CN106934485B - Novel one-dimensional rehearsal blanking method based on genetic algorithm - Google Patents
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Abstract
The invention discloses a large-scale one-dimensional stock layout blanking acceleration algorithm based on a genetic algorithm, which has the main core idea that the idea of genetic coding of the genetic algorithm is used for reference, the rapidity of a heuristic algorithm is considered, the utilization rate maximization of a single raw material is taken as a set target, an approximately optimal solution is sought, and the maximum utilization of the raw material is realized.
Description
Technical Field
The invention relates to the field of computer algorithm application design, in particular to a novel one-dimensional rehearsal material algorithm design based on a genetic algorithm.
Background
The problem of one-dimensional stock layout unloading is to cut a rodlike raw material into a plurality of length sub-parts, and the NP problem of improving the production utilization rate as much as possible on the premise of meeting the actual production requirement. The problem of one-dimensional blanking is widely present in many practical projects such as machinery, manufacturing, construction, electric power, and the like, and is valued by experts and scholars in various industries. In recent years, along with the strong national advocation of energy conservation and emission reduction, resource allocation optimization, rationalization of utilization improvement and scientification and synthesis of management, the aim is to seek an optimized production technical scheme to obtain the maximum benefit so as to find a reasonable balance point between economic benefit and ecological benefit and promote the steady and healthy development of national economy. Therefore, the optimal design and processing scheme is found, so that the production cost can be saved, the market competitiveness of enterprises is improved, the energy consumption can be reduced, and the harm to the environment is reduced.
A one-dimensional problem is a classical problem in combinatorial problems, which belongs to an NP-complete problem in view of complexity of its computational scale, and whose specific solution is limited to actual engineering conditions, so that an exact solution to the problem depends on its actual computational scale and engineering constraints. For the practical engineering and one-dimensional stock layout and blanking problems, the problems have certain limiting conditions, such as many constraint conditions of raw material types, raw material quantity, product types, corresponding product types and quantity, specific production order requirements and the like, a mathematical model is established for the specific problems, an optimal solution set existing in a feasible domain is found, and the solution set of the problems is quite huge, an algorithm suitable for any situation basically does not exist, and great requirements are provided for the design of the algorithm. At present, the research on the problem at home and abroad obtains almost the same conclusion. For one-dimensional stock layout blanking of a small-scale calculation model, a global optimal solution can be found in a limited feasible domain space by a certain method; however, for large-scale and ultra-large-scale calculation models, an effective method for obtaining the global optimal solution is not available at present, and the global approximately optimal solution can be obtained as far as possible only through a genetic algorithm, a heuristic algorithm and the like. The genetic algorithm is used as a common method for solving the combination problem, and can solve a part of one-dimensional blanking problems to a certain extent, but the genetic algorithm has the defects of difficult parameter configuration, incapability of finding global optimum once parameter configuration deviation falls into local optimum, long calculation time and the like; the heuristic algorithm has the advantages of high calculation speed and has the disadvantage that the calculation effect is difficult to ensure.
In order to overcome the defects of the prior art, the invention provides a novel one-dimensional rehearsal blanking algorithm design based on a genetic algorithm, and the method is suitable for many practical projects needing a large amount of bar stock consumption. The optimal design and processing scheme is found, so that the maximum cost can be saved undoubtedly, the direct economic effect can be brought to related enterprises, and the emission index of the enterprises is reduced.
Disclosure of Invention
Technical problem to be solved
In order to overcome the defects of the existing method, the invention discloses a novel one-dimensional rehearsal material algorithm design based on a genetic algorithm.
(II) technical scheme
In order to solve the problems, the invention provides a novel one-dimensional rehearsal material algorithm design based on a genetic algorithm, which comprises the following steps:
s1, sorting the input products according to different lengths and a descending method, wherein the minimum code is 1, the minimum code is sequentially pushed, and the last serial number is the minimum part.
S2, constructing a coding sequence according to the length and the number of the products, such as: (1112222233.. said.) such a code sequence means that there are 2 products and 5 products for No. 1, and 2 products for No. 3. For example, Product _ Length [1] can obtain the Length and other information of Product No. 1 (the conversion relation is built between the coding sequence and the Product information), and the multiple nested loops are successfully converted into single loops by the gene coding principle.
S3, optimizing threshold values (1, 2 and 3) in a third gear, gradually decreasing the threshold values, taking out the codes from the gene codes through single circulation, adding the codes into the accumulated variables, judging the relation between the accumulated variables/input raw material length and the set threshold values, and reaching the standard break; as a successful scan.
