CN113657642B - Smelting workshop production and transportation collaborative optimization method and system based on hybrid algorithm - Google Patents

Smelting workshop production and transportation collaborative optimization method and system based on hybrid algorithm Download PDF

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CN113657642B
CN113657642B CN202110759993.0A CN202110759993A CN113657642B CN 113657642 B CN113657642 B CN 113657642B CN 202110759993 A CN202110759993 A CN 202110759993A CN 113657642 B CN113657642 B CN 113657642B
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孔敏
李菊
谭卫民
张廷龙
朱兵
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Abstract

The invention provides a smelting workshop production and transportation collaborative optimization method and system based on a hybrid algorithm, and relates to the technical field of workshop scheduling optimization. Aiming at the production and transportation collaborative optimization problem of a smelting workshop, the invention provides a mixed algorithm based on a BRKGA algorithm and a TSA algorithm. Firstly, the number of aviation parts and the real number are in one-to-one correspondence based on a random key coding mode, the smelting and transportation sequence of the parts is determined, and secondly, the span from 0 to the time before the transportation to the next stage of all the parts is calculated by utilizing the mould box full-load principle, and Tc is calculated. Then, according to the basic idea of BRKGA algorithm, the chromosome population is randomly initialized, and the chromosome population is continuously subjected to iterative updating through population classification and TSA algorithm-based crossing and mutation operation, so that the approximate optimal solution is finally obtained. According to the technical scheme, the solving process is more efficient and accurate, the production efficiency of enterprises is improved, and the energy consumption of the enterprises is reduced.

Description

Smelting workshop production and transportation collaborative optimization method and system based on hybrid algorithm
Technical Field
The invention relates to the technical field of optimization scheduling of smelting workshops, in particular to a method and a system for collaborative optimization of production and transportation of a smelting workshop based on a hybrid algorithm.
Background
The smelting workshop is a production workshop with a vacuum induction smelting furnace, and the smelting object is generally special steel, alloy and other materials which are needed in high-end aerospace equipment such as aerospace and the like. The flow of a smelting workshop can be summarized into links such as feeding, smelting, casting, cooling, transporting and the like, and a reasonable linkage optimization scheduling scheme of multi-stage operation is provided for the flow, so that the production efficiency of enterprises can be improved, the energy consumption of the enterprises can be reduced, and the method is significant for the enterprises.
At present, for a linkage optimization scheduling scheme of multi-level operation of a smelting workshop, an objective function is generally set according to actual production needs, and then a related algorithm (such as a genetic algorithm) is utilized to optimize the flow of the smelting workshop, so that a final optimization result is obtained, and production scheduling is executed according to the optimization result.
However, when the linkage optimization scheduling scheme of the multi-stage operation of the smelting workshop is solved, the applied algorithm has certain defects, so that the solution efficiency of the optimization scheduling result is low or the solution result is not accurate enough. For example, the bias random key genetic algorithm (biasedrendom-key genetic algorithm, BRKGA), while avoiding premature algorithm convergence to some extent, does not introduce a steering function for the optimal solution, affecting the algorithm convergence rate to some extent; while Tree-seed algorism (TSA) can effectively control the search direction of the algorithm, it is difficult to avoid the dilemma of local optimum in the later period of operation. Based on the method, the invention aims to provide a smelting workshop production and transportation collaborative optimization scheduling method based on a hybrid algorithm, so as to solve the problems that the optimal scheduling result solving efficiency is low and inaccurate when the existing smelting workshop performs multi-stage operation linkage optimal scheduling.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a smelting workshop production and transportation collaborative optimization method and system based on a hybrid algorithm, which solve the problems of low solving efficiency and inaccuracy of an optimized dispatching result in the multi-stage operation linkage optimized dispatching technology of the existing smelting workshop.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme:
in a first aspect, the invention firstly provides a smelting workshop production and transportation collaborative optimization method based on a mixing algorithm, which comprises the following steps:
s1, initializing related parameters and populations of a BRKGA algorithm and a TSA algorithm; the related parameters comprise population scale N, elite individual proportion alpha, variant individual proportion beta and variant probability delta of BRKGA algorithm,tree species in an iteration operator based on a TSA algorithm generate proportion gamma, a trend control parameter ST is searched, and the maximum number Mit of new solutions is generated in the operation process of the algorithm; current lot number of aerospace components k=1; start smelting time start for kth aircraft component k =0; the number n of aviation components which are required to be processed in the smelting workshop; the number m of the cooling chambers carrying the dies can be simultaneously placed at most; die size s of first aerospace component l Wherein, l=1, 2, n; the crucible capacity V in the smelting furnace; cooling time per unit size p l Wherein, l=1, 2, n; the energy consumption c of a smelting workshop in unit time;
s2, calculating fitness function values of all chromosomes in the current population, dividing alpha individuals with the best fitness function values into elite individual groups, and dividing the rest individuals into non-elite individual groups;
s3, reserving the elite individuals, and executing cross operation based on TSA algorithm on the non-elite individual group;
s4, calculating fitness function values of all chromosomes in the updated population, and executing variation operation based on TSA algorithm on the updated non-elite individual group with the updated fitness function values being in inverse beta N;
s5, judging whether the number of new solutions generated by the current iteration is smaller than the maximum number of new solutions Mit, if so, turning to S2, otherwise, outputting an optimal solution X best And the corresponding fitness function value fit best And (5) a corresponding collaborative optimization scheduling scheme is adopted.
