CN116579541A - Genetic algorithm chromosome adjustment method applied to factory intelligent scheduling - Google Patents

Genetic algorithm chromosome adjustment method applied to factory intelligent scheduling Download PDF

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CN116579541A
CN116579541A CN202310373396.3A CN202310373396A CN116579541A CN 116579541 A CN116579541 A CN 116579541A CN 202310373396 A CN202310373396 A CN 202310373396A CN 116579541 A CN116579541 A CN 116579541A
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程依
谢志军
吴崇瑞
陈科伟
辛宇
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Ningbo University
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Abstract

The invention discloses a genetic algorithm chromosome adjustment method applied to factory intelligent scheduling, when a die change collision is considered preferentially in the process of adjusting a chromosome, the machine selection problem is further considered, the die change collision is avoided by replacing a die, when the die change collision cannot be avoided, the machine and the die are replaced, whether the last die used by a selected machine is used by other machines or not is checked, and after the use of the die by other machines is detected, the other machines and the die are replaced for processing, so that frequent die change caused by the die change in the idle time of the machine is avoided, meanwhile, the probability factor is combined to increase the diversity of the chromosome, the chromosome quality of each generation population is ensured by adjusting the chromosome of each generation population, the convergence effect of the algorithm is improved, and better solution can be found out; the method has the advantages that the maximum finishing time of the operation can be prevented from being prolonged while the convergence effect is improved, and the optimization effect is good.

Description

Genetic algorithm chromosome adjustment method applied to factory intelligent scheduling
Technical Field
The invention relates to a genetic algorithm chromosome regulation method, in particular to a genetic algorithm chromosome regulation method applied to factory intelligent scheduling.
Background
As one of the most critical problems in the production, manufacturing and flow planning links, the flexible workshop scheduling problem (FJSP) is an important point for solving the problem of enterprise production optimization, and it makes a fast and effective scheduling scheme by allocating machines and sequencing procedures. However, in a real development environment, reasonable scheduling needs to be considered, not only the selection of machines and the sequence of working procedures, but also the allocation of resources except the machines, which are involved in an actual workshop, for example, a proper die needs to be adapted to a processing machine tool of some special workshops, the weight of the die determines that a loading and unloading process needs to consume a great deal of time, and frequent die loading and unloading often greatly delays the time for completing the operation, so that great resource waste is caused.
One of the common solutions to the FJSP problem is a genetic algorithm, and after adding a mold resource, the problem can be described as a dual-resource flexible shop scheduling problem based on mold loading and unloading, which is specifically expressed as follows: there are I jobs to be processed that require machining on A machines, each job having J i Each step requires processing with a specific die M on a specific machine. Wherein, the working procedures of each operation need to be completed in sequence, and only one machine and one die can be used for processing at a time. When different dies are used, the dies need to be replaced, and the processing can be started after the dies are replaced. Some assumptions are as follows:
(1) Each machine can only process one working procedure of one operation at a time, and the mould is required to be loaded during the processing.
(2) Each mold can only be used on one machine at a time, and if no mold is loaded on the machine or other molds are loaded on the machine, the mold needs to be replaced, and the time spent is dependent on the tonnage of the mold and is irrelevant to the machine on which the mold is loaded.
(3) When a certain process of the job is started, the process cannot be interrupted.
(4) The operation must be completed in the sequence of the working procedures, and the latter working procedure can be started after the former working procedure is completed.
The scheduling problem of the double-resource flexible workshop based on die loading and unloading needs to be solved.
(1) The machine and die selected for each process is determined.
(2) The order of processing on each machine is determined so that the primary indicators of interest (e.g., maximum finishing time, mold change time) are as optimal as possible.
After the problem is described again, the genetic algorithm may continue to be used to solve the problem, with the coding scheme being adapted as well. However, in the conventional genetic algorithm coding strategy, the randomly generated chromosomes are often adjusted by using an insertion decoding method, so as to avoid the problem of poor convergence effect caused by the too poor quality of the randomly generated chromosomes, wherein the most common insertion decoding method is a process insertion method. However, if only the process insertion method is used to adjust the codes added to the mold constraint, the optimization effect is often poor, and even reverse optimization is caused, namely, only the process optimization is considered, more mold changing conflicts are added, so that the maximum finishing time of the operation is prolonged instead.
Disclosure of Invention
The invention aims to solve the technical problem of providing a genetic algorithm chromosome adjustment method which can improve the convergence effect, can avoid the maximum finishing time extension of the operation and has good optimization effect and is applied to intelligent scheduling of factories.
