CN108460463B - High-end equipment assembly line production scheduling method based on improved genetic algorithm - Google Patents
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Abstract
The invention provides a high-end equipment assembly line production scheduling method based on an improved genetic algorithm. The method comprises the following steps: generating a first population based on the initial parameters; calculating fitness values for the chromosomes based on each chromosome in the first population to determine an optimal solution; acquiring iteration times, and comparing the iteration times with an iteration threshold; if the iteration times are smaller than the iteration threshold, iterating the first population by using a preset algorithm to determine a second population and an optimal solution of each chromosome in the second population; and if the iteration times are larger than or equal to the iteration threshold, outputting the optimal solution. Therefore, in the embodiment, the problem of assembly line production scheduling of multiple machines in each process is researched, and an approximately optimal solution of the problem is obtained, so that production resources are saved to the maximum extent, the processing time is reduced, the production efficiency is improved, and the core competitiveness of enterprises is improved.
Description
Technical Field
The invention relates to the technical field of production scheduling, in particular to a high-end equipment assembly line production scheduling method based on an improved genetic algorithm.
Background
The assembly line scheduling problem is a typical production scheduling problem and is widely applied to the production and processing process of manufacturing high-end equipment such as ship and ocean engineering equipment, rail transit equipment, aerospace equipment, intelligent manufacturing equipment and the like. In the process of scheduling in the production line, workpieces to be processed need to be distributed to different machines for production, each workpiece must sequentially pass through the same procedure, and the scheduling task is to determine the distribution condition of different workpieces on the machines and the processing sequence of the workpieces distributed on the same machine.
With the increasing complexity of production and the increasing urgency of resources, the optimization objective of the scheduling problem is becoming diversified, which mainly includes minimizing the manufacturing span time, minimizing the processing cost, minimizing the cost of human resources, etc. The pipeline production scheduling problem is more complex than the general production scheduling problem due to the limitation of a specific production process sequence. In practice, in order to improve the production efficiency, there may be a plurality of machines in one process, and these machines process the workpiece at the same time. That is, in the same process, the machine needs to be allocated to the workpiece to be processed additionally, which makes the actual production scheduling more complex than the traditional flow line production scheduling.
In addition, the related art proves that if more than three machines are included in the flow line production scheduling process, the production scheduling process belongs to the NP-Hard problem, and because the complexity of the NP-Hard problem is higher, the solution mode in the related art is to adopt a meta-heuristic algorithm to specifically analyze and research specific conditions, so that an approximate optimal solution is obtained within reasonable time. For example, a scholars uses a hybrid algorithm of a particle swarm algorithm, a genetic algorithm and a simulated annealing algorithm to solve the traditional pipeline production scheduling problem. When the problem of replacement pipeline scheduling with limited intermediate storage space is solved, a scholars provides a mixed differential evolution algorithm based on an LOV rule, and experimental results show good convergence effect and robustness of the algorithm. However, in some specific problems, the genetic algorithm has the defects of slow convergence speed and easy falling into local optimization.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a high-end equipment assembly line production scheduling method based on an improved genetic algorithm, which is used for solving the technical problems in the related art.
In a first aspect, an embodiment of the present invention provides a high-end equipment pipeline production scheduling method based on an improved genetic algorithm, where the method includes:
generating a first population based on the initial parameters;
calculating fitness values for the chromosomes based on each chromosome in the first population to determine an optimal solution;
acquiring iteration times, and comparing the iteration times with an iteration threshold;
if the iteration times are smaller than the iteration threshold, iterating the first population by using a preset algorithm to determine a second population and an optimal solution of each chromosome in the second population;
and if the iteration times are larger than or equal to the iteration threshold, outputting the optimal solution.
Optionally, the iterating the first population by using a preset algorithm includes:
executing a selection operator, selecting a first number of chromosomes from the first population to form a selection population and determining the chromosome with the maximum fitness value in the selection population, wherein other chromosomes in the first population form a residual population;
executing a crossover operator on the selected population, and updating the selected population;
executing a mutation operator on the selected population, and updating the selected population;
executing a path reconnection operator on the selected population to update the selected population;
gathering a second number of chromosomes selected from the residual population, the chromosome with the maximum fitness value in the selected population and the selected population updated finally to form a second population;
and calculating the fitness value of each chromosome in the second population, and outputting the minimum fitness value and the chromosome corresponding to the minimum fitness value.
