CN107862411A - A kind of extensive flexible job shop scheduling optimization method - Google Patents
A kind of extensive flexible job shop scheduling optimization method Download PDFInfo
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Abstract
Extensive flexible job shop scheduling method, large-scale production task is recombinated to reduce scale, then utilizes adaptive impovement genetic algorithm solving-optimizing.Comprise the following steps that：(1) first by processing technology is similar, workpiece size is in same scope and blank material identical workpiece carries out cluster batching, so as to reduce problem solving scale；(2) algorithm initial parameter is set, using three layers of gene code technology, OBX interleaved modes and certain Mutation Strategy, selects to intersect length with reference to emulation experiment, and solve using adaptive impovement genetic algorithm optimization.This method can reduce problem solving scale, improve solving speed；And reduce workpiece completion date and delay the phase.
Description
Technical Field
The invention relates to the technical field of intelligent optimization algorithm of discrete combination problem. In particular to a scheduling optimization method for a large-scale flexible job shop.
Background
The workshop scheduling problem of not more than 20X 50 (machine tool X workpiece) is a medium and small scale scheduling problem, and the large scale workshop scheduling problem has the following situations [ LIANG Xu, WA NG Jia, H UANG Ming.New coding method for large production scheduling scheme [ J ]. Computer Integrated Manufacturing systems, 2008,10 (14): 1974-1982 ]: (1) when the number of workpieces J is greater than 50 and the number of machines M is greater than 20; (2) when J is less than or equal to 50,M > < 20,JXM > 1000; (3) when J is greater than 50, M is less than or equal to 20, and J × M is greater than 1000. For more than 60 years, the production scheduling and scheduling problem of the job shop belongs to an NP problem, and a plurality of scholars propose optimization methods with good solution effect for solving the problem, but the solution scale is usually small and medium. Most optimization algorithms cannot be directly used for solving large-scale scheduling problems due to the solving efficiency and quality problems. The current methods for solving the problem of large-scale scheduling are as follows: an intelligent optimization algorithm, a Lagrange relaxation decomposition method and a problem decomposition-based method. Various decomposition methods and intelligent optimization algorithms are first applied to the problem of plant scheduling, which is increasing in scale. With the increase of the number of workpieces and the number of machines, the solving space of the JSP problem becomes complex, and the traditional solving algorithm cannot meet the requirements on the quality of the solution and the solving time.
Disclosure of Invention
The invention aims to provide a large-scale flexible job shop scheduling method based on workpiece batch, which has high solving speed, reduces the completion time of workpieces and the drag delay of the workpieces, and aims to solve the problems in the prior art.
The purpose of the invention is realized as follows: the invention adopts a clustered workpiece batching method for production tasks of a flexible job shop, recombines large-scale production tasks to reduce scale, and then solves an optimized scheduling method by utilizing a self-adaptive improved genetic algorithm. Firstly, clustering and batching workpieces which are similar in machining process, have the same workpiece size and are made of the same blank material to reduce problem solving scale, randomly forming different batches, enabling each workpiece to have respective delivery date, and scheduling according to the earliest delivery date in the batches after batching.
Secondly, establishing a mathematical model of the scheduling problem of the large-scale flexible job shop
The large-scale flexible job shop scheduling problem in question can be described in detail as, at M devices (M = { M) } k |M 1 ,M 2 ...M m K =1,2.. M }) on n workpieces (J = { J }) l |J 1 ,J 2 ,...J n L =1,2,. N }), each workpiece contains N predetermined machining sequence steps, each of which can be machined on multiple machines. Forming R type workpieces R = { R after batching i |R 1 ,R 2 ,...R r I =1,2.. R }, and the processing time of the jth procedure of the ith workpiece on the equipment k is T ijk ，T pi Indicating the finishing time of the last process of the p-th batch of the i-th workpiece, D s Indicating lead time of the workpiece s, D pi Indicating the lead time of the p-th batch of the i-th workpiece. The scheduling objective is to minimize the maximum completion time and the total stall.
An objective function: minf = a 1 f 1 '+α 2 f 2 ' (1)
Wherein: f. of 1 ＝min(maxT pi )p＝1,2...B i ；i＝1,2......r (2)
Formula (2) f 1 Representing a minimum maximum completion time; formula (3) f 2 Indicates the total delayIs small.
