CN105741181A - Hybrid flow shop scheduling method for different parallel machines - Google Patents

Abstract

The invention discloses a hybrid flow shop scheduling method for different parallel machines. The method comprises the steps of step S11: initializing target parameters and randomly generating an initialized population; step S12: decoding individuals in the population; step S13: calculating the fitness values of the individuals in the population; step S14: judging whether a stop condition is met or not, and if not, entering the step S15; step S15: performing selection on the population by adopting a roulette wheel selection method to remain elite individuals; step S16: performing crossover operation; step S17: performing mutation operation; step S18: calculating the similarity of the individuals in the population and discarding the individuals with relatively low fitness values; and step S19: supplementing new individuals, generating a new-generation population in combination with the selected elite individuals in the step S15, and returning to the step S12. According to the method, the shop scheduling efficiency is effectively improved and the production cost is reduced, so that the method has a wide application prospect.

Description

A kind of different parallel machine mixed production line Job-Shop method

Technical field

The present invention relates to manufacturing technology field, particularly to a kind of different parallel machine mixed production line Job-Shop methods.

Background technology

Due to manufacturing industry critical role in national economy, obtain the great attention of academia, industrial circle around manufacturing every technology.Scheduling problem in manufacturing industry is an important content in production management, and scheduling problem can be divided into Single Machine Scheduling, Job-shop scheduling (i.e. Job Shop Scheduling), Flow-shop scheduling (i.e. Flow Shop scheduling), Open-shop scheduling (i.e. open job shop scheduling), the several fundamental type of many machines parallel fabrication.Single Machine Scheduling refers to that all process operations of workpiece all complete on a machine, it is necessary to work pieces process task is optimized queuing；Job-shop scheduling refers to n the workpiece distribution processing on m different machine having particular process sequence requirement, and the inter process of different workpieces does not have sequence constraint, but working procedure processing can not be interrupted；Flow-shop scheduling assumes that n workpiece is all processed on same equipment, and process operation is identical with processing sequence；The scheduling of many machines parallel fabrication refers to that the machine of processing is similar with work workpiece, can at multiple stage machine parallel fabrication workpiece.Actual scheduling problem can be the combination of above-mentioned several fundamental type.Along with the research of scheduling problem is goed deep into, it has been found that many scheduling problems are proved to as np complete problem.Therefore, rely solely in classical scheduling theory based on the technology of analytical optimization and method, it is intended to solve to belong to the actual schedule problem of np complete problem, inevitably run into the obstacle being difficult to go beyond.In view of this, based on the method for computational intelligence, including artificial neural network, fuzzy system, genetic algorithm, ant group algorithm, immune algorithm etc., computing intelligence becomes the important research direction solving scheduling problem.But how to design computing intelligence fast and effectively, get final optimal solution or approximate solution, be still current problem demanding prompt solution.

In sum it can be seen that how being effectively improved Job-Shop efficiency is current problem demanding prompt solution.

Summary of the invention

In view of this, it is an object of the invention to provide a kind of different parallel machine mixed production line Job-Shop method, the method can be effectively improved Job-Shop efficiency, reduces production cost, thus improve the ability of enterprise response turn of the market, have broad application prospects.Its concrete scheme is as follows:

A kind of different parallel machine mixed production line Job-Shop method, including:

Step S11: target component is initialized, the initialized population of stochastic generation；Wherein, described target component includes population scale and crossover probability；

Step S12: the individuality in described population is decoded；

Step S13: calculate the ideal adaptation angle value in described population；

Step S14: judge whether to meet end condition, if it is, input optimal solution, and terminate；If it is not, then enter step S15；

Step S15: adopt roulette selection method, described population is selected, to retain elite individuality；

Step S16: utilize intersection operational approach, carries out intersecting operating to the individuality in described population；

Step S17: utilize mutation operation method, carries out mutation operation to the individuality in described population；

Step S18: calculate individual comparability degree in described population, and individuality relatively low for ideal adaptation angle value in two similar individuals is abandoned；

Step S19: supplement new individual, and the elite selected in integrating step S15 is individual, to generate a new generation population, and is back to step S12.

Preferably, in initialized population, each individual adopting arranging and encoding, be specially the arrangement of all workpiece sequence numbers as body one by one, the lengths table of body is shown as n × s, n and represents the sum of workpiece one by one, and s represents the sum of manufacturing procedure；Wherein, in arbitrary individuality, the diverse location of same workpiece sequence number represents the order of its manufacturing procedure.

