CN108460463A - High-end equipment flow line production dispatching method based on improved adaptive GA-IAGA - Google Patents
High-end equipment flow line production dispatching method based on improved adaptive GA-IAGA Download PDFInfo
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Abstract
The present invention provides a kind of high-end equipment flow line production dispatching method based on improved adaptive GA-IAGA.This method includes：The first population is generated based on initial parameter；Based on every chromosome in first population, the fitness value of the chromosome is calculated, to determine optimal solution；Iterations are obtained, and compare the iterations and iteration threshold；If the iterations are less than the iteration threshold, first population is iterated using preset algorithm, to determine the optimal solution of every chromosome in the second population and second population；If the iterations are greater than or equal to the iteration threshold, the optimal solution is exported.As it can be seen that studying the flow line production scheduling problem of more machines in per pass technique in the present embodiment, the approximate optimal solution of the problem is acquired, to save resources of production to greatest extent, process time is reduced, improves production efficiency, enhance the core competitiveness of enterprises.
Description
Technical field
The present invention relates to production scheduling technical field more particularly to a kind of high-end equipment flowing water based on improved adaptive GA-IAGA
Line production scheduling method.
Background technology
Fluvial incision is a kind of typical production scheduling problems, be widely present in ship and marine engineering equipment,
In the process of manufacture of high-end equipment manufacturing such as railway transportation equipment, aerospace equipment, intelligent manufacturing equipment.In assembly line
It in scheduling process, needs workpiece to be processed to be assigned on different machines and produces, each workpiece must pass through identical successively
Process, scheduler task is to determine distribution condition of the different workpieces on machine and distributes the workpiece on same machine
Processing sequence.
It is main as production complexity and resource urgent constantly promotion, the optimization aim of scheduling problem also become more diversified
To include minimizing manufacture span time, minimizing processing cost, minimize cost of human resources etc..Due to existing to specific
The limitation of production process sequence, flow line production scheduling problem are more more complicated than general production scheduling problems.In practical application, it is
Raising production efficiency may have more machines in one process, these machines are simultaneously processed workpiece.I.e. same
In one process, be additionally required for workpiece to be processed dispensation machines the case where, cause actual production scheduling than traditional flowing water
Line production scheduling is more complicated.
In addition, proving in the related technology, if including three or more machines in flow line production scheduling process, then the production
Scheduling process belongs to NP-Hard problems, and since the complexity of NP-Hard problems is relatively high, settling mode in the related technology is
Using meta-heuristic algorithm, specific condition is made a concrete analysis of and is studied, to acquire near-optimization within reasonable time
Solution.For example, there is scholar to solve traditional assembly line using particle cluster algorithm, genetic algorithm and the hybrid algorithm of simulated annealing
Production scheduling problems.In the limited displacement fluvial incision of solution intermediate storage space, there is scholar to propose based on LOV
The hybrid differential evolution algorithm of rule, experimental result embody the good convergence effect of algorithm and robustness.However in certain spies
Genetic algorithm there is convergence rates shortcoming that is slow, being easily absorbed in local optimum in fixed problem.
Invention content
For the defects in the prior art, the present invention provides a kind of high-end equipment assembly line based on improved adaptive GA-IAGA
Production scheduling method, 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 flow line production tune based on improved adaptive GA-IAGA
Degree method, the method includes：
The first population is generated based on initial parameter；
Based on every chromosome in first population, the fitness value of the chromosome is calculated, to determine optimal solution；
Iterations are obtained, and compare the iterations and iteration threshold；
If the iterations are less than the iteration threshold, first population is iterated using preset algorithm,
To determine the optimal solution of every chromosome in the second population and second population；
If the iterations are greater than or equal to the iteration threshold, the optimal solution is exported.
Optionally, using preset algorithm to first population be iterated including：
Selection opertor is executed, the first quantity chromosome is selected to form selected population from first population and determines institute
The maximum chromosome of fitness value in selected population is stated, other chromosomes in first population form remaining population；
Crossover operator is executed to the selected population, updates the selected population；
Mutation operator is executed to the selected population, updates the selected population；
To the selected population execution route reconnection operator, the selected population is updated；
It is maximum to gather fitness value in the second quantity chromosome picked out in the remaining population, the selected population
Chromosome and final updating selected population, formed the second population；
The fitness value for calculating every chromosome in second population exports minimum fitness value and its corresponding dye
Colour solid.