Constraint conditions are as follows:
the formula (5) shows that the processing time of the jth procedure of the pth batch of the ith workpiece is equal to the sum of the processing times of all the workpieces in the batch
S ip(j-1)k +T ip(j-1)k +W ip(j-1)k ≤S ipjk' p＝1,2...B i ；i＝1,2...r；j＝1,2...N；k,k'＝1,2...m (6)
Formula (6) the start time of the next process of the same workpiece in the same batch is more than or equal to the completion time of the previous process
S ipjk +T ipjk +W ipjk ≤S i'p'j'k p,p'＝1,2...B i ；i,i'＝1,2...r；j,j'＝1,2...N；k＝1,2...m (7)
The formula (7) occupies the same machine, and the start time of the next batch of workpieces is more than or equal to the finish time of the previous batch of workpieces
The formula (8) indicates that the total number of all the batches of workpieces in each type of workpiece is equal to the total number of all the workpieces
D pi ＝minD s i＝1,2...r；s＝1,2......C ip ,p＝1,2...B i (9)
Expression (9) indicates that the lead time of the p-th lot of the i-th workpiece is equal to the earliest lead time in the lot
The specific symbolic meanings are shown in Table 2.
TABLE 1 meanings of symbols
Third, the three-layer coding is adopted to code the gene in the genetic algorithm program
The patent adopts a three-layer coding mode for adapting to the problem of large-scale flexible job scheduling. Assuming that there are two types of workpieces, each having two processes, as shown in fig. 2, the first layer represents the number of batches formed after the workpiece is batched, and integer coding is used to represent that the type 1 workpiece is divided into 2 batches. The second layer represents the processing sequence of the workpiece steps, and adopts a coding scheme based on workpieces, wherein 101 represents the first batch of the type 1 workpieces, 102 represents the second batch of the type 1 workpieces, 101 appears in the process gene for the first time to represent the first step, and so on, and the processing sequence represented by the process gene is 101 → 201 → 102 → 101 → 201 → 102. The third level represents the machine selected for processing by the workpiece process, and the first process representing the workpiece 101 selects the first machine 2 from the set of selectable machines using integer coding.
Fourth, initialization of genetic algorithm parameters
According to the principle and basis of initial parameter selection, the population size is set to be 40, the cross probability is 0.1, the mutation probability is 0.04, and the maximum genetic generation number is 300.
Fifthly, generating an initial population
According to the coding mode, the initial population is generated in a random mode, and in order to keep the legality of the initial population, the constraint is added when the initial population is generated, namely the number of times of the constraint workpiece numbers appearing in the process gene section is equal to the number of workpiece processes.
Sixthly, calculating the population fitness value
And (3) calculating the objective function value of each individual by using the formulas (1) to (3), distributing the fitness value, and selecting the cross variation operation with higher fitness to enter the next generation.
Seventh, selection
The selection operator uses a roulette method, which is widely used for selection operations because of its convenience. In this method, the probability that an individual is selected is related to its own fitness value, with the greater the probability that the fitness value is retained.
Eighth, selecting OBX crossover operator to perform genetic algorithm crossover
After the population is crossed and shuffled, two individuals are randomly selected, as shown in fig. 4, and the process gene crossing is intercepted, in the patent, an improved crossing operator Based on Order-Based cross (OBX) is adopted, the crossing step is shown in fig. 4, as shown in fig. 4a, genes to be crossed are randomly selected and selected in a parent generation P1, a crossed gene pool Pos (201,101,102) is formed, but as the genes of a Chrom2 batch are 1 by using a first type workpiece batch, 102 workpieces do not exist in the process gene of Chrom2, 102 is deleted from the crossed gene pool, and the genes finally used for crossing have Pos (201, 101). If the gene 201 is found in the parent P2 and there are many processes, so there are many 201 in P2, then one is randomly selected as shown in FIG. 4b, then the selected gene in P2 is deleted, the other genes in the gene pool are crossed, and finally the genes which are vacant in P2 are sequentially filled with the selected gene by P1 to form the offspring C2 as shown in FIG. 4C. And the alternative genes and the genes which cannot be used for crossing are reserved in P1, and then the vacancy in P1 is sequentially filled by using the genes remained in P2 and removing individuals 202 (second workpieces of a second type of workpieces) which are not in P1 to form a progeny C1 as shown in FIG. 4C.