Optionally, calculating fitness values for the chromosomes based on each chromosome in the first population to determine an optimal solution comprises:
forming a coding matrix of the workpiece to be processed according to the coding rule;
determining a corresponding chromosome according to the coding matrix;
producing according to the assignment of the workpiece to be processed to one of the machines of the first procedure;
calculating the completion time of each workpiece on the first procedure according to the working processing time matrix;
determining the completion time of each machine in the first procedure;
based on each process after the second process:
according to the non-decreasing sequencing of the corresponding finishing time of the previous working procedure, a processing sequence of the workpiece is obtained;
determining a production machine corresponding to each workpiece according to the coding matrix and the processing sequence;
calculating the completion time of each machine on the current working procedure according to the working processing time matrix;
determining the finishing time of each workpiece in the current working procedure;
calculating the manufacturing span time of each workpiece after all the workpieces are processed in all the procedures;
calculating an fitness value of a chromosome corresponding to each workpiece based on the manufacturing span time.
Optionally, executing a selection operator, selecting a first number of chromosomes from the first population to form a selected population, comprises:
calculating the fitness value of each chromosome in the first population, and taking the fitness value as F;
determining the chromosome with the largest fitness value;
calculating the selection probability of each chromosome;
calculating the cumulative selection probability of each chromosome;
a plurality of random decimals are generated, and a first number of chromosomes is selected to form a selected population.
Optionally, performing a crossover operator on the selected population, and updating the selected population includes:
step 1: let the probability of hybridization pc＝0.5，k＝1；
Step 2: using the selected population as the current population, and generating a [0, 1] for the kth chromosome of the current population]Random number rand ofk；
And step 3: judgment randk＜pcWhether the chromosome is established or not, if so, selecting the kth chromosome; otherwise, executing step 4;
and 4, step 4: step 2 is executed until all chromosomes of the current population are traversed, and finally K chromosomes are selected as cross population crosstop; if K is an odd number, randomly picking out a chromosome from the parent and adding the chromosome into the cross population crossPop;
and 5: making m equal to 1, and randomly generating an integer r of [1, NxP-1 ];
step 6: exchanging all genes on the right of the r gene position of the m & ltth & gt and m +1 & gt chromosomes in the cross population crossspop to generate two offspring, and replacing the m & ltth & gt and m +1 & gt chromosomes in the current population;
and 7: repeating step 6 until all chromosomes in the cross population crosstop are traversed by making m ═ m + 2;
and 8: and returning the updated current population as a new selected population.
Optionally, performing a mutation operator on the selected population, updating the selected population comprising:
step 1: let the probability of variation pm＝0.2，i＝1，j＝1；
Step 2: selecting a population selectPop generated by the crossover operator as a current population;
and step 3: generating a [0, 1] for the jth gene locus of the ith chromosome of the current population]Random number rand ofk；
And 4, step 4: judgment randk＜pmIf true, generate a [1, P ]]The random number r is not equal to the integer of the original gene position, and the original gene position is replaced by the random number r; otherwise, executing step 5;
and 5: judging whether j is greater than or equal to PxN or not by making j equal to j +1, and executing a step 6 if j is greater than or equal to PxN; if not, executing the step 3;
step 6: judging whether i is greater than or equal to selectsize or not by setting i to i +1, and executing the step 7 if the i is greater than or equal to selectsize; if not, executing the step 3;
and 7: and returning the updated current population as a new selected population selectPop.