Preferably, in step s 12, decoding process specifically includes:

Step S121: in first operation, workpiece is selected according to coded sequence, and the workpiece chosen is distributed on the machine that extremely current process velocity is the fastest, if the process velocity of current all machines is all identical, then randomly choose machine, update the release time of the workpiece time that machines in first operation and machine；Wherein, corresponding workpiece allocation rule includes: have the workpiece priority allocation of the longest process time；Corresponding machine assignment rule includes: the more fast machine of process velocity more first distributes, the free time the shortest distribution of machine；Until the first operation of all workpiece is assigned；

Step S122: in second operation work, machines the time according to each workpiece in first operation, it is determined that it is in the processing sequence of this procedure；Wherein, the processing sequence of workpiece determines that rule includes: the workpiece first completed first is processed, and machines time identical workpiece if existing, then select the workpiece of long processing time in residue workpiece first to process, if the process time in residue workpiece is all identical, then randomly chooses a workpiece and first process；Corresponding workpiece allocation rule includes: distribution, on the machine that process velocity is the fastest, if machining speed is all identical, then randomly chooses machine, and updates the time that machines of workpiece and the release time of processing machine；Until all workpiece are assigned in second operation work；

Step S123: repeating said steps S122, until all process steps is assigned.

Preferably, in step s 13, the computing formula of ideal adaptation angle value is as follows:

$f\left(i\right)=\frac{1}{c_{max}\left(i\right)};$

Wherein, c_max(i) maximum scheduling time representated by i-th individuality in described population.

Preferably, in step S17, described mutation operation method includes:

According to equationShown in mutation probability, the individuality in described population is made a variation, wherein, N is the scale of described population, and C is default constant.

Preferably, in step S18, the computational methods of individual comparability degree include:

Adopting the continuous matched rule in r position, the similarity that any two in described population is individual is evaluated, r is positive integer；Wherein, the continuous matched rule in described r position is particularly as follows: to there is r character between any two character string X and Y identical, then it is assumed that X and Y is similar.

Preferably, the keeping method of population diversity includes:

Calculate individual comparability degree in population, and individuality relatively low for ideal adaptation angle value in two similar individuals is abandoned；

Stochastic generation is newly individual, and new individuality is added population, to produce new population.

The present invention is directed to the parallel machine mixed production line Job-Shop problem that production capacity is different, propose a kind of dispatching method based on computational intelligence, devise relevant coded method, coding/decoding method, intersection operation, mutation operation, selection operation, individual comparability degree evaluation methodology, population diversity keeping method etc..The present invention is based on computational intelligence method, can fast and effeciently get optimal solution or its approximate solution, to solve different parallel machine mixed production line Job-Shop technical barrier, thus being effectively improved Job-Shop efficiency, reduce production cost, thus improve the ability of enterprise response turn of the market, have broad application prospects.

Accompanying drawing explanation

In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, the accompanying drawing used required in embodiment or description of the prior art will be briefly described below, apparently, accompanying drawing in the following describes is only embodiments of the invention, for those of ordinary skill in the art, under the premise not paying creative work, it is also possible to obtain other accompanying drawing according to the accompanying drawing provided.

Fig. 1 is the disclosed exemplary plot about HFSP problem of the embodiment of the present invention；

Fig. 2 is the disclosed a kind of different parallel machine mixed production line Job-Shop method flow diagrams of the embodiment of the present invention；

Fig. 3 is individual comparability degree evaluation methodology schematic diagram disclosed in the embodiment of the present invention；

Fig. 4 is the disclosed operational approach schematic diagram that intersects of the embodiment of the present invention.

Detailed description of the invention

Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is only a part of embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, the every other embodiment that those of ordinary skill in the art obtain under not making creative work premise, broadly fall into the scope of protection of the invention.

The embodiment of the invention discloses a kind of different parallel machine mixed production line Job-Shop method, the method is the HFSP problem (HFSP for different parallel machines, i.e. HybridFlow-shopSchedulingProblem, mixed production line Job-Shop problem) and propose.