Optionally, it is based on every chromosome in first population, calculates the fitness value of the chromosome, to determine most
Excellent solution includes：
The encoder matrix of workpiece to be processed is formed according to coding rule；
Corresponding chromosome is determined according to the encoder matrix；
It is produced according to being assigned to the workpiece to be processed on a wherein machine for the first procedure；
Completion date of each workpiece on the first procedure is calculated according to the processing time matrix of work；
Determine the completion date of every machine in first procedure；
Based on each procedure after second operation work：
The sequence of completion date non-decreasing is corresponded to according to a upper procedure, obtains the job sequence of workpiece；
The corresponding production machine of each workpiece is determined according to the encoder matrix and the job sequence；
The completion date of each machine on current process is calculated according to the processing time matrix of work；
Determine the completion date of each workpiece on current process；
After the processing that all workpiece complete all process steps, the manufacture span time of each workpiece is calculated；
The fitness value of each workpiece homologue is calculated based on the manufacture span time.
Optionally, selection opertor is executed, the first quantity chromosome is selected to form selected population from first population
Including：
The fitness value of every chromosome in first population is calculated, and is F；
Determine the maximum chromosome of the fitness value；
Calculate the select probability of every chromosome；
Calculate the accumulative select probability of every chromosome；
Multiple random decimals are generated, the first quantity chromosome is selected to form selected population.
Optionally, crossover operator is executed to the selected population, updating the selected population includes：
Step 1：Enable probability of crossover pc=0.5, k=1；
Step 2：One is generated for k-th of the chromosome of the current population using selected population as current population
[0,1] random number randk；
Step 3：Judge randk＜ pcIt is whether true, if so, then pick out k-th of chromosome；Otherwise, step 4 is executed；
Step 4：It enables k=k+1, executes step 2, until all chromosomes of the current population are traversed, finally select
Go out K chromosome as cross-species crossPop；If K is odd number, choose a chromosome at random from parent, is added and hands over
It pitches in population crossPop；
Step 5：M=1 is enabled, the random integer r for generating one [1, N × P-1]；
Step 6：R-th of gene position for exchanging m-th and the m+1 chromosome in the cross-species crossPop is right
All genes on side generate two offsprings, and replace m and the m+1 chromosome in current population；
Step 7：M=m+2 is enabled, step 6 is repeated, until all chromosomes are traversed in the cross-species crossPop；
Step 8：Updated current population is returned as new selected population.
Optionally, mutation operator is executed to the selected population, updating the selected population includes：
Step 1：Enable mutation probability pm=0.2, i=1, j=1；
Step 2：The selected population selectPop that crossover operator is generated is as current population；
Step 3：For j-th of gene position of i-th of the chromosome of the current population, the random of one [0,1] is generated
Number randk；
Step 4：Judge randk＜ pmIt is whether true, if so, the random number r of one [1, P] is generated, and random number r is not
Equal to the integer in original gene position, original gene position is replaced with random number r；Otherwise, step 5 is executed；
Step 5：J=j+1 is enabled, judges whether j >=P × N is true, if so, execute step 6；If not, execute step
3；
Step 6：I=i+1 is enabled, judges whether i >=selectsize is true, if so, execute step 7；If not, it holds
Row step 3；
Step 7：Updated current population is returned as new selected population selectPop.
Optionally, to the selected population execution route reconnection operator, updating the selected population includes：
Step 1：It is a to enable initial solutioninit, it is a to be oriented to solutionguide, set AbestFor the optimal solution that is generated in ergodic process
Set, the selected population selectPop that mutation operator is generated is as current population；
Step 2：The fitness value for calculating described every chromosome of current population is selected the maximum chromosome of fitness value and is made
A is solved to be oriented toguide, the big chromosome of fitness value time is selected as initial solution ainit；
Step 3：Initial solution is assigned to abegin, i.e. abegin=ainit；
Step 4：Compare abeginAnd aguideIn each element, record different elements position DP=r | ainit≠
aguide, r=1,2 ..., n }；
Step 5：Enable set AnewSet for the new explanation generated in search process traverses each position in set DP
R, by abeginPosition r on element replace with guiding solution aguideThe new explanation of element on middle corresponding position, generation is put into set
AnewIn；
Step 6：Set of computations AnewIn all chromosomes fitness value, by the maximum chromosome a of fitness valuebestMake
For new abegin, and abestIt is put into set AbestIn；
Step 7：The chromosome of fitness value minimum in current population is replaced with into abest；
Step 8：Step 4- steps 7 are executed, until abeginIn each element be oriented to solution aguideIt is identical；
Step 9：Updated current population is returned as new selected population selectPop.