And S4, starting a mutation operator after the scanning operator fails (the minimum threshold target cannot be reached). Because the operator is scanned, the relation between the sequential accumulation calculation utilization rate and the threshold value is used as a cycle termination condition, and certain limitation exists. Mutation operators only need to supplement the limitation, and increase the possible types of genotypes. For example: and (3) coding genotype obtained by result scanning operator operation: 11112, the utilization can reach 93% (below the minimum scan target, trigger mutation operator).
The first step is as follows: self-exchange. The first (n-1) number is randomly selected, exchanged with the last nth number. For example, crossover 3 and 5, to give a new genotype 11211
The second step is that: the total length is used, minus the length of the previous n-1 number after the exchange, equals the new length of the residue.
Third cloth: and taking the new surplus length from the residual code library as a termination judgment condition according to the utilization rate. If the genotype after mutation (the corresponding utilization rate is better than that before mutation, which is called successful mutation), the genotype is replaced with the original genotype and retained. If the mutation fails, the pre-mutation is retained.
S5, inheriting the excellent gene code of the previous parent by using an inheritance operator as a new child gene code, so that the operation speed of the algorithm can be greatly increased. The superior gene coding of the parent may not be present in part of the gene in the product sequence coding, which is called inheritance failure. Excellent gene codes of the parents can be found in product sequence codes, and then excellent varieties can be combined.
Preferably, the novel one-dimensional rehearsal blanking algorithm design provided by the invention based on the genetic algorithm, wherein the step S1 specifically comprises the steps of sequencing the sub-parts to be processed according to the principle that the sub-parts with longer Length are arranged in front, and respectively storing the Length information and the Number information of the sequenced new sub-part sequence into corresponding arrays of Product _ Length [ ] and Product _ Length _ Number [ ]; and storing the arranged new subsequence into a Length Raw _ Material _ Length [ ] and a Number Raw _ Material _ Number [ ] of the arranged new subsequence according to the user intention and the principle that the Raw materials with high priority treatment level are arranged in the front.
Preferably, the invention provides a novel one-dimensional rehearsal algorithm design based on genetic algorithm, wherein step S2 is specifically to construct genetic coding sequence (as shown in fig. 1). The length of the raw material is set to L according to the above1,L2,L3,…，Ln-1，Ln(ii) a The corresponding quantities are: n is a radical of1,N2,N3,…，Nn-1，Nn(ii) a We use the number 1, 2.. No. n to represent the code of the corresponding raw material, and represent the number of 1 raw material in the stock according to the number of 1 occurrence in the array, so that we can build the following raw material coding sequence,
Encoding_Raw_Materials[Maximum_RM]＝{1,1,1,,2,2,2,...,n,n,n}；(1)
preferably, the invention provides a novel one-dimensional rehearsal blanking algorithm design based on a genetic algorithm, wherein the step S3 specifically includes that an algorithm solving problem is composed of double-layer loops, an outer-layer loop takes out a Raw material to be processed from Encoding _ Raw _ Materials [ ] and sends the Raw material to be processed to an iterator each time, an inner-layer loop sequentially takes out a sub-part length from Encoding _ Product [ ] and sends the sub-part length to an accumulator in the iterator, the accumulator generates judgment after superimposing the sub-part length, if a newly added sub-part allows the accumulator to overflow (exceeds the Raw material length sent to the iterator), the last superimposed sub-part is removed, and if the newly added sub-part does not allow the accumulator to overflow, the adding result is retained. And (3) entering a new sub-part into an accumulator each time, calculating whether the ratio (single utilization rate) of the accumulator to the length of the input raw material reaches a set threshold value, and if the ratio reaches a set first threshold value target, taking the result as a successful test at this time, and keeping the sub-part code added into the superimposer. And the sub-part code is rejected from Encoding _ Product [ ] and the Raw material input to the iterator also needs to be rejected from Encoding _ Raw _ Materials [ ].
If the first threshold target is not reached, the step length of the loop variable of the iterator can be modified in an attempt to adjust, if the utilization rate after adjustment can reach the second threshold or the third threshold target, the adjustment is regarded as a qualified test, and the work of refreshing the code sequence is repeated. If the condition can not be met after the step length of the loop variable in the iterator is adjusted, the mutation operator can be tried to optimize.
Preferably, the invention provides a novel one-dimensional rehearsal material algorithm design based on a genetic algorithm, wherein the step S4 specifically comprises the following three steps of mutation operator: (1) self-exchange. The first (n-1) number of gene codes was randomly selected, alternating with the last nth number. (2) Calculating the total length calculated by the number (n-1) before the newly generated gene codes, thereby obtaining the new excess material length. (3) And substituting the new excess length into an iterator of the scanning operator to carry out operation. If the combination of the mutated gene codes is better than that before the mutation, called advantageous mutation, the original genotype is replaced, if the mutation effect is not better than that of the previous gene codes, the treatment is not carried out, and the mutation operator is repeated until the proper genotype is found. If no suitable gene code is found for 10 mutations, the mutation is considered to be a failed mutation.