The technical scheme adopted for solving the technical problems is as follows: when a genetic algorithm is used for solving the problem of scheduling a double-resource flexible workshop based on die loading and unloading, the total number of operations to be processed in the problem is recorded as I, the I operations are numbered according to 1-I in sequence, the operation with the number of I is called as operation I, i=1, 2,3, … and I, and the total number of the operations of the operation I is recorded as J i The j-th step of the operation i is referred to as step O ij ，j＝1，2，…，J i The method comprises the steps of carrying out a first treatment on the surface of the Total number of machines that will be able to process the I jobsThe machine number of the machine A is set to be 1,2,3, … and A in sequence; the total number of the dies which can be used in the I operations is recorded as M, and the die numbers of the M dies are set to be 1,2,3, … and M in sequence; the mold change time of the mth mold is recorded as MT m M=1, 2,3, …, M, process O will be able to be handled ij Is denoted as L ij Will be able to handle process O ij The set of machine numbers of all machines of (a) is called process O ij Is to process step O ij The set of alternative machines is denoted as A ij ，A ij ＝{A ij1 ，A ij2 ，…，A ijLij }, wherein A ij1 ，A ij2 ,……,A ijLij Respectively represent the treatable process O ij Machine number of the machine of (2); step O ij Optional machine set A of (2) ij The corresponding set of processing times is denoted as AT ij ，AT ij ＝{AT ij1 ，AT ij2 ，…，AT ijLij (wherein A) ij Machine number and AT in (a) ij One-to-one correspondence of processing time of AT ij Each processing time in (a) represents use A ij Machine processing step O of the corresponding machine number ij The required processing time; process step O can be performed ij The total number of all dies is denoted as H ij Will enable processing procedure O ij The set of die numbers for all dies of (a) is referred to as process O ij Optional die set of (a), process O ij Is denoted as M ij ，M ij ＝{M ij1 ，
M ij2 ，…，M ijHij M is }, where M ij1 ，M ij2 ,…,M ijHij Respectively represent the treatable process O ij Is a mold number of a mold; generating an initial population containing P scheduling solutions, namely a 0 th generation population, by adopting a mixed strategy of global selection, local selection and random selection and adopting a three-layer coding mode of a procedure ordering sequence, a machine selection sequence and a mould selection sequence, wherein one scheduling solution is a chromosome, P is an integer which is more than or equal to 100 and can be divided by 100, and the P chromosomes are numbered sequentially according to 1-P The chromosome numbered P is referred to as the P-th chromosome, p=1, 2, …, P; each chromosome comprises three layers of sequences, namely a process sequencing sequence, a machine selection sequence and a mould selection sequence, wherein the lengths of the process sequencing sequence, the machine selection sequence and the mould selection sequence are respectively set to be Q,each gene of the sequence represents a procedure, the value of each gene corresponds to a job number, and J exists in the sequence i Job number, J of the ith job i The job number of the ith job sequentially represents the 1 st to the Ji th working procedures of the ith job according to the left-to-right sequence, namely when the ith appears from left to right for the j th time in the working procedure sequencing sequence, the occurrence number is j, and the occurrence number corresponds to the j th working procedure of the ith job; in the machine selection sequence, the value of each gene is a machine number, each gene corresponds to a procedure, namely, the value of each gene is a machine number capable of processing the procedure corresponding to the machine number, the value of each gene can be found in the selectable machine set of the procedure corresponding to the machine number, and the genes of the machine selection sequence and the procedure O 11 ,O 12 …
O 1J1 O 21 ,O 22 …O 2J2 …O I1 ,O I2 …O IJI One-to-one correspondence is made from left to right in sequence; in the die selection sequence, the value of each gene is a die number, each gene corresponds to a procedure, namely, the value of each gene is a die number capable of processing the procedure corresponding to the die number, the value of each gene can be found in a selectable die set of the procedure corresponding to the die number, and the genes of the die selection sequence and the procedure O 11 ,O 12 …O 1J1 O 21 ,O 22 …O 2J2 …O I1 ,O I2 …O IJI One-to-one correspondence is made from left to right in sequence; in the running process of the genetic algorithm, the current iteration coefficient is recorded as t, when the t iteration is carried out, the sequence of the p-th chromosome in the t-1 generation population is updated according to the genetic algorithm, the sequence of the p-th chromosome in the t.1 generation population is obtained, and the t-1 generation population is updatedThe machine selection sequence and the die selection sequence of the p chromosome in the population are updated by adopting the following method to obtain the machine selection sequence and the die selection sequence of the p chromosome in the t.1 generation population:
step one: setting an iteration variable q, initializing q, and enabling q=1; the mold with the mold number m is called a mold m; setting the number of times of use of the die m as MN m Let its initial value be 0; setting the machine number of the machine where the die m is currently located as MA m Let its initial value be 0, MA m A value of 0 indicates that the die m is not used by any machine; setting the current end use time of the die m as ME m Let its initial value be 0, ME m A value of 0 indicates that die m is not currently being used on any machine; the machine with machine number c is called machine c, c=1, … a, and the mold number of the mold currently used by machine c is set to AM c Let its initial value be 0, AM c When 0, it means that machine c is not currently using any mold; setting the current machining end time of machine c to AE c Let its initial value be 0; setting the current process completion time of the operation i to be OE i Let its initial value be 0;
step two: determining corresponding operation and procedure according to the value of the q-th gene of the procedure selection sequence of the p-th chromosome of the t-1 generation population and the occurrence frequency of the value in the procedure selection sequence of the p-th chromosome of the t-1 generation population, then finding the gene corresponding to the procedure in the machine selection sequence of the p-th chromosome of the t-1 generation population and the gene corresponding to the procedure in the mould selection sequence, and updating the two found genes, wherein the specific updating process is as follows:
s2.1, determining the operation number of the operation corresponding to the q-th gene and the process number of the corresponding process, wherein the operation number is denoted as f, the process number is denoted as g, determining the position of the gene found in the machine selection sequence, denoted as R, namely the gene from left to right in the machine selection sequence, determining the position of the gene found in the mold selection sequence, denoted as R, namely the gene from left to right in the mold selection sequence, and the machine number of the machine corresponding to the R-th gene in the machine selection sequence, denoted as R n, i.e. the machine corresponding to the R-th gene of the machine selection sequence is machine n, the die number of the die corresponding to the R-th gene of the die selection sequence is denoted as e, i.e. the die corresponding to the R-th gene of the die selection sequence is die e, determining the process O fg Is A fg And the optional die set is M fg ；
S2.2, checking the mould number AM of the mould currently used by the machine n n If 0, turning to S2.3, otherwise, turning to S2.4;