As shown from the above technical solution, the embodiment of the present invention generates the first population for initial parameter, is then based on described
Every chromosome in first population, calculates the fitness value of the chromosome, to determine optimal solution；Later, iterations are obtained,
And compare the iterations and iteration threshold；Finally, if the iterations are less than the iteration threshold, pre- imputation is utilized
Method is iterated first population, to determine the optimal solution of every chromosome in the second population and second population；
If the iterations are greater than or equal to the iteration threshold, the optimal solution is exported.As it can be seen that studying per pass in the present embodiment
The high-end equipment flow line production scheduling problem of more machines, acquires the approximate optimal solution of the problem in technique, to maximum limit
The saving resources of production of degree reduce process time, improve production efficiency, enhance the core competitiveness of enterprises.
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 the high-end equipment flow line production dispatching method based on improved adaptive GA-IAGA that one embodiment of the invention provides
Method flow block diagram；
Fig. 2 is that the flow line production of uniform machines dispatches schematic diagram；
Fig. 3 is the high-end equipment flow line production dispatching method based on improved adaptive GA-IAGA that one embodiment of the invention provides
Method flow schematic diagram；
Fig. 4 is the high-end equipment flow line production dispatching device based on improved adaptive GA-IAGA that one embodiment of the invention provides
Block diagram.
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.
It should be noted that the processing machine on per procedure is uniform machines, i.e. the speed of machine is identical, and processing is same
Process time is identical when workpiece.But in same procedure, the different work pieces process time is different.
Also, each workpiece can only be processed in synchronization by a uniform machines, and at any time every it is same
Class machine is only processed a workpiece.
In addition, each workpiece does not allow for interrupting once processing, there is unlimited memory space in every procedure.It calculates
When completion date, do not consider mechanical disorder, cutter switching and inter process workpiece the times such as transport, preparation.
In the embodiment of the present invention, the optimization aim of scheduling process be to solve for workpiece in each process the distribution condition of machine and
The job sequence of workpiece minimizes manufacture span time Cmax。
Fig. 1 is the high-end equipment flow line production dispatching method based on improved adaptive GA-IAGA that one embodiment of the invention provides
Method flow block diagram.Fig. 2 is that the flow line production of uniform machines dispatches schematic diagram.Fig. 3 is the base that one embodiment of the invention provides
In the method flow schematic diagram of the high-end equipment flow line production dispatching method of improved adaptive GA-IAGA.Referring to Fig. 1~Fig. 3, the stream
Waterline dispatching method includes：
First, the step of introducing 101, the first population generated based on initial parameter.
Piece count N, operation quantity P, the machine quantity M in each process and workpiece are inputted in the present embodiment in each process
In process time TimeOfPJ.Initialization algorithm parameter, including population scale popsize, iterations runtime, intersection
Probability pcWith mutation probability Pm, enable iterations run=1.
In the present embodiment the high-end equipment flow line production dispatching method based on improved adaptive GA-IAGA be based on genetic algorithm and
Path relinking algorithm is realized.Based on above-mentioned each initial parameter, the first population can be generated according to above-mentioned improved adaptive GA-IAGA.
Secondly, 102 are introduced, based on every chromosome in first population, calculates the fitness value of the chromosome, with
The step of determining optimal solution.
In the present embodiment, initial population Pop is generated at random in conjunction with coding rule, obtain the first data (popsize) item dye
Colour solid calculates the manufacture span time C of every chromosomemax。
According to the manufacture span time C of every chromosomemaxAnd formulaMeter
Calculate the fitness value of the chromosome.
Also, run is updated, even run=run+1.
Again, 103 are introduced, iterations, and the step of comparing the iterations and iteration threshold and 105 are obtained,
If the iterations are greater than or equal to the iteration threshold, the step of exporting the optimal solution.