Preferably, the invention provides a novel one-dimensional rehearsal algorithm design based on genetic algorithm, wherein step S5 specifically includes inheriting excellent gene codes of previous parents as new offspring gene codes, so as to greatly increase the operation speed of the algorithm. The superior gene coding of the parent may not be present in part of the gene in the product sequence coding, which is called inheritance failure. Excellent gene codes of the parents can be found in product sequence codes, and then excellent varieties can be combined.
(III) the invention has the following beneficial effects:
the method has the advantages that the algorithm idea, the calculation time and the complexity of model calculation are considered at the same time, the one-dimensional blanking problem can be solved under multiple constraint conditions (including the condition that the constraint conditions are uncertain), the mode of adopting a scanning operator three-stage optimization threshold, a mutation operator and an inheritance operator is adopted, the utilization rate maximization of a single raw material is taken as a set target, and an approximately optimal solution is sought, so that the raw materials are utilized to the maximum extent in the actual production process, the resources are saved, the production cost of enterprises is reduced, and the utilization rate of actual production is guaranteed.
Drawings
FIG. 1 is a topological diagram of the constructed genetic coding sequences of the present invention.
FIG. 2 is a schematic diagram of the mutation operator of the present invention.
FIG. 3 is a system process block diagram of the present invention.
Detailed Description
The following description will be made in detail with reference to the accompanying drawings and examples. The following examples are intended to illustrate the technical solution of the present invention.
The invention provides a novel one-dimensional rehearsal blanking algorithm design based on genetic algorithm, which is characterized in that input products are sorted according to different lengths and a descending method, a coding sequence is built according to the lengths and the number of the products, three-gear optimized threshold (1 gear, 2 gear and 3 gear) of a scanning operator is utilized, the threshold is gradually decreased, whether the set threshold is reached or not is judged, if the minimum threshold target cannot be reached, a mutation operator is started, if a gene coding combination after mutation is superior to that before mutation, which is called as favorable mutation, the original genotype is replaced, otherwise, the original genotype is not processed, and finally, a succession operator is utilized to inherit a variety with better genotype in the previous generation, so that the aim of maximizing the material utilization rate is realized, and the method specifically comprises the following steps:
s1, sorting, namely sorting the sub-parts to be processed according to a principle that the sub-parts with longer lengths are arranged in front, and respectively storing Length information and quantity information of a sorted new sub-part sequence into corresponding arrays of a Product _ Length [ ] and a Product _ Length _ Number [ ]; and storing the arranged new subsequence into a Length Raw _ Material _ Length [ ] and a Number Raw _ Material _ Number [ ] of the arranged new subsequence according to the user intention and the principle that the Raw materials with high priority treatment level are arranged in the front.
S2, constructing a genetic coding sequence according to the length and the quantity of the product (shown in figure 1). The length of the raw material is set to L according to the above1,L2,L3,…，Ln-1，Ln(ii) a The corresponding quantities are: n is a radical of1,N2,N3,…，Nn-1，Nn(ii) a We use the number 1, 2.. No. n to represent the code of the corresponding raw material, and represent the number of 1 raw material in the stock according to the number of 1 occurrence in the array, so that we can build the following raw material coding sequence,
Encoding_Raw_Materials[Maximum_RM]＝{1,1,1,,2,2,2,...,n,n,n}；(1)
for example: (1112222233.. said.) such a code sequence means that there are 2 products and 5 products for No. 1, and 2 products for No. 3. For example, Product _ Length [1] can obtain the Length and other information of Product No. 1 (the conversion relation is built between the coding sequence and the Product information), and the multiple nested loops are successfully converted into single loops by the gene coding principle.
S3, the algorithm solving problem is composed of double-layer loops, an outer-layer loop takes out a Raw material to be processed from Encoding _ Raw _ Materials [ ] and sends the Raw material to be processed to an iterator each time, an inner-layer loop sequentially takes out the length of a sub-part from Encoding _ Product [ ] and sends the length of the sub-part to an accumulator in the iterator, judgment is generated after the accumulator superposes the length of the sub-part each time, if the newly added sub-part allows the accumulator to overflow (exceeds the length of the Raw material sent to the iterator), the last superposed sub-part is removed, and if the newly added sub-part does not allow the accumulator to overflow, the adding result is kept. And (3) entering a new sub-part into an accumulator each time, calculating whether the ratio (single utilization rate) of the accumulator to the length of the input raw material reaches a set threshold value, and if the ratio reaches a set first threshold value target, taking the result as a successful test at this time, and keeping the sub-part code added into the superimposer. And the sub-part code is rejected from Encoding _ Product [ ] and the Raw material input to the iterator also needs to be rejected from Encoding _ Raw _ Materials [ ].