The chromosome crossing length can influence the performance of the algorithm, and the patent researches the relation between the chromosome crossing length and the solving precision and the calculating speed of the algorithm. And selecting 10 to 100 percent of the chromosome length for crossing, respectively and repeatedly carrying out simulation operation to calculate the average value of the chromosome length, and calculating the sum of weighted values to find that the crossing length is optimal when the crossing length accounts for 10 percent of the chromosome length. In order to find out a better target value, the patent selects 10 to 20 percent of the chromosome length to carry out crossing, and repeatedly simulates and calculates the average value of the chromosome length, and then calculates a weighted value, and finds that selecting 12 percent of the chromosome length to carry out crossing can obtain a more accurate solution and improve the operation speed, so that 12 percent of the crossing length is selected.
Ninth, selecting mutation strategy to legalize the mutated gene
The variation is divided into batch variation and machine variation, wherein the batch variation is shown in fig. 5, and the process genes and machine genes need to be decoded again to form new chromosomes after each batch variation to ensure the validity of the chromosomes, if the batch of the first type of workpieces is decreased by 1, the process genes are correspondingly decreased by one workpiece 102, and if the batch of the second type of workpieces is increased by 1, the process genes are increased by one workpiece 202, and both machine genes select corresponding machines according to the process genes. Machine variation is shown in FIG. 6, e.g., parent (1,9) variation is 2, indicating selection of M 11 Machine No. 3 of (2,3).
Tenth, adopting adaptive genetic algorithm to carry out optimization solution
The sine self-adaption is a self-adaption method in which the crossing rate and the specific variation rate change according to the average individual adaptability value and the maximum adaptability value change according to a linear function curve. The method has the advantages that a high convergence speed can be obtained by selecting a high crossing rate and a high variation rate in the initial stage of the optimization process, the diversity of a population is kept, after multiple iterations, in order to avoid damage to an optimal solution, the low crossing rate and the low variation rate are selected for carrying out detailed search, formulas used in the method are detailed in formulas (10) and (11) [ Yang Jianbin, sun Shudong, niuniu, wang Meng. Experiments prove that the convergence speed and the convergence precision of the algorithm can be improved by the sine self-adaptive genetic algorithm, and the globality and the precision of algorithm searching can be ensured by the method.
The cross probability:
the mutation probability:
in the formula f max The maximum fitness value in the current population is obtained; f. of avg The average fitness value in the current population is obtained; f' is the larger fitness value of the two individuals to be crossed; f is the fitness value of the individual to be mutated; p is a radical of c1 、 p m1 The value is taken in the range of (0,1) as the coefficient.
Eleventh, decoding
Decoding is the inverse operation of coding, the procedure processing sequence of each batch of various workpieces is obtained by the procedure gene section of the chromosome, the corresponding processing machine is obtained by the machine gene section, and the starting time and the ending time of each procedure are calculated according to the formulas (4) to (9).
The patent provides a large-scale job shop scheduling method based on workpiece batching, and the method is characterized in that parts which are similar in machining process and have the same pipe diameter size in the same range and same in blank material are clustered and batched to form different batches randomly, each workpiece has a respective delivery date, and scheduling is carried out according to the earliest delivery date in the batches after batching. And then optimizing the batch number and the batch processing sequence of the batch by using a OBX-based cross adaptive genetic algorithm. The method can reduce the problem solving scale and improve the solving speed; and reduces workpiece completion time and drag delays.
The beneficial effects of the invention are:
(1) Reduce the problem solving scale and improve the solving speed
Workpieces with similar characteristics are subjected to batch processing according to a batch principle, and then batch times are optimized by using a self-adaptive genetic algorithm based on a OBX crossing mode, so that the problem scale is reduced, and the solving speed is greatly increased.
(2) Reducing the completion time and delay of workpieces
After workpieces of the same type are batched, the processing preparation time of the workpieces can be shortened, the workpiece batching times are optimized, and the workpiece delay can be reduced.
The method sets algorithm initial parameters, adopts a three-layer gene coding technology, a OBX crossing mode and a certain variation strategy, combines a simulation experiment to select the crossing length, and utilizes a self-adaptive improved genetic algorithm to optimize and solve. The invention provides a scheduling method for batching according to the production characteristic similarity of workpieces and solving a large-scale flexible job shop by using an improved genetic algorithm, which can reduce the problem solving scale and improve the solving speed; and reduces workpiece finishing time and drag delays. The method has important significance and obvious practical engineering application value for solving the large-scale workshop operation scheduling.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 chromosome coding map.
FIG. 3 parent chromosomal sequence.
FIG. 4 is a cross-flow diagram based on OBX.
FIG. 5 is a batch variation flow chart.