Preferably, when initializing the population in S1, generating an initial solution of the population according to a random key coding rule, which specifically includes:
randomly initializing a population of BRKGA algorithm, wherein the population comprises N chromosomes X= { X i I=1, 2,..n }, wherein the genome of chromosome i is X i ＝{x ij ∈(0，1)|j＝1，2，...，n}；
A numbering set a= { j|j=1, 2,.. i ＝{X i1 ，X i2 ，...，X in X is }, X ij One-to-one with part number jThe reaction is carried out;
all elements in the numbering set A are processed according to the corresponding X i The components in the reordered numbering set A are used as the smelting and transportation sequence of the components and are marked asThe corresponding die size set is { s } 1 ，s 2 ，...，s n Cooling time set per unit size is { p } 1 ，p 2 ，...，p n }。
Preferably, the step S2 of calculating fitness function values of the chromosomes in the current population, and dividing the α individuals with the best fitness function values into elite individual groups, and dividing the remaining individuals into non-elite individual groups includes:
s21, set k = 1 Let start k ＝0，
Wherein k represents the current batch number; start k Indicating the start smelting time of the kth batch;
s22, order
And let k=k+1, let again
wherein ,
finish k indicating the cooling end time of the k batch;
n represents the number of aviation components which are required to be processed in the smelting workshop;
m represents the number of dies which can be placed simultaneously at most in the cooling chamber carrying the dies;
s l the mold size for the first aerospace component, l=1, 2, n;
v represents the capacity of a crucible in the smelting furnace;
r represents the time required for smelting the metallic material of all the aerospace components;
p l The cooling time per unit size is indicated, l=1, 2,. -%, n;
t represents the time required from time 0 to the time required for the last component to be transported to the next stage;
c represents the energy consumption of a smelting workshop in unit time;
s23, judging whether (k-1) m is less than or equal to n and is true, if true, turning to S22, otherwise outputting Tc=finish x c, taking the Tc=finish x c as fitness value of the chromosome, and calculating fitness value fit= { fit of each chromosome in the current population i |i＝1，2，...，N}；
S24, arranging chromosomes in the population in an ascending order according to fitness values, and marking individuals corresponding to the chromosomes arranged at the front alpha as elite individual groupsIndividuals corresponding to the remaining chromosomes are marked as non-elite individuals
Preferably, the performing a TSA algorithm-based interleaving operation on the group of non-elite individuals in S3 includes:
s31, marking the current t generation non-elite individual group asSetting i=αn+1;
s32, aiming at non-elite individualsIf the random number rand (0, 1) is less than or equal to ST, then according to +.>Randomly generating a tree species->Otherwise according to->Randomly generating a tree species->Judging whether the solution of the tree species is better than that of the original tree species, and if so, replacing the solution of the original tree species;
wherein ST denotes a search trend control parameter;a j-th gene for any chromosome other than the best chromosome in the elite group of individuals; alpha ij Is a random scaling factor;
s33, repeating the S32 operation gamma N times;
wherein, gamma represents the tree species generation ratio in the iteration operator based on TSA algorithm; n represents the population size of the BRKGA algorithm;
s34, if i > N, the loop is ended, otherwise let i=i+1 and go to S32.
