacturer is rearranged according to process time non-increasing；
402, it is processed successively according to the workpiece at the new each manufacturer of sequence pair.
403, defined variable l, tl, and initialize l=1；
404, initialize tl=0,；
405, the completion date of each workpiece at each manufacturer is assigned to t successivelyl, and by tl+TlIt is assigned to tl；
406, whether Rule of judgment l≤m meets, if satisfied, then return to step 404；Otherwise the complete working hour of each manufacturer is obtained
Between, set t={ t1,…,tl,…,tm, and return to step 407；
407, enable Cmax=max1≤l≤m(tl), wherein and CmaxIt is assigned to fit (Xi), it is denoted as chromosome xiFitness value.
4. coordinated dispatching method according to claim 1, which is characterized in that make great efforts crossover operation using shellfish and update described kind
Before group's vector, the method further includes：
According to the weight of each chromosome described in the fitness value calculation of each chromosome；
Two parent chromosomes of roulette selection, and intersected using described two parent chromosomes and generate a new filial generation dyeing
Body, until generate Q filial generation genome at progeny population vector it is vectorial to substitute the population.
5. coordinated dispatching method according to claim 4, which is characterized in that two parent chromosome packets of roulette selection
It includes：
501, obtain the weight vectors W={ W of population chromosome1,W2,...,Wi,...,Wn}；
502, setting variable sum=0, h=1.
503, enable r=rand (0,1), wherein rand (0,1) indicates the random number between 0 to 1.
504, enable sum=sum+Wh, judge whether sum >=r is true, if so, h is then exported, selection is terminated；Otherwise, h=h+1 is enabled
And return to step 503.
6. coordinated dispatching method according to claim 4, which is characterized in that update the population vector using Immigrant strategy
Including：
601, two chromosomes are randomly selected from updated population vectorWithIt calculates
VhIt is and XhThe identical solution vector of structure；
602, it is based on Xh,VhIt executes shellfish effort crossover operation and obtains Uh；It calculatesIts
In, rand (0,1) indicates the random number between 0 to 1, obtains new chromosome vector
By UhIt is assigned to Xh；
603, h=h+1 is enabled, judges whether h≤Q is true, if so, then return to step 601；Otherwise, step 604 is executed；
604, fitness is calculated, globally optimal solution is updated；And ε n after fitness value in population vector is sorted from small to large
Chromosome is substituted with RANDOM SOLUTION.
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