Optionally, executing a path reconnection operator on the selected population, and updating the selected population includes:
step 1: let the initial solution be ainitGuided solution is aguideSet A ofbestSelecting a population selectPop generated by a mutation operator as a current population for a set of optimal solutions generated in the traversal process;
step 2: calculating the fitness value of each chromosome of the current population, and selecting the chromosome with the maximum fitness value as a guide solution aguideSelecting chromosome with second highest fitness value as initial solution ainit；
And step 3: assigning an initial solution to abeginI.e. abegin＝ainit；
And 4, step 4: comparison abeginAnd aguideOf the different elements, record the position DP of the different element as { r | a ═ r | ainit≠aguide,r＝1,2,…,n}；
And 5: order set AnewTraversing each position r in the set DP for the set of new solutions generated during the search, and abeginIs replaced by the guiding solution aguideThe elements in the corresponding positions in the set A, the new solution generatednewPerforming the following steps;
step 6: compute set AnewAll chromosomes in the database, and the chromosome a with the largest fitness valuebestAs new abeginAnd a isbestPut into set AbestPerforming the following steps;
and 7: replacing the chromosome with the minimum fitness value in the current population with abest；
And 8: executing the steps 4 to 7 until abeginEach element in (a) and a guided solution (a)guideAre completely the same;
and step 9: and returning the updated current population as a new selected population selectPop.
According to the technical scheme, the embodiment of the invention aims at the initial parameters to generate the first population, and then the fitness value of each chromosome in the first population is calculated to determine the optimal solution; then, obtaining iteration times, and comparing the iteration times with an iteration threshold; finally, if the iteration times are smaller than the iteration threshold, iterating the first population by using a preset algorithm to determine a second population and an optimal solution of each chromosome in the second population; and if the iteration times are larger than or equal to the iteration threshold, outputting the optimal solution. Therefore, in the embodiment, the problem of production scheduling of a high-end equipment assembly line of multiple machines in each process is researched, and an approximately optimal solution of the problem is obtained, so that production resources are saved to the maximum extent, processing time is shortened, production efficiency is improved, and core competitiveness of enterprises is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a block diagram of a method for scheduling production in a high-end equipment pipeline based on an improved genetic algorithm according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a pipeline production scheduling of a similar machine;
FIG. 3 is a flowchart illustrating a method for scheduling production of a high-end equipment assembly line based on an improved genetic algorithm according to an embodiment of the present invention;
fig. 4 is a block diagram of a high-end equipment pipeline production scheduling device based on an improved genetic algorithm according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the processing machines in each process are similar machines, that is, the speeds of the machines are the same, and the processing time is the same when the same workpiece is processed. However, different workpieces are processed at different times in the same process.
Moreover, each workpiece can only be processed by one similar machine at the same time, and each similar machine can only process one workpiece at any time.
In addition, each workpiece is not allowed to be interrupted once the machining is started, and each process has an infinite storage space. And when the completion time is calculated, the time of machine failure, cutter switching, conveying and preparation of workpieces among processes and the like is not considered.
In the embodiment of the invention, the optimization goal of the scheduling process is to solve the allocation condition of the machine of the workpiece in each process and the processing sequence of the workpiece, and minimize the manufacturing span time Cmax。
Fig. 1 is a flowchart of a method for scheduling production of a high-end equipment assembly line based on an improved genetic algorithm according to an embodiment of the present invention. Fig. 2 is a schematic diagram of the pipeline production scheduling of the similar machine. Fig. 3 is a flowchart of a method for scheduling production of a high-end equipment assembly line based on an improved genetic algorithm according to an embodiment of the present invention. Referring to fig. 1 to 3, the pipeline scheduling method includes:
first, introduction 101, a step of generating a first population based on initial parameters.
In the present embodiment, the number of workpieces N, the number of processes P, the number of machines M in each process, and the machining time of the workpiece in each process are input. Initializing algorithm parameters including population size popsize, iteration number runtime and cross probability pcAnd the mutation probability PmLet the iteration number run be 1.
The high-end equipment assembly line production scheduling method based on the improved genetic algorithm is realized based on the genetic algorithm and the path reconnection algorithm. Based on the initial parameters, a first population may be generated according to the improved genetic algorithm described above.
Next, a step of calculating fitness values for said chromosomes based on each chromosome in said first population to determine an optimal solution is introduced 102.
In this embodiment, an initial population Pop is randomly generated by combining the encoding rule, a first data (popsize) chromosome is obtained, and a manufacturing span time C of each chromosome is calculatedmax。
Span time C of production according to each chromosomemaxAnd formulas
An fitness value for the chromosome is calculated.