FIG. 6 is a machine variation flow chart.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
Examples 1
Example 1 the JSP problem of 230 x 18 scale was employed, i.e. 230 workpieces were machined on 18 machines. Each workpiece comprises four working procedures, and the sequence of the working procedures is Qi1, qi2, qi3 and Qi4. Wherein Qi1, qi2 and Qi3 are processing operations and can only be selected to be operated on a processing machine; qi4 is a test operation and can only be selected to operate on a test machine. And the distribution of machines is such that machines 1-15 are processing machines and machines 16-18 are inspection machines.
EXAMPLES example 2
Example 2 the JSP problem on a 460 x 18 scale was adopted, i.e. 460 workpieces were machined at 18, the machining machines and inspection machines being distributed as in example 1.
Detailed description of the preferred embodiment
First, similar lots based on workpiece characteristics
The method includes the steps of firstly, clustering and batching parts which are similar in machining process, have the same workpiece size and are made of the same blank material, randomly forming different batches, enabling each workpiece to have a respective delivery time, and scheduling according to the earliest delivery time in the batches after batching. For example, there are 10 pieces of pipe work, the main characteristics of which are shown in table 1. The pipe diameter has two size ranges of 6mm-12mm and 14mm-38mm, the main process of the workpiece is replaced by A, B, C, D, E, F, G, wherein E and F can be processed by the same machine, under the condition of the same process sequence, the process can be considered to be similar under the condition that the process sequence is the same or different processes can adopt the same machine, and for example, B → C → F → G and B → C → E → G can be considered to be similar in the process flow. The batch results obtained according to the above batch method were: a first group (1, 5, 9) with a lead time of 2017/07/1; a second group (2, 10) with a lead time of 2017/07/1; and the third group (3, 4, 6, 7, 8) has a lead time of 2017/07/2.
TABLE 1 workpiece Property Table
Second, construct mathematical model
The optimized mathematical model of example 1 was constructed according to equations (1) - (9):
an objective function: min f = a 1 f 1 '+α 2 f 2 ' (1)
Wherein: f. of 1 ＝min(maxT pi )i＝1,2,.....48；p＝1,2......B i (2)
Constraint equation:
D pi ＝minD s i＝1,2,.....r；s＝1,2......C ip (9)
third, the improved genetic algorithm of OBX crossover operator is adopted for optimization
The operation modes of other examples are consistent, after a mathematical model is obtained, according to the principle and basis of initial parameter selection, the population scale is set to be 40, the cross probability is 0.1, the mutation probability is 0.04, and the maximum genetic algebra is 300 generations.
And (3) grouping the workpieces according to the rules by adopting MATLAB programming, randomly generating an initial population, selecting excellent individuals according to a certain probability after decoding, entering a next generation cross and variation updating population, and circularly iterating until a maximum iteration algebra is reached, and outputting the optimal completion time and a corresponding scheduling solution.
The two examples with different scales respectively use the non-batch adaptive genetic algorithm and the batch adaptive genetic algorithm of the patent to respectively calculate for ten times so as to compare relevant data, thereby illustrating the effectiveness of the optimization method in the invention. The optimal solution and related data of the JSP problem of each scale after calculation are shown in Table 4. According to different experimental data results, the performance is compared, and the performance indexes such as minimum completion time/delay, operation efficiency and the like are greatly improved according to the following data.
Minimum completion time and search performance
Table 4 data table of experimental results
As can be seen from Table 4, the lingering periods of two different large-scale JSP problems after batch scheduling are both 0, while the non-batch scheduling methods have lingering in different degrees; the maximum completion time is reduced by at least 21 percent and is reduced by at most 25 percent; the operation efficiency is improved by at least 61 percent and is improved by 66 percent to the maximum extent.