s2.3, comparison in Process O fg Is a selectable mould set M fg The number of the use times of each die is recorded as M if only one die with the minimum use times exists min If there are a plurality of dies with the smallest number of times of use, the corresponding die changing times of the dies are compared, and if there is only one die with the smallest die changing time, the die number of the die is marked as M min If there are a plurality of dies with the minimum die changing time, one of the dies is randomly selected, and the die number of the die is designated as M min The method comprises the steps of carrying out a first treatment on the surface of the Randomly generating a probability factor pi by adopting a random function, wherein pi is a random number between 0 and 1; if pi>0.5, updating the value of the R gene of the die selection sequence of the p chromosome of the t-1 generation population to M min Otherwise, the value of the R gene of the mould selection sequence of the p chromosome of the t-1 generation population is kept unchanged; the value of the R gene of the die selection sequence of the p chromosome of the t-1 generation population at this time is designated as R min The method comprises the steps of carrying out a first treatment on the surface of the By using dies R min Number of die uses MN Rmin The sum of the current value of (1) added to the updated mold R min Number of die uses MN Rmin Is used to update the die R with n min Machine number MA of the machine currently located Rmin Is a value of (2); mold R min Is the current end of use time ME of (2) Rmin Update the value of the current value to the MT Rmin AT (automatic Transmission) fgn And (3) summing; the current processing end time AE of machine n n The value of (2) and the current completion time OE of the job f f The value of (a) is updated to be the current use end time ME of the die e e Is the most recent value of (2); will beMachine n currently uses die number AM of die n The value of (2) is updated to M min The method comprises the steps of carrying out a first treatment on the surface of the Then turning to the third step;
s2.4, checking AM n Whether or not in the process O fg Is a selectable mould set M fg If yes, go to S2.5, otherwise, copy procedure O fg Optional machine set A of (2) fg This was designated A' fg And turning to S2.6;
s2.5, checking the mould AM n Machine number MA of the current machine of (a) AMn If equal to n, updating the value of the R gene of the die selection sequence of the p chromosome of the t-1 generation population to AM n Comparing the current process completion time OE of the operation f f Value of machine n and current machining end time AE n If the values of (2) are equal, the current process completion time OE of the operation f is directly set to f Assignment of values to Process O fg Start time T of (1) fg If not, assigning a larger value to step O fg Start time T of (1) fg Then, the mold AM n Number of die uses MN AMn Updates the value of (2) to the sum of the current value and 1, and sets the mold AM n Machine number MA of the current machine of (a) AMn The value of (2) is updated to n; mold AM n Is the current end of use time ME of (2) AMn The value of (2) is updated to T fg +AT fgn The method comprises the steps of carrying out a first treatment on the surface of the The current processing end time AE of machine n n The current process completion time OE of the value of (a) and the job f f Is updated to the current end of use time ME of the die AMe AMn Is the most recent value of (2); and turning to the third step; if n is not equal, copy Process O fg Optional machine set A of (2) fg Is marked as A' fg And turning to S2.6;
s2.6 at A' fg The number n of machine n is removed and A 'is checked at this time' fg Whether the machine is an empty set, if so, adopting an optional machine set A fg Updating the current set of selectable machines A' fg Then, executing the step S2.7 and then turning to the step S2.3, otherwise, directly executing the step S2.7 and then turning to the step S2.2;
S2.7、randomly generating a probability factor pi1 by a random function, pi1 being a random number between 0 and 1, if pi1 >0.5, then compare in procedure O fg Optional machine set M of (1) fg If there is only one machine with the smallest current finishing time, selecting the machine, and recording the value of the machine number of the machine as A min If there are a plurality of machines with the smallest current processing end time, one of the machines is randomly selected, and the value of the machine number of the machine is recorded as A min Thereafter, the value of the R-th gene of the machine selection sequence of the p-th chromosome of the t-1 th generation population is updated to A min The method comprises the steps of carrying out a first treatment on the surface of the If pi1 is less than or equal to 0.5, the value of the R gene of the machine selection sequence of the p-th chromosome of the t-1 generation population is kept unchanged;
step three: judging whether the current value of Q is equal to Q or not, if not, adopting the sum of the current value of Q and 1 to update the value of Q, returning to the second step for the next iteration, and if so, finishing all gene updating of the p chromosome of the t-1 generation population to obtain a machine selection sequence and a mold selection sequence of the p chromosome in the t.1 generation population.
Compared with the prior art, the invention has the advantages that when the chromosome is regulated, the die changing conflict is considered in priority, the machine selection problem is further considered, the die changing times are reduced by replacing the die to avoid die changing conflict, and the die changing time of the die is reduced; when the machine represented by the machine gene pointed by the original chromosome cannot avoid die change conflict, the original scheme is adjusted by changing the machine and the die, so that the die change time is reduced, and the maximum finishing time of the operation is directly shortened; the main reason for generating working intervals by the chromosome adjusted by the method is the working sequence intervals, namely the time intervals caused by waiting for the completion of the previous working procedure, but not the working intervals caused by waiting for release when the used die is in a busy state, so that the die under the scheme is more reasonable in use, the die utilization rate is greatly improved, and the chromosome adjustment optimization effect is better; according to the method, whether the last die used by the selected machine is used by other machines or not is checked, and after the die is detected to be used by other machines, the other machines and the die are replaced for processing, so that frequent die replacement caused by die replacement in idle time of the machines is avoided, meanwhile, the probability factor is combined, the problem that the die is difficult to select for operation in the later stage if the die is not selected for use in the earlier stage, the die use rate is reduced is solved, and the variety of chromosomes is further increased; the invention ensures the chromosome quality of each generation population by adjusting the chromosomes of each generation population, namely the adaptability of the chromosomes of each generation population is more towards 1, so that the algorithm can more rapidly find a better solution in the same iteration times, the convergence effect of the algorithm is improved, and meanwhile, after the iteration times are increased, the algorithm can have the opportunity to find the better solution to obtain a better solution to the problem, thereby the invention can avoid the maximum finishing time extension of the operation while improving the convergence effect, and the optimization effect is good.
Drawings
FIG. 1 is a diagram showing the final effect of a modified chromosome, which is obtained by modifying an original chromosome using a conventional process insertion method;
FIG. 2 is a diagram showing the final effect Gantt according to the scheme represented by the adjusted chromosome after the original chromosome is adjusted by using the prior machine insertion method;
FIG. 3 is a diagram showing the final effect Gantt chart of the method represented by the adjusted chromosome after the original chromosome is adjusted by using the prior mold insertion method;
FIG. 4 is a graph of the final effect Gantt chart of the method for adjusting chromosomes according to the scheme represented by the adjusted chromosomes after the original chromosomes are adjusted by using the genetic algorithm applied to factory intelligent scheduling according to the invention.