Then, run is updated, that is, run +1 is set.
Thirdly, introducing 103, obtaining the iteration times, comparing the iteration times with the iteration threshold, and 105, and outputting the optimal solution if the iteration times is more than or equal to the iteration threshold.
In this embodiment, an iteration number run is obtained, and the iteration number run is compared with an iteration threshold runtime.
104, if the iteration times are smaller than the iteration threshold, iterating the first population by using a preset algorithm to determine a second population and an optimal solution of each chromosome in the second population;
if run is not less than runtime, the first population is iterated according to a preset algorithm, and accordingly the updated second population and the optimal solution of each chromosome in the second population are obtained.
105, if the iteration number is greater than or equal to the iteration threshold, outputting the optimal solution
If run is larger than or equal to runtime, the termination condition is met, and the optimal fitness value and the corresponding chromosome are output.
Therefore, in the embodiment, the problem of assembly line production scheduling of multiple machines in each process is researched, and an approximately optimal solution of the problem is obtained, so that production resources are saved to the maximum extent, the processing time is reduced, the production efficiency is improved, and the core competitiveness of enterprises is improved.
In an embodiment of the present invention, the calculating step of calculating the fitness value in step 102 includes steps 201 to 212. Wherein, each step is as follows:
step 201: according to the coding rule, if the set of the workpieces to be processed is J ═ J1,...,Jj,...,JnAnd (4) sequentially carrying out the same P procedures on each workpiece, wherein M machines of the same type exist in each procedure, and generating a P × N-dimensional coding matrix AP×NAs shown in formula (1). Wherein the ith row represents the ith process and the jth column represents the workpiece Jj，aijShows the workpiece J in the ith processjA processing machine ofijIs in the value range of [1, M]。
Step 202: according to the above coding rules, each coding matrix may determine a chromosome. The chromosomes are arranged in sequence according to the row sequence of the coding matrix, and the length of the chromosomes is P multiplied by N, which is shown in a formula (2).
[a11,a12,…,a1j,…,a1N,aP1,aP2,…,aPj,…,aPN]。 (2)
Step 203: the workpiece enters a first procedure and is coded according to the coding matrix AP×NAssigning a work JjTo the first step of the process a1jAnd (4) carrying out production on a machine.
Step 204: calculating the finishing time TJ of each workpiece on the first process according to the processing time matrix TimeOfPJ of the workpiecej. When a plurality of workpieces are distributed to the same machine, the workpieces are processed in order of increasing workpiece number.
Step 205: determining the completion time TPM of a kth machine in a first process1kI.e. the last tool to be worked on the machineThe finishing time of the piece.
And i is 2, and each process based on the second process comprises the following steps:
step 206: the workpieces enter the ith procedure, all the workpieces are sorted according to the completion time of the workpiece corresponding to the previous procedure in a non-decreasing manner, and the processing sequence pi of one workpiece is obtained1,π2,…,πj,…,πn}，πjRepresentative of a work JπjArranged at the j-th position in the machining sequence and machined sequentially according to the workpiece sequence.
Step 207: according to the coding matrix A of the workpieceP×NJudging the machining sequence pi ═ pi1,π2,…,πj,…,πnOn which machine each workpiece is produced.
Step 208: calculating the completion time TPM of each machine in the ith process according to the formula (3) based on the processing time matrix TimeOfPJ of the workpieceik。
Where max { x, y } denotes taking the larger of x and y
Step 209: the workpiece in the ith procedure
Has a completion time of
Order to
Step 210: step 206 is executed by making i equal to i +1 until the workpiece is finished with all the processes.
Step 211: calculating the manufacturing span time C of the workpiece according to the following equation (4)max。
Step 212: the fitness value of the chromosome was calculated according to the following formula (5).
In an embodiment of the present invention, a preset algorithm iterates a first population, where the steps are as follows:
step 301, executing a selection operator, selecting a first number of chromosomes from the first population to form a selected population, and determining a chromosome with the largest fitness value in the selected population, where other chromosomes in the first population form a remaining population, specifically including:
3011, calculate fitness values for all chromosomes in the first population, and sum to F.
3012, find out the chromosome a with the largest fitness value.