Preferably, the performing a mutation operation based on the TSA algorithm on the updated non-elite individual group with the updated fitness function value being the reciprocal βn in S4 includes:
s41, marking the current t generation reciprocal beta N non-elite individuals asSetting i= (1- β) n+1;
s42, for X nep Any non-elite individual of (t)Setting j=1;
s43, if random number rand (0, 1) is less than or equal to 0.5, makingNo->
wherein ,the j-th gene of the best chromosome at present; />Any of the j-th genes of the elite panel of individuals currently except the best chromosome;
s44, let j=j+1, judge j is less than or equal to n and is established, if established, go to S43, otherwise finish the cycle.
In a second aspect, the invention also provides a smelting workshop production and transportation collaborative optimization system based on a mixing algorithm, which comprises:
a processing unit for executing the following steps:
s1, initializing related parameters of a BRKGA algorithm and a TSA algorithm and a population; the related parameters comprise population scale N, elite individual proportion alpha, variant individual proportion beta and variant probability delta of a BRKGA algorithm, the proportion gamma is generated based on tree species in an iterative operator of the TSA algorithm, a trend control parameter ST is searched, and the maximum number Mit of new solutions is generated in the running process of the algorithm; current lot number of aerospace components k=1; start smelting time start for kth aircraft component k =0; the number n of aviation components which are required to be processed in the smelting workshop; the number m of the cooling chambers carrying the dies can be simultaneously placed at most; die size s of first aerospace component l Wherein, l=1, 2, n; the crucible capacity V in the smelting furnace; cooling time per unit size p l Wherein, l=1, 2, n; the energy consumption c of a smelting workshop in unit time;
s2, calculating fitness function values of all chromosomes in the current population, dividing alpha individuals with the best fitness function values into elite individual groups, and dividing the rest individuals into non-elite individual groups;
s3, reserving the elite individuals, and executing cross operation based on TSA algorithm on the non-elite individual group;
s4, calculating fitness function values of all chromosomes in the updated population, and executing variation operation based on TSA algorithm on the updated non-elite individual group with the updated fitness function values being in inverse beta N;
s5, judging whether the number of new solutions generated by the current iteration is smaller than the maximum number of new solutions Mit, if so, turning to S2, otherwise, ending the algorithm execution;
an output unit for outputting an optimal solution X best Corresponding fit best And corresponding schemes.
Preferably, when the processing unit initializes the population in S1, generating an initial solution of the population according to the random key coding rule includes:
Randomly initializing a population of BRKGA algorithm, wherein the population comprises N chromosomes X= { X i I=1, 2,..n }, wherein the genome of chromosome i is X i ＝{x ij ∈(0，1)|j＝1，2，...，n}；
A numbering set a= { j|j=1, 2,.. i ＝{X i1 ，X i2 ，...，X in X is }, X ij One-to-one correspondence with the component number j;
all elements in the numbering set A are processed according to the corresponding X i The components in the reordered numbering set A are used as the smelting and transportation sequence of the components and are marked asThe corresponding die size set is { s } 1 ，s 2 ，...，s n Cooling time set per unit size is { p } 1 ，p 2 ，...，p n }。
Preferably, when executing S2, the processing unit calculates fitness function values of the chromosomes in the current population, and divides the α individuals with the best fitness function values into elite individual groups, and divides the remaining individuals into non-elite individual groups, including:
s21, set k=1, let start k ＝0，
Wherein k represents the current batch number; start k Indicating the start of the kth batchSmelting time;
s22, order
And let k=k+1, let again
wherein ,
finish k indicating the cooling end time of the k batch;
n represents the number of aviation components which are required to be processed in the smelting workshop;
m represents the number of dies which can be placed simultaneously at most in the cooling chamber carrying the dies;
s l The mold size for the first aerospace component, l=1, 2, n;
v represents the capacity of a crucible in the smelting furnace;
r represents the time required for smelting the metallic material of all the aerospace components;
p l the cooling time per unit size is indicated, l=1, 2,. -%, n;
t represents the time required from time 0 to the time required for the last component to be transported to the next stage;
c represents the energy consumption of a smelting workshop in unit time;