Claims (5)
1. A scheduling optimization method for a large-scale flexible job shop is characterized by comprising the following steps: establishing a mathematical model of the flexible job shop scheduling problem, degrading the problem scale based on a workpiece batch scheduling method, and performing optimization solution by using an adaptive genetic algorithm to form a set of scheduling optimization method for solving the large-scale flexible job shop, wherein the method comprises the following steps:
first, similar lots based on workpiece characteristics
The method comprises the steps of firstly, clustering and batching parts which are similar in machining process, have the same workpiece size and are made of the same blank material, randomly forming different batches, enabling each workpiece to have respective delivery date, and scheduling according to the earliest delivery date in the batches after batching;
secondly, establishing a mathematical model of the scheduling problem of the large-scale flexible job shop
The large-scale flexible job shop scheduling problem can be described in detail as, at M devices (M = { M) } k |M 1 ,M 2 ...M m K =1,2.. M }) on n workpieces (J = { J }) l |J 1 ,J 2 ,...J n L =1,2,. N }), each workpiece including N predetermined machining sequence steps, each step being capable of machining on a plurality of machines; forming R-type workpieces R = { R after batching i |R 1 ,R 2 ,...R r I =1,2.. R }, and the j-th process of the i-th workpiece has a processing time T on the equipment k ijk ，T pi Indicating the finishing time of the last process of the p-th batch of the i-th workpiece, D s Indicating lead time of the workpiece s, D pi Indicating the lead time of the p batch of the ith workpiece; the scheduling objective is to minimize the maximum completion time and the total delay;
an objective function: min f = a 1 f 1 '+α 2 f 2 ' (1)
Wherein: f. of 1 ＝min(maxT pi )p＝1,2...B i ；i＝1,2......r (2)
Formula (2) f 1 Representing a minimum maximum completion time; formula (3) f 2 Indicating that the total lag period is minimal;
constraint conditions are as follows:
equation (5) shows that the processing time of the jth process of the pth batch of the ith workpiece on the equipment k is equal to the sum of the processing times of all the workpieces in the batch
S ip(j-1)k +T ip(j-1)k +W ip(j-1)k ≤S ipjk' p＝1,2...B i ；i＝1,2...r；j＝1,2...N；k,k'＝1,2...m (6)
The formula (6) represents the start time S of the next process for the same workpiece in the same batch ipjk′ Completion time of preceding process or more
S ipjk +T ipjk +W ipjk ≤S i'p'j'k p,p'＝1,2...B i ；i,i'＝1,2...r；j,j'＝1,2...N；k＝1,2...m (7)
The formula (7) occupies the same machine and the start time S of the next batch of workpieces i′p′j′k The finishing time of the workpieces of the previous batch is more than or equal to
The expression (8) indicates that the total number of all the batches of workpieces in each type of workpiece is equal to the total number of all the workpieces
D pi ＝minD s i＝1,2...r；s＝1,2......C ip ；p＝1,2...B (9)
The formula (9) indicates that the delivery time of the p batch of the ith workpiece is equal to the specific symbol meaning of the earliest delivery time in the batch as follows:
α 1 representing function f 1 The weight of (c);
α 2 representing function f 2 The weight of (c);
f 1 ' means f 1 Normalizing the processed function value;
f 2 ' means f 2 Normalizing the processed function value;
M ij a processing equipment set which indicates that the ith workpiece can be used in the jth procedure;
S ipjk indicating the starting time of the jth process of the jth batch of the ith workpiece on the equipment k;
W ipjk the adjustment time of the jth process of the ith batch of workpieces on the equipment k is shown;
X ipjk representing a decision variable, and taking a value of 1 when the jth procedure of the pth batch of i-type workpieces is processed on the equipment k, or taking a value of 0;
t sjk showing a workpiece J s The j-th procedure in the device k;
T ipjk the processing time of the jth process of the jth batch of the i-type workpieces on the equipment k is shown;
B i indicating the number of batches formed by the ith type of workpiece batches;
C ip representing the batch number of the p-th batch of the ith type of workpieces;
third, the three-layer coding is adopted to carry out gene coding in genetic algorithm program
Adopting a three-layer coding mode, wherein the first layer represents the number of batches formed after the workpieces are batched, the second layer represents the processing sequence of the workpiece procedures, and the third layer represents the machine selected by the workpiece procedures;
fourth, genetic algorithm parameter initialization
According to the principle and basis of initial parameter selection, setting the population scale to be 40, the cross probability to be 0.1, the mutation probability to be 0.04 and the maximum genetic algebra to be 300 generations;
fifthly, generating an initial population
Generating an initial population in a random mode according to a coding mode, and adding constraint when generating the initial population in order to keep the legality of the initial population, wherein the times of constraining the occurrence of the workpiece numbers in a procedure gene segment are equal to the number of workpiece procedures;
sixthly, calculating the population fitness value
Calculating the objective function value of each individual by using formulas (1) - (3), distributing fitness value, and selecting the cross variation operation of the next generation with higher fitness;
seventh, selection
The selection operator adopts a roulette method, in the method, the probability that an individual is selected is related to the fitness value of the individual, and the higher the fitness value is, the higher the probability of being reserved is;
eighth, selecting OBX crossover operator to perform genetic algorithm crossover
Randomly selecting two individuals after the population is subjected to cross shuffling, intercepting the process gene cross, and performing cross by adopting an improved cross operator Based on an Order-Based crossbar (OBX); and the relationship between the chromosome crossing length and the algorithm solving precision and the operation speed is explored through simulation, and a proper chromosome crossing length is selected;
ninth, selecting mutation strategy to legalize the mutated gene
The variation is divided into batch variation and machine variation, and the process genes and the machine genes need to be decoded again to form new chromosomes after each batch variation so as to ensure the legality of the chromosomes;
tenth, adopting adaptive genetic algorithm to carry out optimization solution
Optimizing according to the sine self-adaptive genetic algorithm of the formula (10) and the formula (11) to improve the convergence speed and the convergence precision of the algorithm and ensure the globality and the precision of algorithm searching;
cross probability:
the mutation probability:
in the formula f max The maximum fitness value in the current population is obtained; f. of avg The average fitness value in the current population is obtained; f' is the larger fitness value of the two individuals to be crossed; f is the fitness value of the individual to be mutated; p is a radical of c1 、p m1 The value is taken in the range of (0,1) as a coefficient;
eleventh, decoding
Decoding is the inverse operation of encoding, the procedure processing sequence of each batch of various workpieces is obtained by the procedure gene section of the chromosome, the corresponding processing machine is obtained by the machine gene section, and the starting time and the ending time of each procedure are calculated according to the formulas (4) - (9).