3013, the selection probability p of each chromosome is calculated according to the following formulai。
3014, the cumulative selection probability q for each chromosome is calculated according to the following formulai。
3015, randomly generating a [0, 1]]A decimal Rand of (e), if Rand is less than or equal to q1Then the first chromosome is selected. If q isk-1≤Rand≤qkThen the k-th chromosome is selected.
3016, repeat step 3015 for a total of 1 selection size, and select selection size-1 chromosome.
3017, the chromosomes in steps 3016 and 3012 are combined to form a population selectPop with selectsize bars.
Step 302, executing a crossover operator on the selected population, and updating the selected population, specifically including:
3021，let the probability of hybridization pc＝0.5，k＝1。
3022, selecting the selected population selectPop selected by the selecting operation as the current population.
3023 for the kth chromosome of the current population, a [0, 1] is generated]Random number rand ofk. Judgment randk＜pcAnd if so, selecting the kth chromosome. Otherwise, step 3024 is performed.
3024, let k be k +1, step 3022 is performed until all chromosomes of the current population are traversed. Finally, K chromosomes are selected as cross population crosstop. If K is odd, randomly picking out a chromosome from the parent and adding the chromosome into the crossPop.
3025 an integer r of [1, N × P-1] is randomly generated by setting m to 1.
3026 all genes to the right of the r gene position of the m and m +1 chromosomes in crosstop are swapped to produce two offspring and replace the m and m +1 chromosomes in the current population.
3027, let m be m +2, repeat step 3026 until all chromosomes in the crosstop have been traversed.
3028, returning the updated current population as a new selectPop.
Step 303, executing a mutation operator on the selected population, and updating the selected population, specifically including:
3031 making the mutation probability pm＝0.2，i＝1，j＝1。
3032, the selective Pop generated by the crossover operator is used as the current population.
3033 for the j gene position of the i chromosome of the current population, a [0, 1] is generated]Random number rand ofk。
3034, judging randk＜pmIf true, generate a [1, P ]]And the random number r is not equal to the integer of the original gene position, and the original gene position is replaced by the random number r. Otherwise, step 3035 is performed.
3035, let j equal to j +1, determine if j ≧ P × N is true, if true, execute step 3036; if not, go to step 3033.
3036, let i equal to i +1, determine if i is greater than or equal to selectsize, if yes, execute step 3037; if not, go to step 3033.
3037, returning the updated current population as a new selected population selectPop.
Step 304, executing a path reconnection operator on the selected population, and updating the selected population, specifically including:
3041 let the initial solution be ainitGuided solution is aguideSet A ofbestAnd taking the selectPop generated by the mutation operator as the current population for the set of optimal solutions generated in the traversal process.
3042 calculating fitness value of all chromosomes in current population, and selecting the chromosome with the highest fitness value as guide solution aguideSelecting chromosome with second highest fitness value as initial solution ainit。
3043 assigning the initial solution to abeginI.e. abegin＝ainit。
3044 comparison of abeginAnd aguideOf the different elements, record the position DP of the different element as { r | a ═ r | ainit≠aguide,r＝1,2,…,n}。
3045 order set AnewTraversing each position r in the set DP for the set of new solutions generated during the search, and abeginIs replaced by the guiding solution aguideThe elements in the corresponding positions in the set A, the new solution generatednewIn (1).
3046 calculate set AnewAll chromosomes in the database, and the chromosome a with the largest fitness valuebestAs new abeginAnd a isbestPut into set AbestIn (1).
3047 and replacing chromosome with the least fitness value in the current population by abest。
3048 go to step 3044 and 3047 until abeginEach element in (a) and a guided solution (a)guideAre identical.
3049, returning the updated current population as the new selected population selectPop.
And 305, aggregating the second number of chromosomes selected from the residual population, the chromosome with the maximum fitness value in the selected population and the selected population updated finally to form a second population.
Popsize- (1+ selectsize) chromosome is selected from the remaining population restPop in step 304, and BestCho and selectPop, which are the most adaptive chromosomes in the selected population, are combined to form a second population newPop containing popsize chromosomes.