s23, judging whether (k-1) m is less than or equal to n and is true, if true, turning to S22, otherwise outputting Tc=finish x c, taking the Tc=finish x c as fitness value of the chromosome, and calculating fitness value fit= { fit of each chromosome in the current population i |i＝1，2，...，N}；
S24, arranging chromosomes in the population in an ascending order according to fitness values, and marking individuals corresponding to the chromosomes arranged at the front alpha as elite individual groupsThe remaining chromosomes were scored as non-elite individuals
Preferably, when executing S3, the processing unit performs a cross operation based on a TSA algorithm on the non-elite individual group, including:
s31, marking the current t generation non-elite individual group asSetting i=αn+1;
s32, aiming at non-elite individualsIf the random number rand (0, 1) is less than or equal to ST, then according to +.>Randomly generating a tree species->Otherwise according to->Randomly generating a tree species- >Judging whether the tree species solution is better than the original tree species solution, if so, replacing the solution of the original tree species;
wherein ST denotes a search trend control parameter;a j-th gene for any chromosome other than the best chromosome in the elite group of individuals; alpha ij Is a random scaling factor;
s33, repeating the S32 operation gamma N times;
wherein, gamma represents the tree species generation ratio in the iteration operator based on TSA algorithm; n represents the population size of the BRKGA algorithm;
s34, if i > N, the loop is ended, otherwise let i=i+1 and go to S32.
Preferably, when executing S4, the executing, by the processing unit, the mutation operation based on the TSA algorithm on the updated non-elite individual group with the updated fitness function value being the reciprocal βn includes:
s41, marking the current t generation reciprocal beta N non-elite individuals asSetting i= (1- β) n+1;
s42, for X nep Any non-elite individual of (t)Setting j=1;
s43, if random number rand (0, 1) is less than or equal to 0.5, makingNo-> wherein ,the j-th gene of the best chromosome at present; />Any of the j-th genes of the elite panel of individuals currently except the best chromosome;
s44, let j=j+1, judge j is less than or equal to n and is established, if established, go to S43, otherwise finish the cycle.
(III) beneficial effects
The invention provides a smelting workshop production and transportation collaborative optimization method and system based on a hybrid algorithm. Compared with the prior art, the method has the following beneficial effects:
1. according to the technical scheme, firstly, the number of aviation components and the real number code are in one-to-one correspondence based on a random key coding mode, the smelting and transportation sequence of the components is determined, secondly, the full-load principle of a mould box is utilized, so that the span time T from 0 to the time before the transportation to the next stage of all the aviation components is calculated, tc is calculated, then, the chromosome population is randomly initialized based on a BRKGA algorithm and a TSA algorithm mixed algorithm, and the chromosome population is continuously subjected to iterative update through population classification and TSA-based cross and mutation operation, so that the approximate optimal solution is finally obtained. According to the technical scheme, the mixed algorithm based on the BRKGA algorithm and the TSA algorithm can provide a better search advancing direction in the solving process, has better convergence, and simultaneously takes account of the advantages of parallel distributed computing of the population, so that the solving process is more efficient and accurate, the production efficiency of enterprises is improved, and the energy consumption of the enterprises is reduced.
2. Compared with the traditional BRKGA algorithm, the method can guide the searching direction to advance towards the current optimized direction in the solving process, so that the solving speed is improved, and meanwhile, the solving accuracy is improved;
3. According to the technical scheme, the mutation operation based on the TSA algorithm is used, the genes of elite individuals are introduced, and compared with the mutation operation of the traditional BRKGA algorithm, the effectiveness of searching is ensured to a certain extent.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for collaborative optimization of production and transportation in a smelting plant based on a hybrid algorithm in an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the application solves the problems of low and inaccurate solving efficiency of the optimized dispatching result in the existing multi-stage operation linkage optimized dispatching technology of the smelting workshop by providing the production and transportation collaborative optimization method and system of the smelting workshop based on the hybrid algorithm, and achieves the purposes of quickly and accurately solving the optimized dispatching result, improving the production efficiency of enterprises and reducing the energy consumption of the enterprises.