2. The large-scale flexible job shop scheduling optimization method according to claim 1, characterized in that: the third layer coding mode in the step three is specifically as follows: under the condition that two types of workpieces exist, and each workpiece has two working procedures, the first layer represents the number of batches formed after the workpieces are assembled, and integer coding Chrom (1,1) is adopted to represent that the 1 st type of workpieces are divided into 2 batches; the second layer represents the processing sequence of the workpiece process, and adopts a coding mode based on the workpiece, wherein 101 represents the 1 st type of processThe first lot of pieces, 102 representing the second lot of type 1 pieces, 101 appearing first in the process gene representing its first process, and so on, the process gene representing the machining sequence 101 → 201 → 102 → 101 → 201 → 102; the third layer represents the machine selected by the working procedure of the workpiece, integer coding is adopted, and Chrom (1,9) represents the first working procedure of the workpiece 101 in the selectable machine set M ij The first machine 2 is selected.
3. The large-scale flexible job shop scheduling optimization method according to claim 1, characterized in that: the step eight is based on the interleaving step of OBX improved interleaving operator as follows: randomly selecting and selecting genes to be crossed in a parent P1 to form a crossed gene pool Pos (201,101,102), deleting 102 from the crossed gene pool because the batch of the first type workpieces for genes in Chrom2 is 1, so that Pos (201, 101) exists in the genes finally used for crossing, and 102 workpieces do not exist in the process genes of Chrom 2; finding the genes 201 in the parent P2, wherein a plurality of genes 201 exist in P2 due to a plurality of processes, selecting one gene at random, deleting the selected gene in P2, crossing other genes in a gene pool, and finally filling the vacant genes in P2 with the selected gene by using P1 to form a child C2; and the alternative genes and the genes which cannot be used for crossing are reserved in the P1, and then the second batch of workpieces which are the individuals 202 which are not in the P1, namely the second type of workpieces, are removed by using the genes which are remained in the P2 to sequentially fill up the vacancy in the P1 to form a progeny C1.
4. The large-scale flexible job shop scheduling optimization method according to claim 1, characterized in that: the selection mode of the appropriate chromosome length in the eighth step is specifically as follows: selecting 10 to 100 percent of the length of the chromosome to carry out crossing, respectively and repeatedly carrying out simulation operation to calculate the average value of the length of the chromosome, and then calculating the sum of weighted values to find that the crossing length is optimal when the crossing length accounts for 10 percent of the length of the chromosome; in order to find out a better target value, 10% to 20% of the chromosome length is selected to be crossed, simulation operation is repeated to obtain an average value, then a weighted value is obtained, and the fact that the chromosome length is selected to be crossed by 12% is found to obtain a more accurate solution and improve the operation speed, so that the cross length of 12% is selected.
5. The large-scale flexible job shop scheduling optimization method according to claim 1, characterized in that: in the tenth step, when the optimization is performed according to the sine adaptive genetic algorithm, a larger cross rate and a larger variation rate are selected at the initial stage of the optimization process to obtain a faster convergence rate and keep the diversity of the population, and after multiple iterations, a smaller cross rate and a smaller variation rate are selected for careful search to avoid the optimal solution from being damaged.
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