Step 306, calculating the fitness value of each chromosome in the second population, and outputting the minimum fitness value and the chromosome corresponding to the minimum fitness value.
In this example C was calculated for all chromosomes in the newPop of the second populationmaxMinimum output of CmaxThe value and its corresponding chromosome.
Therefore, the method and the device for scheduling the production of the high-end equipment assembly line based on the similar machine are provided by the embodiment of the invention, the workpieces to be processed are firstly coded through an improved genetic algorithm, the machine allocation condition of the workpieces on each process is determined, and then the processing sequence of the workpieces is determined according to a first-in-first processing strategy, so that the manufacturing span time of the workpieces is calculated.
In the embodiment of the invention, an initial population is randomly generated, selection, crossing, variation and path reconnection operators are executed on the initial population, and the population is updated through continuous iteration so as to optimize a solution space and finally obtain an optimal solution. The improved genetic algorithm solves the problem of production scheduling of the high-end equipment assembly line based on the similar machine, saves production resources of enterprises, improves the production efficiency and the customer satisfaction of the enterprises, improves the management level of the enterprises, and has better convergence speed and stability of the optimal solution of the algorithm.
In the embodiment of the invention, the similar machines are combined with the problem of pipeline scheduling, namely, a plurality of similar machines are added in each process, so that the complexity of the problem can be improved, and the application scene can be closer to the actual situation.
In the embodiment of the invention, a matrix coding mode is adopted, so that the solution space of the problem is more intuitively and efficiently represented.
In the embodiment of the invention, the genetic algorithm is improved by utilizing the path reconnection algorithm and the elite retention strategy, so that more dominant chromosomes are generated in the iterative process, and the defects that the genetic algorithm is easy to fall into precocity, local optimization and the like are effectively improved while the population diversity is increased.
Fig. 4 is a high-end equipment pipeline production scheduling device based on an improved genetic algorithm according to an embodiment of the present invention, as shown in fig. 4, the device includes:
a first population generating module 401 for generating a first population based on the initial parameters;
an optimal solution determining module 402, configured to calculate fitness values of the chromosomes based on each chromosome in the first population to determine an optimal solution;
a comparison module 403, configured to obtain iteration times, and compare the iteration times with an iteration threshold;
an iteration module 404, configured to iterate the first population by using a preset algorithm when the iteration number is smaller than the iteration threshold, so as to determine a second population and an optimal solution for each chromosome in the second population;
an output module 405, configured to output the optimal solution when the iteration number is greater than or equal to the iteration threshold.
It should be noted that, the high-end equipment assembly line production scheduling device based on the improved genetic algorithm provided by the embodiment of the present invention is in a one-to-one correspondence relationship with the above method, and the implementation details of the above method are also applicable to the above device, and the above system is not described in detail in the embodiment of the present invention.
In the description of the present invention, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (6)
1. A high-end equipment pipeline production scheduling method based on an improved genetic algorithm is characterized by comprising the following steps:
generating a first population based on the initial parameters;
wherein the initial parameters include: inputting the number N of workpieces, the number P of processes, the number M of machines in each process and the processing time TimeOfPJ of the workpieces in each process, and initializing algorithm parameters including a population size popsize, an iteration number runtime and a cross probability PcAnd the mutation probability PmLet the iteration number run be 1;
calculating fitness values for the chromosomes based on each chromosome in the first population to determine an optimal solution;
acquiring iteration times, and comparing the iteration times with an iteration threshold;
if the iteration times are smaller than the iteration threshold, iterating the first population by using a preset algorithm to determine a second population and an optimal solution of each chromosome in the second population;
if the iteration times are larger than or equal to the iteration threshold, outputting the optimal solution;
wherein the iterating the first population using a preset algorithm comprises:
executing a selection operator, selecting a first number of chromosomes from the first population to form a selection population and determining the chromosome with the maximum fitness value in the selection population, wherein other chromosomes in the first population form a residual population;
executing a crossover operator on the selected population, and updating the selected population;
executing a mutation operator on the selected population, and updating the selected population;
executing a path reconnection operator on the selected population to update the selected population;
gathering a second number of chromosomes selected from the residual population, the chromosome with the maximum fitness value in the selected population and the selected population updated finally to form a second population;
calculating the fitness value of each chromosome in the second population, and outputting the minimum fitness value and the chromosome corresponding to the minimum fitness value;
the calculating fitness values for the chromosomes based on each chromosome in the first population to determine an optimal solution comprises:
forming a coding matrix of the workpiece to be processed according to the coding rule;
determining a corresponding chromosome according to the coding matrix;
producing according to the assignment of the workpiece to be processed to one of the machines of the first procedure;
calculating the completion time of each workpiece on the first procedure according to the working processing time matrix;
determining the completion time of each machine in the first procedure;
based on each process after the second process:
according to the non-decreasing sequencing of the corresponding finishing time of the previous working procedure, a processing sequence of the workpiece is obtained;
determining a production machine corresponding to each workpiece according to the coding matrix and the processing sequence;
calculating the completion time of each machine on the current working procedure according to the working processing time matrix;
determining the finishing time of each workpiece in the current working procedure;
calculating the manufacturing span time of each workpiece after all the workpieces are processed in all the procedures;
calculating an fitness value of a chromosome corresponding to each workpiece based on the manufacturing span time.