The technical scheme in the embodiment of the application aims to solve the technical problems, and the overall thought is as follows:
aiming at the production and transportation collaborative optimization problem of a smelting workshop, the application provides a mixed algorithm based on a BRKGA algorithm and a TSA algorithm. Firstly, the number of aviation parts and the real number are in one-to-one correspondence based on a random key coding mode, the smelting and transportation sequence of the parts is determined, and secondly, the span from 0 to the time before the transportation to the next stage of all the parts is calculated by utilizing the mould box full-load principle, and Tc is calculated. Then, according to the basic idea of BRKGA algorithm, the chromosome population is randomly initialized, and the chromosome population is continuously subjected to iterative updating through population classification and TSA algorithm-based crossing and mutation operation, so that the approximate optimal solution is finally obtained.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
The smelting workshop is a production workshop with a vacuum induction smelting furnace, and the smelting object of the vacuum induction smelting furnace is generally special steel, alloy and other materials which are needed in high-end aerospace equipment such as aerospace and the like. The flow of the smelting workshop is roughly divided into links of feeding, smelting, casting, cooling, transporting and the like. Adding corresponding metal raw materials through a feeding port of a smelting furnace; immediately after the metal starts to melt in a vacuum environment so that it changes from a solid state to a liquid state; then pouring the liquid metal liquid into the mould in sequence; and finally cooling in a mould to form and transporting to the next station for reprocessing. As for feeding, the current feeding mode comprises a one-time feeding mode and a continuous feeding mode, and the technical scheme aims at continuous feeding, namely, metal materials can be added into a crucible of a furnace body in the smelting process on the premise of not influencing the vacuum environment in the furnace so as to maintain the continuity of the production process. In addition, the mould system carrying the moulds can bear a plurality of moulds with different sizes at the same time, the casting mode is sequential casting, when all the moulds in the mould system are required to be cast, the moulds can be cooled and then transported to a station for next reprocessing, and after the cooling is finished, the mould system can carry out the casting operation of the moulds again. The technical scheme aims at providing an optimal product production scheduling scheme of the smelting workshop and a logistics transportation scheme in a product factory aiming at the production and transportation collaborative scheduling problem of the smelting workshop. In the present technical solution, it is assumed that:
(1) The smelting workshop is required to process n aviation components, and the mould size of each aviation component is s l L=1, 2,..n, crucible capacity in the melting furnace is V;
(2) The melting time of the metal material of all the space components is r, and the cooling time of unit size is p l L=1, 2,..n. Compared with the smelting time and the cooling time, the casting time is shorter, so that each casting time is ignored;
(3) At most, m moulds can be placed in a cooling chamber loaded with moulds at the same time, metal materials are poured into the moulds in sequence after being smelted in a crucible, cooling and forming are carried out in the cooling chamber, then the cooling chamber is transferred to the next section for processing, and the cooling chamber is then returned for continuous operation until all parts are finished.
(4) Assuming that the time interval from time 0 to the transportation of the last component to the next stage is T, the energy consumption per unit time of the smelting plant is c, the goal is to find the optimal component smelting and transportation solution to minimize the energy consumption Tc.