And S4, after the scanning operator fails (the minimum threshold target cannot be reached), starting a mutation operator (as shown in figure 2). Because the operator is scanned, the relation between the sequential accumulation calculation utilization rate and the threshold value is used as a cycle termination condition, and certain limitation exists. Mutation operators only need to supplement the limitation, and increase the possible types of genotypes. For example: and (3) coding genotype obtained by result scanning operator operation: 11112, the utilization can reach 93% (below the minimum scan target, trigger mutation operator).
The first step is as follows: self-exchange. The first (n-1) number is randomly selected, exchanged with the last nth number. For example, swapping 3 and 5 to obtain a new genotype 11211
The second step is that: the total length is used, minus the length of the previous n-1 number after the exchange, equals the new length of the residue.
Third cloth: and taking the new surplus length from the residual code library as a termination judgment condition according to the utilization rate. If the genotype after mutation (the corresponding utilization rate is better than that before mutation, which is called successful mutation), the genotype is replaced with the original genotype and retained. If the mutation fails, the pre-mutation is retained.
S5, inheriting the excellent gene code of the previous parent by using an inheritance operator as a new child gene code, so that the operation speed of the algorithm can be greatly increased. The superior gene coding of the parent may not be present in part of the gene in the product sequence coding, which is called inheritance failure. Excellent gene codes of the parents can be found in product sequence codes, and then excellent varieties can be combined.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (4)
1. A novel one-dimensional rehearsal blanking method based on genetic algorithm is used for cutting blanking of rod-shaped products, and is characterized by comprising the following steps:
s1, sorting input products according to the length in a descending order method to obtain codes of the products, wherein the minimum code is 1 and the products with the same length use the same code;
s2, constructing the codes of the products into a coding sequence, wherein the coding sequence comprises the codes of all the products and is arranged from small to large in the coding sequence;
s3, setting an optimization threshold, taking out the codes from the gene codes through single circulation, adding the codes into an accumulated variable, judging whether the ratio of the accumulated variable to the length of the input raw material is smaller than the set optimization threshold, if so, performing one-time successful scanning and outputting, and if not, performing the next step;
s4, starting a mutation operator:
the first step is as follows: self-exchange: randomly selecting the front n-1 number from the coding sequence, and exchanging the front n-1 number with the rearmost n number;
the second step is that: the length of the front n-1 number after the total length is reduced and exchanged is utilized to obtain the length of a new excess material;
the third step: taking the new surplus length from the residual code library, substituting the value, and taking the utilization rate as a termination judgment condition; if the genotype after mutation and the corresponding utilization rate are superior to those before mutation, the successful mutation is called, and the genotype replaces the original genotype and is reserved; if the mutation fails, preserving the mutation;
s5, inheriting the excellent gene code of the previous parent by using an inheritance operator to serve as a new filial generation gene code, wherein the excellent gene code of the parent is possibly absent in part of the product sequence codes, so that inheritance failure is called, and the excellent gene code of the parent can be found in the product sequence codes, so that excellent varieties can be combined;
the output of the algorithm program is: novel coding sequences and cleavage sequences.
2. The novel one-dimensional rehearsal baiting method based on genetic algorithm as claimed in claim 1, wherein the optimization threshold comprises a gradually decreasing third-gear optimization threshold.
3. The genetic algorithm-based novel one-dimensional rehearsal blanking method as claimed in claim 1, wherein a single product has relatively good utilization rate through three steps of genetic operator, scanning operator and mutation operator; each time the product is executed, the product code sequence is refreshed and a new product is entered from inventory.
4. The genetic algorithm-based novel one-dimensional rehearsal baiting method according to claim 1, wherein the inputs of the algorithm program are: length of each product and sequence coding of the gene pool.
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| CN106934485A (en) * | 2017-01-17 | 2017-07-07 | 广东工业大学 | A kind of new one-dimensional based on genetic algorithm rehearses baiting method |
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| CN101739606B (en) * | 2008-11-19 | 2014-03-26 | 北京理工大学 | Raw material-saving one-dimensional stock-cutting method |
| CN101582130A (en) * | 2009-05-27 | 2009-11-18 | 清华大学 | Method for improving genetic algorithm structural optimization efficiency |
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