Detailed Description
The invention is described in further detail below with reference to the embodiments of the drawings.
Examples: genetic algorithm chromosome adjustment method applied to factory intelligent scheduling, and when genetic algorithm is used for solving problem of scheduling of double-resource flexible workshop based on die loading and unloadingIn this case, the total number of jobs to be processed in the problem is denoted as I, I jobs are numbered in order of 1 to I, the jobs numbered I are referred to as jobs I, i=1, 2,3, …, I, and the total number of jobs I is denoted as J i The j-th step of the operation i is referred to as step O ij ，j＝1，2，…，J i The method comprises the steps of carrying out a first treatment on the surface of the The total number of machines capable of processing the I operations is recorded as A, and the machine numbers of the A machines are set to be 1,2,3, … and A in sequence; the total number of the dies which can be used in the I operations is recorded as M, and the die numbers of the M dies are set to be 1,2,3, … and M in sequence; the mold change time of the mth mold is recorded as MT m M=1, 2,3, …, M, process O will be able to be handled ij Is denoted as L ij Will be able to handle process O ij The set of machine numbers of all machines of (a) is called process O ij Is to process step O ij The set of alternative machines is denoted as A ij ，A ij ＝{A ij1 ，A ij2 ，…，A ijLij }, wherein A ij1 ，A ij2 ,……,A ijLij Respectively represent the treatable process O ij Machine number of the machine of (2); step O ij Optional machine set A of (2) ij The corresponding set of processing times is denoted as AT ij ，AT ij ＝{AT ij1 ，AT ij2 ，…，AT ijLij (wherein A) ij Machine number and AT in (a) ij One-to-one correspondence of processing time of AT ij Each processing time in (a) represents use A ij Machine processing step O of the corresponding machine number ij The required processing time; process step O can be performed ij The total number of all dies is denoted as H ij Will enable processing procedure O ij The set of die numbers for all dies of (a) is referred to as process O ij Optional die set of (a), process O ij Is denoted as M ij ，M ij ＝{M ij1 ，M ij2 ，…，M ijHij M is }, where M ij1 ，
M ij2 ,…,M ijHij Respectively represent the treatable process O ij Is a mold number of a mold; by passing throughGenerating an initial population containing P scheduling solutions, namely a 0 th generation population, by adopting a three-layer coding mode of a procedure ordering sequence, a machine selecting sequence and a mould selecting sequence, wherein one scheduling solution is a chromosome, P is an integer which is more than or equal to 100 and can be divided by 100, the P chromosomes are numbered sequentially according to 1-P, and the chromosome with the number P is called a P chromosome, and p=1, 2, … and P; each chromosome comprises three layers of sequences, namely a process sequencing sequence, a machine selection sequence and a mould selection sequence, wherein the lengths of the process sequencing sequence, the machine selection sequence and the mould selection sequence are respectively set to be Q,each gene of the sequence represents a procedure, the value of each gene corresponds to a job number, and J exists in the sequence i Job number, J of the ith job i The job number of the ith job sequentially represents the 1 st to the Ji th working procedures of the ith job according to the left-to-right sequence, namely when the ith appears from left to right for the j th time in the working procedure sequencing sequence, the occurrence number is j, and the occurrence number corresponds to the j th working procedure of the ith job; in the machine selection sequence, the value of each gene is a machine number, each gene corresponds to a procedure, namely, the value of each gene is a machine number capable of processing the procedure corresponding to the machine number, the value of each gene can be found in the selectable machine set of the procedure corresponding to the machine number, and the genes of the machine selection sequence and the procedure O 11 ,O 12 …O 1J1 O 21 ,O 22 …O 2J2 …O I1 ,O I2 …O IJI One-to-one correspondence is made from left to right in sequence; in the die selection sequence, the value of each gene is a die number, each gene corresponds to a procedure, namely, the value of each gene is a die number capable of processing the procedure corresponding to the die number, the value of each gene can be found in a selectable die set of the procedure corresponding to the die number, and the genes of the die selection sequence and the procedure O 11 ,O 12 …O 1J1 O 21 ,O 22 …O 2J2 …O I1 ,O I2 …O IJI One-to-one correspondence is made from left to right in sequence; in the running process of the genetic algorithm, the current iteration coefficient is marked as t, when the t iteration is carried out, the sequence of the sequence ordering the p-th chromosome in the t-1 generation population is updated according to the genetic algorithm to obtain the sequence of the sequence ordering the p-th chromosome in the t.1 generation population, and the machine selection sequence and the mold selection sequence of the p-th chromosome in the t-1 generation population are updated by adopting the following methods to obtain the machine selection sequence and the mold selection sequence of the p-th chromosome in the t.1 generation population:
step one: setting an iteration variable q, initializing q, and enabling q=1; the mold with the mold number m is called a mold m; setting the number of times of use of the die m as MN m Let its initial value be 0; setting the machine number of the machine where the die m is currently located as MA m Let its initial value be 0, MA m A value of 0 indicates that the die m is not used by any machine; setting the current end use time of the die m as ME m Let its initial value be 0, ME m A value of 0 indicates that die m is not currently being used on any machine; the machine with machine number c is called machine c, c=1, … a, and the mold number of the mold currently used by machine c is set to AM c Let its initial value be 0, AM c When 0, it means that machine c is not currently using any mold; setting the current machining end time of machine c to AE c Let its initial value be 0; setting the current process completion time of the operation i to be OE i Let its initial value be 0;
step two: determining corresponding operation and procedure according to the value of the q-th gene of the procedure selection sequence of the p-th chromosome of the t-1 generation population and the occurrence frequency of the value in the procedure selection sequence of the p-th chromosome of the t-1 generation population, then finding the gene corresponding to the procedure in the machine selection sequence of the p-th chromosome of the t-1 generation population and the gene corresponding to the procedure in the mould selection sequence, and updating the two found genes, wherein the specific updating process is as follows:
s2.1, determining the job number of the job corresponding to the q-th gene and the process number of the corresponding process, marking the job number as f, marking the process number as g, and determining the machine selection sequence The position of the found gene is designated as R, namely the R-th gene from left to right of the machine selection sequence, the position of the found gene in the mold selection sequence is determined, the position of the found gene is designated as R, namely the R-th gene from left to right of the mold selection sequence, the machine number of the machine corresponding to the R-th gene of the machine selection sequence is designated as n, namely the machine corresponding to the R-th gene of the machine selection sequence is designated as machine n, the mold number of the mold corresponding to the R-th gene of the mold selection sequence is designated as e, namely the mold corresponding to the R-th gene of the mold selection sequence is designated as mold e, and the process O is determined fg Is A fg And the optional die set is M fg ；
S2.2, checking the mould number AM of the mould currently used by the machine n n If 0, turning to S2.3, otherwise, turning to S2.4;
s2.3, comparison in Process O fg Is a selectable mould set M fg The number of the use times of each die is recorded as M if only one die with the minimum use times exists min If there are a plurality of dies with the smallest number of times of use, the corresponding die changing times of the dies are compared, and if there is only one die with the smallest die changing time, the die number of the die is marked as M min If there are a plurality of dies with the minimum die changing time, one of the dies is randomly selected, and the die number of the die is designated as M min The method comprises the steps of carrying out a first treatment on the surface of the Randomly generating a probability factor pi by adopting a random function, wherein pi is a random number between 0 and 1; if pi>0.5, updating the value of the R gene of the die selection sequence of the p chromosome of the t-1 generation population to M min Otherwise, the value of the R gene of the mould selection sequence of the p chromosome of the t-1 generation population is kept unchanged; the value of the R gene of the die selection sequence of the p chromosome of the t-1 generation population at this time is designated as R min The method comprises the steps of carrying out a first treatment on the surface of the By using dies R min Number of die uses MN Rmin The sum of the current value of (1) added to the updated mold R min Number of die uses MN Rmin Is used to update the die R with n min Machine number MA of the machine currently located Rmin Is a value of (2); mold R min Is the current end of use time ME of (2) Rmin Update the value of the current value to the MT Rmin AT (automatic Transmission) fgn And (3) summing; the current processing end time AE of machine n n The value of (2) and the current completion time OE of the job f f The value of (a) is updated to be the current use end time ME of the die e e Is the most recent value of (2); die number AM of the die currently used for machine n n The value of (2) is updated to M min The method comprises the steps of carrying out a first treatment on the surface of the Then turning to the third step;
s2.4, checking AM n Whether or not in the process O fg Is a selectable mould set M fg If yes, go to S2.5, otherwise, copy procedure O fg Optional machine set A of (2) fg This was designated A' fg And turning to S2.6;
s2.5, checking the mould AM n Machine number MA of the current machine of (a) AMn If equal to n, updating the value of the R gene of the die selection sequence of the p chromosome of the t-1 generation population to AM n Comparing the current process completion time OE of the operation f f Value of machine n and current machining end time AE n If the values of (2) are equal, the current process completion time OE of the operation f is directly set to f Assignment of values to Process O fg Start time T of (1) fg If not, assigning a larger value to step O fg Start time T of (1) fg Then, the mold AM n Number of die uses MN AMn Updates the value of (2) to the sum of the current value and 1, and sets the mold AM n Machine number MA of the current machine of (a) AMn The value of (2) is updated to n; mold AM n Is the current end of use time ME of (2) AMn The value of (2) is updated to T fg +AT fgn The method comprises the steps of carrying out a first treatment on the surface of the The current processing end time AE of machine n n The current process completion time OE of the value of (a) and the job f f Is updated to the current end of use time ME of the die AMe AMn Is the most recent value of (2); and turning to the third step; if n is not equal, copy Process O fg Optional machine set A of (2) fg Is marked as A' fg And turning to S2.6;
s2.6 at A' fg Middle removingExcept for the number n of machine n, check at this time A' fg Whether the machine is an empty set, if so, adopting an optional machine set A fg Updating the current set of selectable machines A' fg Then, executing the step S2.7 and then turning to the step S2.3, otherwise, directly executing the step S2.7 and then turning to the step S2.2;
s2.7 randomly generating a probability factor pi1 by using a random function, wherein pi1 is a random number between 0 and 1, if pi1>0.5, then compare in procedure O fg Optional machine set M of (1) fg If there is only one machine with the smallest current finishing time, selecting the machine, and recording the value of the machine number of the machine as A min If there are a plurality of machines with the smallest current processing end time, one of the machines is randomly selected, and the value of the machine number of the machine is recorded as A min Thereafter, the value of the R-th gene of the machine selection sequence of the p-th chromosome of the t-1 th generation population is updated to A min The method comprises the steps of carrying out a first treatment on the surface of the If pi1 is less than or equal to 0.5, the value of the R gene of the machine selection sequence of the p-th chromosome of the t-1 generation population is kept unchanged;
step three: judging whether the current value of Q is equal to Q or not, if not, adopting the sum of the current value of Q and 1 to update the value of Q, returning to the second step for the next iteration, and if so, finishing all gene updating of the p chromosome of the t-1 generation population to obtain a machine selection sequence and a mold selection sequence of the p chromosome in the t.1 generation population.