Example 1:
in a first aspect, the present invention firstly proposes a method for collaborative optimization of production and transportation in a smelting plant based on a hybrid algorithm, see fig. 1, the method comprising:
s1, initializing BRKGA algorithmAnd related parameters and populations of the TSA algorithm; the related parameters comprise population scale N, elite individual proportion alpha, variant individual proportion beta and variant probability delta of a BRKGA algorithm, the proportion gamma is generated based on tree species in an iterative operator of the TSA algorithm, a trend control parameter ST is searched, and the maximum number Mit of new solutions is generated in the running process of the algorithm; current lot number of aerospace components k=1; start smelting time start for kth aircraft component k =0; the number n of aviation components which are required to be processed in the smelting workshop; the number m of the cooling chambers carrying the dies can be simultaneously placed at most; die size s of first aerospace component l Wherein, l=1, 2, n; the crucible capacity V in the smelting furnace; cooling time per unit size p l Wherein, l=1, 2, n; the energy consumption c of a smelting workshop in unit time;
s2, calculating fitness function values of all chromosomes in the current population, dividing alpha individuals with the best fitness function values into elite individual groups, and dividing the rest individuals into non-elite individual groups;
s3, reserving the elite individuals, and executing cross operation based on TSA algorithm on the non-elite individual group;
s4, calculating fitness function values of all chromosomes in the updated population, and executing variation operation based on TSA algorithm on the updated non-elite individual group with the updated fitness function values being in inverse beta N;
s5, judging whether the number of new solutions generated by the current iteration is smaller than the maximum number of new solutions Mit, if so, turning to S2, otherwise, outputting an optimal solution X best Corresponding fitness function value fit best 。
It can be seen that the technical scheme of the invention firstly corresponds aviation component numbers and real numbers one by one based on a random key coding mode, determines the smelting and transportation sequence of the components, secondly utilizes a mold box filling principle, thereby calculating the span time T from 0 to the time before the transportation to the next stage of all the aviation components, and calculating Tc therefrom, then randomly initializing a chromosome population based on a BRKGA algorithm and a TSA algorithm mixed algorithm, and continuously carrying out iterative updating on the chromosome population through population classification and TSA-based intersection and mutation operation, and finally obtaining an approximate optimal solution. According to the technical scheme, the mixed algorithm based on the BRKGA algorithm and the TSA algorithm can provide a better search advancing direction in the solving process, has better convergence, and simultaneously takes account of the advantages of parallel distributed computing of the population, so that the solving process is more efficient and accurate, the production efficiency of enterprises is improved, and the energy consumption of the enterprises is reduced.
In the above method of the embodiment of the present invention, in order to determine the melting and transportation sequence of the components, a preferred processing manner is to make the aviation component numbers and the real numbers correspond to each other one by one based on the coding manner of the random key, and at this time, when initializing the population in S1, an initial solution of the population is generated according to the coding rule of the random key, which specifically includes:
randomly initializing a population of BRKGA algorithm, wherein the population comprises N chromosomes X= { X i I=1, 2,..n }, wherein the genome of chromosome i is X i ＝{x ij ∈(0,1)|j＝1,2,...,n}；
A numbering set a= { j|j=1, 2,.. i ＝{X i1 ,X i2 ,...,X in X is }, X ij One-to-one correspondence with the component number j;
all elements in the numbering set A are processed according to the corresponding X i The components in the reordered numbering set A are used as the smelting and transportation sequence of the components and are marked asThe corresponding die size set is { s } 1 ,s 2 ,...,s n Cooling time set per unit size is { p } 1 ,p 2 ,...,p n }。
In practice, in order to accurately divide elite individual groups and non-elite individual groups, searching for an optimal component smelting and transporting scheme on the premise of minimizing energy consumption, a preferred processing manner is that, in S2, fitness function values of each chromosome in a current population are calculated, α individuals with the best fitness function values are divided into elite individual groups, and when the remaining individuals are divided into non-elite individual groups, the processing method includes:
S21, set k=1, let start k ＝0，
Wherein k represents the current batch number; start k Indicating the start smelting time of the kth batch;
s22, order
And let k=k+1, let again
wherein ,
finish k indicating the cooling end time of the k batch;
n represents the number of aviation components which are required to be processed in the smelting workshop;
m represents the number of dies which can be placed simultaneously at most in the cooling chamber carrying the dies;
s l the mold size for the first aerospace component, l=1, 2, n;
v represents the capacity of a crucible in the smelting furnace;
r represents the time required for smelting the metallic material of all the aerospace components;
p l the cooling time per unit size is indicated, l=1, 2,. -%, n;
t represents the time required from time 0 to the time required for the last component to be transported to the next stage;
c represents the energy consumption of a smelting workshop in unit time;
s23, judging whether (k-1) m is less than or equal to n and is true, if true, turning to S22, otherwise outputting Tc=finish x c, taking the Tc=finish x c as fitness value of the chromosome, and calculating fitness value fit= { fit of each chromosome in the current population i |i＝1，2，...，N}；
S24, arranging chromosomes in the population in an ascending order according to fitness values, and marking individuals corresponding to the chromosomes arranged at the front alpha as elite individual groupsIndividuals corresponding to the remaining chromosomes are marked as non-elite individuals
In addition, in order to guide the search direction to advance towards the current optimized direction in the solving process, improve the solving speed and improve the solving accuracy at the same time, a cross operation based on a TSA algorithm is used, and at this time, a preferred processing manner is that the executing the cross operation based on the TSA algorithm on the non-elite individual group in S3 includes:
s31, marking the current t generation non-elite individual group asSetting i=αn+1;
s32, aiming at non-elite individualsIf the random number rand (0, 1) is less than or equal to ST, then according to +.>Randomly generating a tree species->Otherwise according to->Randomly generating a tree species->Judging whether the tree species solution is better than the original tree species solution, if so, replacing the solution of the original tree species;
wherein ST denotes a search trend control parameter;for any chromosome in the elite group other than the best chromosomeIs the j-th gene of (2); alpha ij Is a random scaling factor;
s33, repeating the S32 operation gamma N times;
wherein, gamma represents the tree species generation ratio in the iteration operator based on TSA algorithm; n represents the population size of the BRKGA algorithm;
s34, if i > N, the loop is ended, otherwise let i=i+1 and go to S32.