2. The high-end equipment pipeline production scheduling method of claim 1, wherein executing a selection operator to select a first number of chromosomes from said first population to form a selected population comprises:
calculating the fitness value of each chromosome in the first population, and taking the fitness value as F;
determining the chromosome with the largest fitness value;
calculating the selection probability of each chromosome;
calculating the cumulative selection probability of each chromosome;
a plurality of random decimals are generated, and a first number of chromosomes is selected to form a selected population.
3. The high-end equipment pipeline production scheduling method of claim 1, wherein performing a crossover operator on the selected population, updating the selected population comprises:
step 1: let the probability of hybridization pc＝0.5，k＝1；
Step 2: using the selected population as the current population, and generating a [0, 1] for the kth chromosome of the current population]Random number rand ofk；
And step 3: judgment randk＜pcWhether the chromosome is established or not, if so, selecting the kth chromosome; otherwise, executing step 4;
and 4, step 4: step 2 is executed until all chromosomes of the current population are traversed, and finally K chromosomes are selected as cross population crosstop; if K is an odd number, randomly picking out a chromosome from the parent and adding the chromosome into the cross population crossPop;
and 5: making m equal to 1, and randomly generating an integer r of [1, NxP-1 ];
step 6: exchanging all genes on the right of the r gene position of the m & ltth & gt and m +1 & gt chromosomes in the cross population crossspop to generate two offspring, and replacing the m & ltth & gt and m +1 & gt chromosomes in the current population;
and 7: repeating step 6 until all chromosomes in the cross population crosstop are traversed by making m ═ m + 2;
and 8: and returning the updated current population as a new selected population.
4. The high-end equipment pipeline production scheduling method of claim 1, wherein performing a mutation operator on the selected population, updating the selected population comprises:
step 1: let the probability of variation pm＝0.2，i＝1，j＝1；
Step 2: selecting a population selectPop generated by the crossover operator as a current population;
and step 3: generating a [0, 1] for the jth gene locus of the ith chromosome of the current population]Random number rand ofk；
And 4, step 4: judgment randk＜pmIf true, generate a [1, P ]]The random number r is not equal to the integer of the original gene position, and the original gene position is replaced by the random number r; otherwise, executing step 5;
and 5: judging whether j is greater than or equal to PxN or not by making j equal to j +1, and executing a step 6 if j is greater than or equal to PxN; if not, executing the step 3;
step 6: judging whether i is greater than or equal to selectsize or not by setting i to i +1, and executing the step 7 if the i is greater than or equal to selectsize; if not, executing the step 3;
and 7: and returning the updated current population as a new selected population selectPop.