Iterations run is obtained in the present embodiment, compares iterations run and iteration threshold runtime.
104, if the iterations are less than the iteration threshold, changed to first population using preset algorithm
Generation, to determine the optimal solution of every chromosome in the second population and second population；
If run >=runtime is invalid, it is iterated according to the first population of preset algorithm pair, after obtaining update
The second population and the second population in every chromosome optimal solution.
105, if the iterations are greater than or equal to the iteration threshold, export the optimal solution
If run >=runtime meets end condition, optimal fitness value and corresponding chromosome are exported.
As it can be seen that studying the flow line production scheduling problem of more machines in per pass technique in the present embodiment, the problem is acquired
Approximate optimal solution reduce process time to save resources of production to greatest extent, improve production efficiency, enterprise
Core competitiveness.
In one embodiment of the invention, the calculating step of fitness value, including step 201~step are calculated in step 102
212.It is wherein, each that steps are as follows：
Step 201：According to coding rule, if the collection of workpiece to be processed is combined into J={ J1,...,Jj,...,Jn, Mei Gegong
Part will pass through identical P procedures successively, and there are M platform same machines in every procedure, generate P × N-dimensional encoder matrix AP×N,
As shown in formula (1).Wherein the i-th row indicates that the i-th procedure, jth row indicate workpiece Jj, aijIndicate the workpiece J on the i-th procedurej
Processing machine, aijValue range be [1, M].
Step 202：According to above-mentioned coding rule, each encoder matrix can determine item chromosome.Chromosome is according to volume
The row sequence of code matrix is arranged in order, and length is P × N, as shown in formula (2).
[a11,a12,…,a1j,…,a1N,aP1,aP2,…,aPj,…,aPN]。 (2)
Step 203：Workpiece enters the first procedure, according to encoder matrix AP×NAssign workpiece JjTo the of the first procedure
a1jIt is produced on platform machine.
Step 204：According to the processing time matrix TimeOfPJ of workpiece, it is complete on the first procedure to calculate each workpiece
TJ between working hourj.When multiple workpiece are assigned in same machines, processed successively according to workpiece serial number increasing.
Step 205：Determine the completion date TPM of the kth platform machine in the first procedure1k, as process on this machine
The last one workpiece completion date.
I=2 is enabled, each procedure based on second operation work includes：
Step 206：Workpiece enters the i-th procedure, all workpiece is corresponded to according to a upper procedure completion date of workpiece
Non-decreasing sorts, and obtains job sequence Π={ π of a workpiece1,π2,…,πj,…,πn, πjRepresent workpiece JπjIn processing sequence
J-th of position is come in row, and is processed successively according to this workpiece sequence.
Step 207：According to the encoder matrix A of workpieceP×N, judge job sequence Π={ π1,π2,…,πj,…,πnEach
Workpiece produces on which platform machine.
Step 208：According to the processing time matrix TimeOfPJ of workpiece, each machine in the i-th procedure is calculated according to formula (3)
The completion date TPM of deviceik。
Wherein max { x, y } indicates to take the higher value in x and y
Step 209：Workpiece in i-th procedureCompletion date beIt enables
Step 210：I=i+1 is enabled, step 206 is executed, until workpiece completes the processing of all process steps.
Step 211：(4) calculate the manufacture span time C of workpiece according to the following formulamax。
Step 212：(5) calculate the fitness value of chromosome according to the following formula.
In one embodiment of the invention, the first population of preset algorithm pair is iterated, wherein each, steps are as follows：
Step 301, selection opertor is executed, the first quantity chromosome is selected to form selected population from first population
And determining the maximum chromosome of fitness value in the selected population, other chromosomes in first population form remaining kind
Group, specifically includes：
3011, the fitness value of all chromosomes in the first population is calculated, and be F.
3012, find out the maximum chromosome a of fitness value.
3013, the select probability p of every chromosome is calculated according to the following formulai。
3014, the accumulative select probability q of every chromosome is calculated according to the following formulai。
3015, the random decimal Rand for generating one [0,1], if Rand≤q1, then item chromosome selected.If
qk-1≤Rand≤qk, then kth chromosome selected.