It should be noted that HFSP problem specifically refers to: there be n workpiece to carry out the processing of s operation on streamline, wherein, workpiece sequence number is J_i, i=1 ..., n；Operation j=1 ... s, s are operation sum, m_jRepresenting the number of machines corresponding to jth operation, and meet the condition shown in equation (1), namely every procedure has at least a machine, and has at least one procedure to there is parallel machine, and the process performance of every machine can be different.Every procedure of each workpiece can be processed on any one machine, but every one procedure must will complete.Require the sequence of all workpiece and the distribution condition of upper machine of each stage so that regulation index (is generally Maximal Makespan: min (PR_max)) minimum.Wherein, PR_maxRepresent Maximal Makespan.Fig. 1 has illustrated an example of above-mentioned HFSP problem.It addition, the present embodiment also further defines: ST_i,jFor workpiece J_iAt the processing time started of jth procedure, PR_i,jFor workpiece J_iAt the process time of jth procedure, EN_i,jFor workpiece J_iThe time is machined at jth procedure.

Concrete, HFSP problem in the present embodiment following several in assumed:

1), each workpiece to be processed be an entity, the course of processing of an entity can not be carried out simultaneously；2), workpiece number n is known and fixes, before terminating processing, it is impossible to midway is taken away；3), workpiece just can not interrupt once processing；4), a machine synchronization can only process a workpiece；5), a workpiece synchronization can only be processed on a machine；6), workpiece can be processed on any one the machine in per stage；7), workpiece is known and fixing in the process time of each procedure；8), workpiece one-way flow in Flow Shop, all workpiece have same processing sequence；9), process number is more than 2；10), equipment can not wait, middle zero storage.

It addition, above-mentioned equation (1) particularly as follows:

Further, the HFSP problem in the present embodiment need to meet equation (2) to the constraints shown in (15), wherein, equation (2) to equation (15) respectively particularly as follows:

The scheduling problem that the present invention is directed to meets following constraints:

$\underset{i=1}{\overset{n}{\Σ}}{x}_{i,l}=1,l=1,2,...,n---\left(3\right)$

$\underset{l=1}{\overset{n}{\Σ}}{x}_{i,l}=1,i=1,2,...,n---\left(4\right)$

$\underset{k=1}{\overset{m_j}{\Σ}}{y}_{i,j,k}=1,i=1,2,...,n;j=1,2,...,s---\left(6\right)$

${\mathrm{EN}}_{i_1,1}={\mathrm{PR}}_{i_1,1},{\mathrm{EN}}_{i_1,j}={\mathrm{EN}}_{i_1,j-1}+{\mathrm{PR}}_{i_1,j},j=2,...,s---\left(7\right)$

${\mathrm{EN}}_{i_h,1}={\mathrm{EN}}_{i_{h-1},1}+{\mathrm{PR}}_{i_h,1},h=2,...,s---\left(8\right)$

${\mathrm{EN}}_{i_h,j}=max\{{\mathrm{EN}}_{i_{h-1},j},{\mathrm{EN}}_{i_{h-1},j-1}\}+{\mathrm{PR}}_{i_h,j}h=2,3,...,n;j=2,...,s-1---\left(9\right)$

EN_i,j=ST_i,j+PR_i,j, i=1,2 ..., n；J=1,2 ..., s, pr=1,2 ..., s (10)

EN_i,j≤ST_i,j+1, i=1,2 ..., n；J=1,2 ..., s-1 (11)

$\underset{i=1}{\overset{n}{\Σ}}{x}_{i,l}{\mathrm{ST}}_{i,j}\≤\underset{i=1}{\overset{n}{\Σ}}{x}_{i,l+1}{\mathrm{ST}}_{i,j},l=1,2,...,n-1,j=1,2,...,s---\left(12\right)$

$\underset{i=1}{\overset{n}{\Σ}}{x}_{i,l}{\mathrm{ST}}_{i,j}\≤\underset{i=1}{\overset{n}{\Σ}}{x}_{i,l+1}{\mathrm{ST}}_{i,j},l=1,2,...,n-1,j=1,2,...,s---\left(13\right)$

$\begin{array}{c}\underset{i=1}{\overset{n}{\Σ}}{x}_{i,l_1}{y}_{i,j,k}{\mathrm{EN}}_{i,j}\≤\underset{i=1}{\overset{n}{\Σ}}{x}_{i,l_2}{y}_{i,j,k}{\mathrm{EN}}_{i,j}+(1-\underset{i=1}{\overset{n}{\Σ}}{x}_{i,l_2}{y}_{i,j,k}{\mathrm{EN}}_{i,j})\×M,l_1,l_2=1,2,\mathrm{...},n,\\ l_1\≤l_2,j=1,2,\mathrm{...},s,k=1,2,\mathrm{...},m_j\end{array}---\left(14\right)$

minPR_max(15)