5. The high-end equipment pipeline production scheduling method of claim 1, wherein executing a path reconnect operator on the selected population, updating the selected population comprises:
step 1: let the initial solution be ainitGuided solution is aguideSet A ofbestSelecting seeds generated by mutation operator for the optimal solution set generated in the traversal processThe group selectPop is taken as the current group;
step 2: calculating the fitness value of each chromosome of the current population, and selecting the chromosome with the maximum fitness value as a guide solution aguideSelecting chromosome with second highest fitness value as initial solution ainit；
And step 3: assigning an initial solution to abeginI.e. abegin＝ainit；
And 4, step 4: comparison abeginAnd aguideOf the different elements, record the position DP of the different element as { r | a ═ r | ainit≠aguide,r＝1,2,…,n}；
And 5: order set AnewTraversing each position r in the set DP for the set of new solutions generated during the search, and abeginIs replaced by the guiding solution aguideThe elements in the corresponding positions in the set A, the new solution generatednewPerforming the following steps;
step 6: compute set AnewAll chromosomes in the database, and the chromosome a with the largest fitness valuebestAs new abeginAnd a isbestPut into set AbestPerforming the following steps;
and 7: replacing the chromosome with the minimum fitness value in the current population with abest；
And 8: executing the steps 4 to 7 until abeginEach element in (a) and a guided solution (a)guideAre completely the same;
and step 9: and returning the updated current population as a new selected population selectPop.
6. A high-end equipment assembly line production scheduling device based on an improved genetic algorithm is characterized by comprising:
a first population generation module (401) for generating a first population based on the initial parameters;
an optimal solution determination module (402) for calculating fitness values of the chromosomes based on each chromosome in the first population to determine an optimal solution; the method comprises the following steps:
forming a coding matrix of the workpiece to be processed according to the coding rule;
determining a corresponding chromosome according to the coding matrix;
producing according to the assignment of the workpiece to be processed to one of the machines of the first procedure;
calculating the completion time of each workpiece on the first procedure according to the working processing time matrix;
determining the completion time of each machine in the first procedure;
based on each process after the second process:
according to the non-decreasing sequencing of the corresponding finishing time of the previous working procedure, a processing sequence of the workpiece is obtained;
determining a production machine corresponding to each workpiece according to the coding matrix and the processing sequence;
calculating the completion time of each machine on the current working procedure according to the working processing time matrix;
determining the finishing time of each workpiece in the current working procedure;
calculating the manufacturing span time of each workpiece after all the workpieces are processed in all the procedures;
calculating an fitness value of a chromosome corresponding to each workpiece based on the manufacturing span time;
a comparison module (403) for obtaining the number of iterations and comparing the number of iterations with an iteration threshold;
an iteration module (404) configured to iterate the first population by using a preset algorithm when the iteration number is smaller than the iteration threshold value, so as to determine a second population and an optimal solution for each chromosome in the second population; the iterating the first population using a preset algorithm includes:
executing a selection operator, selecting a first number of chromosomes from the first population to form a selection population and determining the chromosome with the maximum fitness value in the selection population, wherein other chromosomes in the first population form a residual population;
executing a crossover operator on the selected population, and updating the selected population;
executing a mutation operator on the selected population, and updating the selected population;
executing a path reconnection operator on the selected population to update the selected population;
gathering a second number of chromosomes selected from the residual population, the chromosome with the maximum fitness value in the selected population and the selected population updated finally to form a second population;
calculating the fitness value of each chromosome in the second population, and outputting the minimum fitness value and the chromosome corresponding to the minimum fitness value;
an output module (405) for outputting the optimal solution when the number of iterations is greater than or equal to the iteration threshold.
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| CN109242396B (en) * | 2018-09-30 | 2021-09-14 | 合肥工业大学 | Scheduling method and system for medical consumable inventory and replenishment collaborative optimization process |
| CN109685243B (en) * | 2018-11-05 | 2023-03-31 | 南京航空航天大学 | Method for optimizing logistics distribution path of job shop based on genetic algorithm |
| CN112990515A (en) * | 2019-12-02 | 2021-06-18 | 中船重工信息科技有限公司 | Workshop resource scheduling method based on heuristic optimization algorithm |
| CN111178529B (en) * | 2019-12-31 | 2023-01-20 | 中科曙光国际信息产业有限公司 | Data processing method and device, electronic equipment and readable storage medium |
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| CN114707432B (en) * | 2022-06-06 | 2022-10-14 | 浙江大学滨江研究院 | Forging factory intelligent scheduling method based on genetic algorithm |
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