When the machine selection sequence and the die selection sequence of the p chromosome in the t.1 th generation population of the genetic algorithm chromosome adjustment method applied to the factory intelligent scheduling are adopted, the machine selection sequence and the die selection sequence are combined with the sequence of the p chromosome in the t.1 th generation population, so that the p chromosome in the t.1 th generation population is obtained; after all the P chromosomes of the t-1 generation population are updated, the P chromosomes of the t.1 generation population are obtained. At this time, the finishing time of each chromosome in the t.1 th generation population is calculated by adopting a finishing time calculation method in a genetic algorithm, the inverse of the finishing time of each chromosome in the t.1 th generation population is used as the fitness thereof, the P chromosomes of the t.1 th generation population are ordered according to the sequence from big to small of the fitness, the P chromosomes of the t.1 th generation population after the ordering are selected to be the better chromosome group, and the P chromosomes of the middle P are selected to be the common chromosome group; randomly generating probability factors pi2 by adopting a random function, wherein pi2 is a random number between 0 and 1, randomly selecting one chromosome in a preferred chromosome group if pi2 is less than 0.65, randomly selecting one chromosome in a common chromosome group if pi2 is more than or equal to 0.65, and selecting P by 80 percent together
Repeatedly generating pi2 in each selection, selecting according to the latest value of pi2, combining the selected chromosomes in pairs in sequence according to a selection order after P.80% of the selections are completed to obtain P.40% group chromosomes, finally carrying out cross treatment on each group of chromosomes to generate P.40% sub-generation chromosomes, and then carrying out mutation on each sub-generation chromosome to obtain P.40% variant offspring chromosomes, wherein P.40% sub-generation chromosomes, P.40% variant offspring chromosomes and P.20% chromosomes of the first P. t.1 generation group after sequencing jointly form a t generation group in the current genetic algorithm; and continuing the next iteration on the t generation population according to the method, and after the iteration reaches the maximum iteration number set by the current genetic algorithm, obtaining a sequence, a machine sequence and a die sequence corresponding to the chromosome with the minimum adaptability in the population generated by the last generation iteration, namely, obtaining a better solution of the scheduling problem of the double-resource flexible workshop based on die loading and unloading.
In order to verify the effect of the genetic algorithm chromosome adjustment method applied to factory intelligent scheduling of the present invention, the present invention provides a problem example and an originally generated chromosome, and the original chromosome is adjusted according to the process insertion method (original method), the machine insertion method modified for the problem, the mold insertion method, and the genetic algorithm chromosome adjustment method applied to factory intelligent scheduling of the present invention, respectively, and for effective explanation, probability factors in the execution steps of the present invention and other methods are removed, and all the steps are executed according to the situation that the probability factor is > 0.5.
The following is one example of this problem: table one providesIs Job information including process information of each Job and machine set and mold set usable for each process, such as 1 st process O of Job1 in the first column of Job1 (Job 1) 11 The machine set that can be used is {1,2}, the mold set is {1,3}, like column 1 of Job2 (Job 2) represents process 1 of Job2, process 1, O 21 The machine set that can be used is {1,4}, the mold set is {2,3}, like column 1 of Job3 (Job 3) represents process 1 of Job3, process O 31 The machine set that can be used is {2,3}, and the mold set is {2,3 }. Table II provides information on the processing time of each process on each machine, e.g. row O 11 The first step of the first operation is shown as a machining time period of 3 on the machine 1, and machining cannot be performed on the machine 3. Table three provides information on the mold change duration of the mold, such as the mold 1 requiring a mold change time of 0.5.
Table 1 job information
TABLE 2 processing time of each step on each machine
TABLE 3 mold change time required for each mold
The following is one original chromosome generated from this problem: if the first step of operation 1 selects the A1 machine and the M1 mold for processing, the first step of operation 2 selects the A1 machine and the M2 mold for processing.
TABLE 4 original chromosome
The following are brief descriptions of the other three insertion methods:
TABLE 5 description of Process insertion method, machine insertion method, mold insertion method
Fig. 1 to 4 are diagrams showing the final effect of the original chromosome after the original chromosome is adjusted by using the conventional process insertion method, the machine insertion method and the mold insertion method modified for the problem, and the chromosome adjustment method applied to the intelligent scheduling of the factory according to the present invention, according to the scheme represented by the adjusted chromosome. Each module in each final effect Gantt chart marks the corresponding procedure, machine and mould gene codes, and represents the coding scheme of the original chromosome, wherein the first number is the operation number, and the number of the operation number represents the number of the procedure of the operation; the second number is the machine number, representing the process being performed on the machine; the third number is the mold number and represents the mold used in this process. If there is a number above the gene code, the position of the number represents the corresponding procedure, machine and mould gene code, and the number represents the actual code of the gene after the gene is adjusted according to the method. The black part in front of the module is the mold changing occupied time. In fig. 1, the original gene 342 can be completely processed at time 3 without the influence of the mold, but after considering the mold factors, the mold 2 is used for both the gene 212 and the gene 122, and the mold changing time and the use time required for the gene 342 are 6, so that the gene 342 cannot be smoothly inserted in front, and the maximum finishing time is greatly prolonged. In FIG. 2, the original code 342 gene prioritizes the machine, is tuned to machine 3 for processing, and the mold is tuned so that the process does not need to wait until the original code 122 gene releases the mold before processing; the original gene with code 331 adjusts the die, saving the die changing time of the die 3. However, as can be seen from fig. 2, although the machine insertion method saves the mold changing time to some extent, the mold changing time is omitted to a small extent because the machine processing time is prioritized. In fig. 3, it can be observed that this method greatly saves the die changing time by only looking at the number of die changes on the machine 3, but it has a disadvantage that if the die is not selected for use in the early stage, it is difficult to use the die again for operation, resulting in a great reduction in the die use rate. Meanwhile, since it is not checked whether the last mold used by the selected machine is used later on by other machines, the gene originally encoded as 243 replaces the mold gene according to the principle, but since the gene originally encoded as 314 occupies the mold 1, the completion time of the process is delayed. In fig. 4, the main cause of the working interval is the processing sequence interval, and the interval due to the use of the mold is greatly reduced.
Meanwhile, by observing four final effect Gantt charts in fig. 1-4, we can see that the chromosome adjustment method of the genetic algorithm applied to factory intelligent scheduling of the invention greatly reduces the maximum finishing time compared with the process inserting method and the machine inserting method, and simultaneously avoids a plurality of complicated die changing, compared with the die inserting method, the maximum finishing time of the two methods is the same in the embodiment, but the die use of the invention is more reasonable, and meanwhile, the problem that the die is difficult to add and use because the die of the die inserting method is not selected in the earlier stage is avoided.