3016, repetition step 3015 is selectsize-1 times total, selects selectsize-1 chromosome.
3017, the chromosome in step 3016 and step 3012 is merged into kind of the composition with selectsize chromosome
Group selectPop.
Step 302, crossover operator is executed to the selected population, updates the selected population, specifically includes：
3021, enable probability of crossover pc=0.5, k=1.
3022, the selected population selectPop that selection operation is selected is as current population.
3023, for k-th of the chromosome of current population, generate the random number rand of one [0,1]k.Judge randk＜
pcIt is whether true, if so, then pick out k-th of chromosome.Otherwise, step 3024 is executed.
3024, k=k+1 is enabled, step 3022 is executed, until all chromosomes of current population are traversed.It is final to select K
Chromosome is as cross-species crossPop.If K is odd number, choose a chromosome at random from parent, is added
In crossPop.
3025, m=1 is enabled, the random integer r for generating one [1, N × P-1].
3026, exchange all genes on the right of r-th of gene position of the m and the m+1 chromosome in crossPop, production
Raw two offsprings, and replace m and the m+1 chromosome in current population.
3027, m=m+2 is enabled, step 3026 is repeated, until all chromosomes are traversed in crossPop.
3028, updated current population is returned as new selectPop.
Step 303, mutation operator is executed to the selected population, updates the selected population, specifically includes：
3031, enable mutation probability pm=0.2, i=1, j=1.
3032, select selectPop as current population using what crossover operator generated.
3033, for j-th of gene position of i-th of the chromosome of current population, generate the random number of one [0,1]
randk。
3034, judge randk＜ pmIt is whether true, if so, the random number r of one [1, P] is generated, and random number r is differed
Integer in original gene position replaces original gene position with random number r.Otherwise, step 3035 is executed.
3035, j=j+1 is enabled, judges whether j >=P × N is true, if so, execute step 3036；If not, execute step
Rapid 3033.
3036, i=i+1 is enabled, judges whether i >=selectsize is true, if so, execute step 3037；If not,
Execute step 3033.
3037, updated current population is returned as new selected population selectPop.
Step 304, to the selected population execution route reconnection operator, the selected population is updated, is specifically included：
3041, it is a to enable initial solutioninit, it is a to be oriented to solutionguide, set AbestCollection for the optimal solution generated in ergodic process
It closes, the selectPop that mutation operator is generated is as current population.
3042, the fitness value of all chromosomes of current population is calculated, the maximum chromosome conduct of fitness value is selected and leads
To solution aguide, the big chromosome of fitness value time is selected as initial solution ainit。
3043, initial solution is assigned to abegin, i.e. abegin=ainit。
3044, compare abeginAnd aguideIn each element, record different elements position DP=r | ainit≠aguide,
R=1,2 ..., n }.
3045, enable set AnewSet for the new explanation generated in search process traverses each position r in set DP,
By abeginPosition r on element replace with guiding solution aguideThe new explanation of element on middle corresponding position, generation is put into set
AnewIn.
3046, set of computations AnewIn all chromosomes fitness value, by the maximum chromosome a of fitness valuebestAs
New abegin, and abestIt is put into set AbestIn.
3047, the chromosome of fitness value minimum in current population is replaced with into abest。
3048, step 3044-3047 is executed, until abeginIn each element be oriented to solution aguideIt is identical.
3049, updated current population is returned as new selected population selectPop.
Step 305, gather and adapted in the second quantity chromosome picked out in the remaining population, the selected population
The selected population of angle value maximum chromosome and final updating forms the second population.
Popsize- (1+selectsize) chromosome, choosing are picked out from the remaining population restPop in step 304
The maximum chromosome BestChro and selected population selectPop of fitness in population is selected into row set, composition includes popsize
Second population newPop of chromosome.
Step 306, the fitness value for calculating every chromosome in second population, export minimum fitness value and its
Corresponding chromosome.
The C of all chromosomes in second crowd of newPop is calculated in the present embodimentmax, export minimum CmaxIt is worth and its corresponding
Chromosome.
As it can be seen that being proposed for the high-end equipment flow line production scheduling problem based on uniform machines in the embodiment of the present invention, lead to
Revised genetic algorithum is crossed, first encodes workpiece to be processed, determines machine assignment situation of the workpiece in each process, then
According to the processing sequence for arriving first the determining workpiece of the strategy first processed, to calculate the manufacture span time of workpiece.