Meanwhile, in order to ensure the effectiveness of searching to a certain extent, a mutation operation based on a TSA algorithm is used, and genes of elite individuals are introduced, and at this time, a preferred processing mode is that executing the mutation operation based on the TSA algorithm on the updated non-elite individual group with the updated fitness function value being the reciprocal beta N in S4 includes:
S41, marking the current t generation reciprocal beta N non-elite individuals asSetting i= (1- β) n+1;
s42, for X nep Any non-elite individual of (t)Setting j=1;
s43, if random number rand (0, 1) is less than or equal to 0.5, makingNo->
wherein ,the j-th gene of the best chromosome at present; />Any of the j-th genes of the elite panel of individuals currently except the best chromosome;
s44, let j=j+1, judge j is less than or equal to n and is established, if established, go to S43, otherwise finish the cycle.
The specific implementation of one embodiment of the present invention is described below in conjunction with a detailed explanation of the specific steps of S1-S7.
S1, initializing related parameters of a BRKGA algorithm and a TSA algorithm and a population; the related parameters comprise population scale N, elite individual proportion alpha, variant individual proportion beta and variant probability delta of a BRKGA algorithm, the proportion gamma is generated based on tree species in an iterative operator of the TSA algorithm, a trend control parameter ST is searched, and the maximum number Mit of new solutions is generated in the running process of the algorithm; current lot number of aerospace components k=1; start smelting time start for kth aircraft component k =0; the number n of aviation components which are required to be processed in the smelting workshop; the number m of the cooling chambers carrying the dies can be simultaneously placed at most; die size s of first aerospace component l Wherein, l=1, 2, n; the crucible capacity V in the smelting furnace; cooling time per unit size p l Wherein, l=1, 2, n; and energy consumption c of the smelting workshop in unit time.
S11, setting a population scale N, elite individual proportion alpha, variant individual proportion beta and variant probability delta of a bias random-key genetic algorithm (BRKGA); setting Tree-seed algorism (TSA) -based iteration operator Tree seed generation ratio gamma, searching trend control parameter ST; setting an algorithm operation iteration termination condition Mit, namely the number of new solutions generated in the algorithm operation process is not more than Mit; current lot number of aerospace components k=1; start smelting time start for kth aircraft component k =0; the number n of aviation components which are required to be processed in the smelting workshop; the number m of the cooling chambers carrying the dies can be simultaneously placed at most; die size s of first aerospace component l Wherein, l=1, 2, n; the crucible capacity V in the smelting furnace; cooling time per unit size p l Wherein, l=1, 2, n; and energy consumption c of the smelting workshop in unit time.
S12, initializing a population. Generating initial solutions of the population according to random key coding rules, and generating N chromosomes X= { X i |i＝1，2..n }, wherein the genome of chromosome i is X i ＝{x ij ∈(0，1)|j＝1，2，...，n}。
Randomly initializing a population of BRKGA algorithm, wherein the population comprises N chromosomes X= { X i I=1, 2,..n }, wherein the genome of chromosome i is X i ＝{x ij E (0, 1) |j=1, 2. For any chromosome X i ＝{X i1 ，X i2 ，...，X in A numbered set a= { j|j=1, 2, n, for any chromosome X i ＝{X i1 ，X i2 ，...，X in X is }, X ij One-to-one correspondence with the part number j. Let X be the number set a= {1,2,..once, n }, with n aerospace components ij And corresponds to the space component number j one by one. Then all elements in the numbering set A are processed according to the corresponding X i The components in the reordered numbering set A are used as the smelting and transportation sequence of the components and are marked asThe corresponding die size set is { s } 1 ，s 2 ，...，s n Cooling time set per unit size is { p } 1 ，p 2 ，...，p n }。
S2, calculating fitness function values of all chromosomes in the current population, dividing alpha individuals with the best fitness function values into elite individual groups, and dividing the rest individuals into non-elite individual groups.