Equation (2) x_ijIt is 1, represents that workpiece i is arranged at the l position, be otherwise 0；Equation (3) expression guarantees that each priority position can only a corresponding workpiece；Equation (4) guarantees each workpiece only one of which priority position；Equation (5) y_i,j,kIt is 1, represents that workpiece i is arranged at the l position of jth procedure, be otherwise 0；Equation (6) represents that each workpiece can only select one in parallel processor to process；Equation (7) represents, if the first operation of workpiece, then the first working procedure processing deadline of workpiece is equal to process time of workpiece, and otherwise it machines the time that machines equal to the upper one procedure time plus process time of this procedure；Equation (8) represents that the time that machines of the first operation of workpiece adds the process time of this workpiece first operation equal to the time that machines of a upper workpiece；Equation (9) represents that the time that machines of workpiece adds sum process time of this workpiece equal to the time that machines of previous workpiece with the maximum of the time that machines of an operation on this workpiece；Equation (10) represents each workpiece relation starting the time that processes and end process time in each stage, and each workpiece can only select one in LPT device to be processed；Equation (11) represents that same workpiece must first complete the processing of current generation before carrying out the process of next stage；Equation (12) represents same stage, and the workpiece that priority is more high starts the time that processes more early, and namely a machine can only process a workpiece simultaneously；Equation (13) represents more morning time that the workpiece that in same stage scheduling arrangement, ranking is more forward starts to process；Equation (14) represents that same stage distribution workpiece ranking on uniform machinery workpiece rearward just can be processed after must waiting forward work pieces process, it is a big number when the workpiece being in diverse location does not add man-hour M on the uniform machinery of same stage, to ensure that inequality perseverance is set up；Equation (15) is regulation index, i.e. earliest finish time.

It should be noted that above-mentioned so-called same stage refers to same one procedure.

For HFSP problem set forth above, embodiments providing a kind of different parallel machine mixed production line Job-Shop method, shown in Figure 2, the method specifically includes:

Step S11: target component is initialized, the initialized population of stochastic generation；Wherein, target component includes population scale and crossover probability；

Step S12: the individuality in population is decoded；

Step S13: calculate the ideal adaptation angle value in population；

Step S14: judge whether to meet end condition, if it is, input optimal solution, and terminate；If it is not, then enter step S15；

Step S15: adopt roulette selection method, population is selected, to retain elite individuality；

Step S16: utilize intersection operational approach, carries out intersecting operating to the individuality in population；

Step S17: utilize mutation operation method, carries out mutation operation to the individuality in population；

Step S18: calculate individual comparability degree in population, and individuality relatively low for ideal adaptation angle value in two similar individuals is abandoned；

Step S19: supplement new individual, and the elite selected in integrating step S15 is individual, to generate a new generation population, and is back to step S12.

It should be noted that the end condition in above-mentioned steps S14 can be when the algebraically of population is more than preset value, then start-stop process.

Visible, the embodiment of the present invention is in the premise at elite retention strategy, and the individuality that namely fitness is the highest is directly entered population of future generation, and all the other individualities, according to roulette selection method, select population of future generation with the individuality being proportional to ideal adaptation degree.

The present invention is directed to the parallel machine mixed production line Job-Shop problem that production capacity is different, propose a kind of dispatching method based on computational intelligence, devise relevant coded method, coding/decoding method, intersection operation, mutation operation, selection operation, individual comparability degree evaluation methodology, population diversity keeping method etc..The present invention is based on computational intelligence method, can fast and effeciently get optimal solution or its approximate solution, to solve different parallel machine mixed production line Job-Shop technical barrier, thus being effectively improved Job-Shop efficiency, reduce production cost, thus improve the ability of enterprise response turn of the market, have broad application prospects.