In the embodiment of the present invention, initial population is randomly generated, selection, intersection, variation and path weight are executed to initial population
Even operator, the update completed to population by constantly iteration finally acquire optimal solution to optimize solution space.I.e. improved something lost
Propagation algorithm solves the high-end equipment flow line production scheduling problem based on uniform machines, has saved the resources of production of enterprise, improves
The production efficiency and customer satisfaction of enterprise, improves the management level of enterprise, and the optimal solution of the algorithm have it is preferable
Convergence rate and stability.
In the embodiment of the present invention, uniform machines are combined with fluvial incision, i.e., is added more in each process
Uniform machines can not only improve the complexity of problem, can also make application scenarios more closing to reality situation.
In the embodiment of the present invention, by the way of matrix coder, the solution space of more intuitive efficiently problem of representation.
In the embodiment of the present invention, using path relinking algorithm and elite retention strategy improved adaptive GA-IAGA, make it in iteration
Generate more advantage chromosomes in the process, while increasing population diversity, be effectively improved genetic algorithm be easily absorbed in it is precocious,
The defects of local optimum.
Fig. 4 is a kind of high-end equipment flow line production scheduling based on improved adaptive GA-IAGA that one embodiment of the invention provides
Device, as shown in figure 4, described device includes：
First population generation module 401, for generating the first population based on initial parameter；
Optimal solution determining module 402, for based on every chromosome in first population, calculating the suitable of the chromosome
Angle value is answered, to determine optimal solution；
Contrast module 403 for obtaining iterations, and compares the iterations and iteration threshold；
Iteration module 404, for when the iterations are less than the iteration threshold, using preset algorithm to described the
One population is iterated, to determine the optimal solution of every chromosome in the second population and second population；
Output module 405, for when the iterations are more than or equal to the iteration threshold, output to be described optimal
Solution.
It should be noted that the high-end equipment flow line production tune provided in an embodiment of the present invention based on improved adaptive GA-IAGA
It is one-to-one relationship to spend device with the above method, and the implementation detail of the above method is equally applicable to above-mentioned apparatus, the present invention
Embodiment is no longer described in detail above 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 (7)
1. a kind of high-end equipment flow line production dispatching method based on improved adaptive GA-IAGA, which is characterized in that the method packet
It includes：
The first population is generated based on initial parameter；
Based on every chromosome in first population, the fitness value of the chromosome is calculated, to determine optimal solution；
Iterations are obtained, and compare the iterations and iteration threshold；
If the iterations are less than the iteration threshold, first population is iterated using preset algorithm, with true
The optimal solution of every chromosome in fixed second population and second population；
If the iterations are greater than or equal to the iteration threshold, the optimal solution is exported.
2. high-end equipment flow line production dispatching method according to claim 1, which is characterized in that using preset algorithm to described
First population be iterated including：
Selection opertor is executed, the first quantity chromosome is selected to form selected population from first population and determines the choosing
The maximum chromosome of fitness value in population is selected, other chromosomes in first population form remaining population；
Crossover operator is executed to the selected population, updates the selected population；
Mutation operator is executed to the selected population, updates the selected population；
To the selected population execution route reconnection operator, the selected population is updated；
Gather the maximum dye of fitness value in the second quantity chromosome picked out in the remaining population, the selected population
The selected population of colour solid and final updating forms the second population；
The fitness value for calculating every chromosome in second population exports minimum fitness value and its corresponding dyeing
Body.
3. high-end equipment flow line production dispatching method according to claim 2, which is characterized in that based in first population
Every chromosome calculates the fitness value of the chromosome, to determine that optimal solution includes：
The encoder matrix of workpiece to be processed is formed according to coding rule；
Corresponding chromosome is determined according to the encoder matrix；
It is produced according to being assigned to the workpiece to be processed on a wherein machine for the first procedure；
Completion date of each workpiece on the first procedure is calculated according to the processing time matrix of work；
Determine the completion date of every machine in first procedure；
Based on each procedure after second operation work：
The sequence of completion date non-decreasing is corresponded to according to a upper procedure, obtains the job sequence of workpiece；
The corresponding production machine of each workpiece is determined according to the encoder matrix and the job sequence；
The completion date of each machine on current process is calculated according to the processing time matrix of work；
Determine the completion date of each workpiece on current process；
After the processing that all workpiece complete all process steps, the manufacture span time of each workpiece is calculated；
The fitness value of each workpiece homologue is calculated based on the manufacture span time.