Claims (1)

1. When a genetic algorithm is used for solving the problem of scheduling a double-resource flexible workshop based on die loading and unloading, the total number of operations to be processed in the problem is recorded as I, the I operations are numbered according to 1-I in sequence, the operation with the number of I is called as operation I, i=1, 2,3, … and I, and the total number of the operations of the operation I is recorded as J i The j-th step of the operation i is referred to as step O ij ，j＝1，2，…，J i The method comprises the steps of carrying out a first treatment on the surface of the The total number of machines capable of processing the I operations is recorded as A, and the machine numbers of the A machines are set to be 1,2,3, … and A in sequence; the total number of the dies which can be used in the I operations is recorded as M, and the die numbers of the M dies are set to be 1,2,3, … and M in sequence; the mold change time of the mth mold is recorded as MT m M=1, 2,3, …, M, process O will be able to be handled ij Is denoted as L ij Will be able to handleTreatment Process O ij The set of machine numbers of all machines of (a) is called process O ij Is to process step O ij The set of alternative machines is denoted as A ij ，A ij ＝{A ij1 ，A ij2 ，…，A ijLij }, wherein A ij1 ，A ij2 ,……,A ijLij Respectively represent the treatable process O ij Machine number of the machine of (2); step O ij Optional machine set A of (2) ij The corresponding set of processing times is denoted as AT ij ，AT ij ＝{AT ij1 ，AT ij2 ，…，AT ijLij (wherein A) ij Machine number and AT in (a) ij One-to-one correspondence of processing time of AT ij Each processing time in (a) represents use A ij Machine processing step O of the corresponding machine number ij The required processing time; process step O can be performed ij The total number of all dies is denoted as H ij Will enable processing procedure O ij The set of die numbers for all dies of (a) is referred to as process O ij Optional die set of (a), process O ij Is denoted as M ij ，M ij ＝{M ij1 ，M ij2 ，…，M ijHij M is }, where M ij1 ，M ij2 ,…,M ijHij Respectively represent the treatable process O ij Is a mold number of a mold; generating an initial population containing P scheduling solutions, namely a 0 th generation population, by adopting a mixed strategy of global selection, local selection and random selection and adopting a three-layer coding mode of a procedure ordering sequence, a machine selection sequence and a mould selection sequence, wherein one scheduling solution is a chromosome, P is an integer which is more than or equal to 100 and can be divided by 100, the P chromosomes are numbered sequentially according to 1-P, and the chromosome with the number P is called a P chromosome, and p=1, 2, … and P; each chromosome comprises three layers of sequences, namely a process sequencing sequence, a machine selection sequence and a mould selection sequence, wherein the lengths of the process sequencing sequence, the machine selection sequence and the mould selection sequence are respectively set to be Q, Each gene of the sequence represents a procedure, the value of each gene corresponds to a job number, and J exists in the sequence i Job number, J of the ith job i The job number of the ith job sequentially represents the 1 st to the Ji th working procedures of the ith job according to the left-to-right sequence, namely when the ith appears from left to right for the j th time in the working procedure sequencing sequence, the occurrence number is j, and the occurrence number corresponds to the j th working procedure of the ith job; in the machine selection sequence, the value of each gene is a machine number, each gene corresponds to a procedure, namely, the value of each gene is a machine number capable of processing the procedure corresponding to the machine number, the value of each gene can be found in the selectable machine set of the procedure corresponding to the machine number, and the genes of the machine selection sequence and the procedure O 11 ,O 12 …O 1J1 O 21 ,O 22 …O 2J2 …O I1 ,O I2 …O IJI One-to-one correspondence is made from left to right in sequence; in the die selection sequence, the value of each gene is a die number, each gene corresponds to a procedure, namely, the value of each gene is a die number capable of processing the procedure corresponding to the die number, the value of each gene can be found in a selectable die set of the procedure corresponding to the die number, and the genes of the die selection sequence and the procedure O 11 ,O 12 …
O 1J1 O 21 ,O 22 …O 2J2 …O I1 ,O I2 …O IJI One-to-one correspondence is made from left to right in sequence; in the running process of a genetic algorithm, the current iteration coefficient is marked as t, and when the t iteration is carried out, the sequence of the p-th chromosome in the t-1 generation population is updated according to the genetic algorithm to obtain the sequence of the p-th chromosome in the t.1 generation population, and the method is characterized in that the machine selection sequence and the die selection sequence of the p-th chromosome in the t-1 generation population are updated by adopting the following methods to obtain the machine selection sequence and the die selection sequence of the p-th chromosome in the t.1 generation population:
step one: setting an iteration variable q, initializing q, and enabling q=1; the mold with the mold number m is called a mold m; setting the number of times of use of the die m as MN m Let its initial value be 0; setting the machine number of the machine where the die m is currently located as MA m Let its initial value be 0, MA m A value of 0 indicates that the die m is not used by any machine; setting the current end use time of the die m as ME m Let its initial value be 0, ME m A value of 0 indicates that die m is not currently being used on any machine; the machine with machine number c is called machine c, c=1, … a, and the mold number of the mold currently used by machine c is set to AM c Let its initial value be 0, AM c When 0, it means that machine c is not currently using any mold; setting the current machining end time of machine c to AE c Let its initial value be 0; setting the current process completion time of the operation i to be OE i Let its initial value be 0;
step two: determining corresponding operation and procedure according to the value of the q-th gene of the procedure selection sequence of the p-th chromosome of the t-1 generation population and the occurrence frequency of the value in the procedure selection sequence of the p-th chromosome of the t-1 generation population, then finding the gene corresponding to the procedure in the machine selection sequence of the p-th chromosome of the t-1 generation population and the gene corresponding to the procedure in the mould selection sequence, and updating the two found genes, wherein the specific updating process is as follows:
s2.1, determining the operation number of the operation corresponding to the q-th gene and the process number of the corresponding process, wherein the operation number is denoted as f, the process number is denoted as g, the position of the gene found in the machine selection sequence is determined as R, namely the gene is the R-th gene from left to right in the machine selection sequence, the position of the gene found in the mold selection sequence is determined as R, namely the R-th gene from left to right in the mold selection sequence, the machine number of the machine corresponding to the R-th gene in the machine selection sequence is denoted as n, namely the machine corresponding to the R-th gene in the machine selection sequence is denoted as n, the mold number of the mold corresponding to the R-th gene in the mold selection sequence is denoted as e, namely the mold corresponding to the R-th gene in the mold selection sequence is denoted as e, and the process O fg Is A fg And the optional die set is M fg ；
S2.2, check machine n currentMould number AM of the mould used n If 0, turning to S2.3, otherwise, turning to S2.4;
s2.3, comparison in Process O fg Is a selectable mould set M fg The number of the use times of each die is recorded as M if only one die with the minimum use times exists min If there are a plurality of dies with the smallest number of times of use, the corresponding die changing times of the dies are compared, and if there is only one die with the smallest die changing time, the die number of the die is marked as M min If there are a plurality of dies with the minimum die changing time, one of the dies is randomly selected, and the die number of the die is designated as M min The method comprises the steps of carrying out a first treatment on the surface of the Randomly generating a probability factor pi by adopting a random function, wherein pi is a random number between 0 and 1; if pi>0.5, updating the value of the R gene of the die selection sequence of the p chromosome of the t-1 generation population to M min Otherwise, the value of the R gene of the mould selection sequence of the p chromosome of the t-1 generation population is kept unchanged; the value of the R gene of the die selection sequence of the p chromosome of the t-1 generation population at this time is designated as R min The method comprises the steps of carrying out a first treatment on the surface of the By using dies R min Number of die uses MN Rmin The sum of the current value of (1) added to the updated mold R min Number of die uses MN Rmin Is used to update the die R with n min Machine number MA of the machine currently located Rmin Is a value of (2); mold R min Is the current end of use time ME of (2) Rmin Update the value of the current value to the MT Rmin AT (automatic Transmission) fgn And (3) summing; the current processing end time AE of machine n n The value of (2) and the current completion time OE of the job f f The value of (a) is updated to be the current use end time ME of the die e e Is the most recent value of (2); die number AM of the die currently used for machine n n The value of (2) is updated to M min The method comprises the steps of carrying out a first treatment on the surface of the Then turning to the third step;
s2.4, checking AM n Whether or not in the process O fg Is a selectable mould set M fg If yes, go to S2.5, otherwise, copy procedure O fg Optional machine set A of (2) fg This was designated A' fg And turn and convertTo S2.6;
s2.5, checking the mould AM n Machine number MA of the current machine of (a) AMn If equal to n, updating the value of the R gene of the die selection sequence of the p chromosome of the t-1 generation population to AM n Comparing the current process completion time OE of the operation f f Value of machine n and current machining end time AE n If the values of (2) are equal, the current process completion time OE of the operation f is directly set to f Assignment of values to Process O fg Start time T of (1) fg If not, assigning a larger value to step O fg Start time T of (1) fg Then, the mold AM n Number of die uses MN AMn Updates the value of (2) to the sum of the current value and 1, and sets the mold AM n Machine number MA of the current machine of (a) AMn The value of (2) is updated to n; mold AM n Is the current end of use time ME of (2) AMn The value of (2) is updated to T fg +AT fgn The method comprises the steps of carrying out a first treatment on the surface of the The current processing end time AE of machine n n The current process completion time OE of the value of (a) and the job f f Is updated to the current end of use time ME of the die AMe AMn Is the most recent value of (2); and turning to the third step; if n is not equal, copy Process O fg Optional machine set A of (2) fg Is marked as A' fg And turning to S2.6;
s2.6 at A' fg The number n of machine n is removed and A 'is checked at this time' fg Whether the machine is an empty set, if so, adopting an optional machine set A fg Updating the current set of selectable machines A' fg Then, executing the step S2.7 and then turning to the step S2.3, otherwise, directly executing the step S2.7 and then turning to the step S2.2;
s2.7 randomly generating a probability factor pi1 by using a random function, wherein pi1 is a random number between 0 and 1, if pi1>0.5, then compare in procedure O fg Optional machine set M of (1) fg If there is only one machine with the smallest current finishing time, selecting the machine, and recording the value of the machine number of the machine as A min If there are a plurality of current processing end timesThe smallest machine is selected randomly, and the value of the machine number of the machine is marked as A min Thereafter, the value of the R-th gene of the machine selection sequence of the p-th chromosome of the t-1 th generation population is updated to A min The method comprises the steps of carrying out a first treatment on the surface of the If pi1 is less than or equal to 0.5, the value of the R gene of the machine selection sequence of the p-th chromosome of the t-1 generation population is kept unchanged;
step three: judging whether the current value of Q is equal to Q or not, if not, adopting the sum of the current value of Q and 1 to update the value of Q, returning to the second step for the next iteration, and if so, finishing all gene updating of the p chromosome of the t-1 generation population to obtain a machine selection sequence and a mold selection sequence of the p chromosome in the t.1 generation population.
CN202310373396.3A 2023-04-10 2023-04-10 Genetic algorithm chromosome adjustment method applied to factory intelligent scheduling Pending CN116579541A (en)

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Publication number Priority date Publication date Assignee Title
CN116748447A (en) * 2023-08-16 2023-09-15 武汉新威奇科技有限公司 Quick die changing method and system for full-automatic forging production line

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116748447A (en) * 2023-08-16 2023-09-15 武汉新威奇科技有限公司 Quick die changing method and system for full-automatic forging production line
CN116748447B (en) * 2023-08-16 2023-10-13 武汉新威奇科技有限公司 Quick die changing method and system for full-automatic forging production line

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