The embodiment of the invention discloses a kind of concrete different parallel machine mixed production line Job-Shop methods, relative to a upper embodiment, technical scheme has been made further instruction and optimization by the present embodiment.Concrete:

In the present embodiment, in initialized population, each individual adopting arranging and encoding, be specially the arrangement of all workpiece sequence numbers as body one by one, the lengths table of body is shown as n × s, n and represents the sum of workpiece one by one, and s represents the sum of manufacturing procedure；Wherein, in arbitrary individuality, the diverse location of same workpiece sequence number represents the order of its manufacturing procedure.Such as, body { 1,3,2,4,4 one by one of 4 workpiece 3 procedures, 2,3,1,2,4,3,1}, therein digital 1 to 4 represent workpiece sequence number, and the order of appearance represents processing sequence, first the 1 first operation representing workpiece 1, second 1 second operation work representing workpiece 1, the like.Need it is further noted that in a upper embodiment step S19, the individuality in the population of new generation of generation is also that the mode adopting above-mentioned arranging and encoding is encoded.

It addition, in a upper embodiment step S12, decoding process specifically includes:

Step S121: in first operation, workpiece is selected according to coded sequence, and the workpiece chosen is distributed on the machine that extremely current process velocity is the fastest, if the process velocity of current all machines is all identical, then randomly choose machine, update the release time of the workpiece time that machines in first operation and machine；Wherein, corresponding workpiece allocation rule includes: have the workpiece priority allocation of the longest process time；Corresponding machine assignment rule includes: the more fast machine of process velocity more first distributes, the free time the shortest distribution of machine；Until the first operation of all workpiece is assigned；

Step S122: in second operation work, machines the time according to each workpiece in first operation, it is determined that it is in the processing sequence of this procedure；Wherein, the processing sequence of workpiece determines that rule includes: the workpiece first completed first is processed, and machines time identical workpiece if existing, then select the workpiece of long processing time in residue workpiece first to process, if the process time in residue workpiece is all identical, then randomly chooses a workpiece and first process；Corresponding workpiece allocation rule includes: distribution, on the machine that process velocity is the fastest, if machining speed is all identical, then randomly chooses machine, and updates the time that machines of workpiece and the release time of processing machine；Until all workpiece are assigned in second operation work；

Step S123: repeat step S122, until all process steps is assigned.

Further, in a upper embodiment step S13, the computing formula of ideal adaptation angle value, i.e. fitness function, specific as follows:

$f\left(i\right)=\frac{1}{c_{max}\left(i\right)}---\left(16\right)$

Wherein, c_max(i) maximum scheduling time representated by i-th individuality in population.

In a upper embodiment step S17, mutation operation method specifically includes:

According to the mutation probability shown in equation (17), the individuality in population is made a variation, equation (17) particularly as follows:

$p_i={\[1+\exp (C\×f(i)/\underset{j=1}{\overset{N}{\Σ}}f(j\left)\right)\]}^{-1}---\left(17\right)$

Wherein, N is the scale of population, and C is default constant, and equation (17) shows that affinity is more big, then mutation probability is more little, otherwise, then more big.

It addition, in a upper embodiment step S18, the computational methods of individual comparability degree include:

Adopting the continuous matched rule in r position, the similarity that any two in population is individual is evaluated, r is positive integer；Wherein, the continuous matched rule in r position is particularly as follows: to there is r character between any two character string X and Y identical, then it is assumed that X and Y matches, and namely thinks that both are similar, specifically can referring to Fig. 3.

In the present embodiment, the keeping method of population diversity includes: calculating individual comparability degree in population, and abandoned by individuality relatively low for ideal adaptation angle value in two similar individuals, stochastic generation is newly individual, and new individuality is added population, to produce new population.

Further, intersection operational approach in a upper embodiment step S16 specifically refers to: chooses two individualities from population randomly and by given crossover probability, a random fragment position is swapped, then the part not comprised in intersecting further according to order polishing original in male parent, as shown in Figure 4, the filial generation produced is calculated its fitness, if being better than male parent, replacing male parent, otherwise abandoning.

Finally, it can further be stated that, in this article, term " includes ", " comprising " or its any other variant are intended to comprising of nonexcludability, so that include the process of a series of key element, method, article or equipment not only include those key elements, but also include other key elements being not expressly set out, or also include the key element intrinsic for this process, method, article or equipment.When there is no more restriction, statement " including ... " key element limited, it is not excluded that there is also other identical element in including the process of described key element, method, article or equipment.