4. high-end equipment flow line production dispatching method according to claim 2, which is characterized in that selection opertor is executed, from institute
State selected in the first population the first quantity chromosome formed selected population include：
The fitness value of every chromosome in first population is calculated, and is F；
Determine the maximum chromosome of the fitness value；
Calculate the select probability of every chromosome；
Calculate the accumulative select probability of every chromosome；
Multiple random decimals are generated, the first quantity chromosome is selected to form selected population.
5. high-end equipment flow line production dispatching method according to claim 2, which is characterized in that executed to the selected population
Crossover operator, updating the selected population includes：
Step 1：Enable probability of crossover pc=0.5, k=1；
Step 2：K-th of the chromosome of the current population is generated one [0,1] using selected population as current population
Random number randk；
Step 3：Judge randk＜ pcIt is whether true, if so, then pick out k-th of chromosome；Otherwise, step 4 is executed；
Step 4：It enables k=k+1, executes step 2, until all chromosomes of the current population are traversed, finally pick out K
Chromosome is as cross-species crossPop；If K is odd number, choose a chromosome at random from parent, is added and intersects kind
In group crossPop；
Step 5：M=1 is enabled, the random integer r for generating one [1, N × P-1]；
Step 6：On the right of r-th of gene position for exchanging m-th and the m+1 chromosome in the cross-species crossPop
All genes generate two offsprings, and replace m and the m+1 chromosome in current population；
Step 7：M=m+2 is enabled, step 6 is repeated, until all chromosomes are traversed in the cross-species crossPop；
Step 8：Updated current population is returned as new selected population.
6. high-end equipment flow line production dispatching method according to claim 2, which is characterized in that executed to the selected population
Mutation operator, updating the selected population includes：
Step 1：Enable mutation probability pm=0.2, i=1, j=1；
Step 2：The selected population selectPop that crossover operator is generated is as current population；
Step 3：For j-th of gene position of i-th of the chromosome of the current population, the random number of one [0,1] is generated
randk；
Step 4：Judge randk＜ pmIt is whether true, if so, the random number r of one [1, P] is generated, and random number r is not equal to
The originally integer in gene position replaces original gene position with random number r；Otherwise, step 5 is executed；
Step 5：J=j+1 is enabled, judges whether j >=P × N is true, if so, execute step 6；If not, execute step 3；
Step 6：I=i+1 is enabled, judges whether i >=selectsize is true, if so, execute step 7；If not, execute step
Rapid 3；
Step 7：Updated current population is returned as new selected population selectPop.
7. high-end equipment flow line production dispatching method according to claim 2, which is characterized in that executed to the selected population
Path relinking operator, updating the selected population includes：
Step 1：It is a to enable initial solutioninit, it is a to be oriented to solutionguide, set AbestSet for the optimal solution generated in ergodic process,
The selected population selectPop that mutation operator is generated is as current population；
Step 2：The fitness value for calculating described every chromosome of current population is selected the maximum chromosome conduct of fitness value and is led
To solution aguide, the big chromosome of fitness value time is selected as initial solution ainit；
Step 3：Initial solution is assigned to abegin, i.e. abegin=ainit；
Step 4：Compare abeginAnd aguideIn each element, record different elements position DP=r | ainit≠aguide, r=
1,2,…,n}；
Step 5：Enable set AnewSet for the new explanation generated in search process traverses each position r in set DP, will
abeginPosition r on element replace with guiding solution aguideThe new explanation of element on middle corresponding position, generation is put into set Anew
In；
Step 6：Set of computations AnewIn all chromosomes fitness value, by the maximum chromosome a of fitness valuebestAs new
abegin, and abestIt is put into set AbestIn；
Step 7：The chromosome of fitness value minimum in current population is replaced with into abest；
Step 8：Step 4- steps 7 are executed, until abeginIn each element be oriented to solution aguideIt is identical；
Step 9：Updated current population is returned as new selected population selectPop.
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