Above a kind of different parallel machine mixed production line Job-Shop methods provided by the present invention are described in detail, principles of the invention and embodiment are set forth by specific case used herein, and the explanation of above example is only intended to help to understand method and the core concept thereof of the present invention；Simultaneously for one of ordinary skill in the art, according to the thought of the present invention, all will change in specific embodiments and applications, in sum, this specification content should not be construed as limitation of the present invention.

Claims (7)

1. a different parallel machine mixed production line Job-Shop method, it is characterised in that including:

Step S11: target component is initialized, the initialized population of stochastic generation；Wherein, described target component includes population scale and crossover probability；

Step S12: the individuality in described population is decoded；

Step S13: calculate the ideal adaptation angle value in described population；

Step S14: judge whether to meet end condition, if it is, input optimal solution, and terminate；If it is not, then enter step S15；

Step S15: adopt roulette selection method, described population is selected, to retain elite individuality；

Step S16: utilize intersection operational approach, carries out intersecting operating to the individuality in described population；

Step S17: utilize mutation operation method, carries out mutation operation to the individuality in described population；

Step S18: calculate individual comparability degree in described population, and individuality relatively low for ideal adaptation angle value in two similar individuals is abandoned；

Step S19: supplement new individual, and the elite selected in integrating step S15 is individual, to generate a new generation population, and is back to step S12.

2. different parallel machine mixed production line Job-Shop method according to claim 1, it is characterized in that, in initialized population, each individual employing arranging and encoding, it is specially the arrangement of all workpiece sequence numbers as body one by one, the lengths table of body is shown as n × s, n and represents the sum of workpiece one by one, and s represents the sum of manufacturing procedure；Wherein, in arbitrary individuality, the diverse location of same workpiece sequence number represents the order of its manufacturing procedure.

3. different parallel machine mixed production line Job-Shop method according to claim 2, it is characterised in that in step s 12, decoding process specifically includes:

Step S121: in first operation, workpiece is selected according to coded sequence, and the workpiece chosen is distributed on the machine that extremely current process velocity is the fastest, if the process velocity of current all machines is all identical, then randomly choose machine, update the release time of the workpiece time that machines in first operation and machine；Wherein, corresponding workpiece allocation rule includes: have the workpiece priority allocation of the longest process time；Corresponding machine assignment rule includes: the more fast machine of process velocity more first distributes, the free time the shortest distribution of machine；Until the first operation of all workpiece is assigned；

Step S122: in second operation work, machines the time according to each workpiece in first operation, it is determined that it is in the processing sequence of this procedure；Wherein, the processing sequence of workpiece determines that rule includes: the workpiece first completed first is processed, and machines time identical workpiece if existing, then select the workpiece of long processing time in residue workpiece first to process, if the process time in residue workpiece is all identical, then randomly chooses a workpiece and first process；Corresponding workpiece allocation rule includes: distribution, on the machine that process velocity is the fastest, if machining speed is all identical, then randomly chooses machine, and updates the time that machines of workpiece and the release time of processing machine；Until all workpiece are assigned in second operation work；

Step S123: repeating said steps S122, until all process steps is assigned.

4. different parallel machine mixed production line Job-Shop method according to claim 3, it is characterised in that in step s 13, the computing formula of ideal adaptation angle value is as follows:

$f\left(i\right)=\frac{1}{c_{max}\left(i\right)};$

Wherein, c_max(i) maximum scheduling time representated by i-th individuality in described population.

5. different parallel machine mixed production line Job-Shop method according to claim 4, it is characterised in that in step S17, described mutation operation method includes:

According to equationShown in mutation probability, the individuality in described population is made a variation, wherein, N is the scale of described population, and C is default constant.

6. different parallel machine mixed production line Job-Shop method according to claim 5, it is characterised in that in step S18, the computational methods of individual comparability degree include:

Adopting the continuous matched rule in r position, the similarity that any two in described population is individual is evaluated, r is positive integer；Wherein, the continuous matched rule in described r position is particularly as follows: to there is r character between any two character string X and Y identical, then it is assumed that X and Y is similar.

7. different parallel machine mixed production line Job-Shop method according to claim 6, it is characterised in that the keeping method of population diversity includes:

Calculate individual comparability degree in population, and individuality relatively low for ideal adaptation angle value in two similar individuals is abandoned；

Stochastic generation is newly individual, and new individuality is added population, to produce new population.