Calculating fitness function value fit= { fit of each chromosome in current population i |i＝1，2，...，N}。
S21, set k=1, let start k ＝0，
S22, order
And let k=k+1, let again
Wherein k represents the current batch number;
start k indicating the start smelting time of the kth batch;
finish k indicating the cooling end time of the k batch;
n represents the number of aviation components which are required to be processed in the smelting workshop;
m represents the number of dies which can be placed simultaneously at most in the cooling chamber carrying the dies;
s l the mold size for the first aerospace component, l=1, 2, n;
v represents the capacity of a crucible in the smelting furnace;
r represents the time required for smelting the metallic material of all the aerospace components;
p l the cooling time per unit size is indicated, l=1, 2,. -%, n;
t represents the time required from time 0 to the time required for the last component to be transported to the next stage;
c represents the energy consumption of a smelting workshop in unit time;
s23, judging whether (k-1) m is less than or equal to n and is true, if true, turning to S22, otherwise outputting Tc=finish x c, taking the Tc=finish x c as an fitness value of the chromosome, and calculating fitness function values fit= { fit of all chromosomes in the current population i I=1, 2,..n }. Wherein the minimum fitness value is denoted as fit best The chromosome corresponding to the fitness value is denoted as X best ＝{x bj ∈(0，1)|j＝1，2，...，n}。
S24, arranging chromosomes in the population in an ascending order according to fitness values, and marking the chromosomes arranged at the front alpha as elite individual groups The remaining chromosomes are marked as non-elite group of individuals +.>
S3, reserving the elite individuals, and executing cross operation based on TSA algorithm on the non-elite individual group.
Direct reservation for elite individual groupsFor any non-elite individual +.>And performing population iteration by adopting cross operation based on TSA algorithm.
Specifically, the cross operation based on the TSA algorithm includes:
s31 assume that the current t-th generation non-elite individual group is written asSetting i=αn+1.
S32, aiming at non-elite individualsIf the random number rand (0, 1) is less than or equal to ST, then according to +.>Randomly generating a tree species->Otherwise, according to->Randomly generating a tree species->
Wherein ST denotes a search trend control parameter;a j-th gene for any chromosome other than the best chromosome in the elite group of individuals; alpha ij Is a random scaling factor. Judging theWhether the tree species solution is better than the original tree solution, and if so, replacing the original tree solution.
S33, repeating the S32 operation gamma N times.
Wherein, gamma represents the tree species generation ratio in the iteration operator based on TSA algorithm; n represents the population size of the BRKGA algorithm.
S34, if i > N, the loop is ended, otherwise let i=i+1 and go to S32.
S4, calculating fitness function values of all chromosomes in the updated population, and executing variation operation based on TSA algorithm on the updated non-elite individual group with the updated fitness function values being in reciprocal beta N.
Re-calculating fitness function values of all chromosomes in the updated population, sorting the population in a descending order according to the fitness function values based on the fitness function values of all the updated chromosomes, and performing variation operation on the reciprocal beta N chromosomes based on a TSA algorithm, wherein the method specifically comprises the following steps:
s41, supposing that the current t-th generation reciprocal beta N non-elite individuals are recorded asSetting i= (1- β) n+1.
S42, for X nep Any non-elite individual of (t)Setting j=1.
S43, if random number rand (0, 1) is less than or equal to 0.5, makingNo-> wherein ,for the j-th gene of the currently best chromosome,/->Any of the current elite panel of individuals except the best chromosome is the j gene.
S44, let j=j+1, judge j is less than or equal to n and is established, if established, go to S43, otherwise finish the cycle.
S5, judging whether the number of new solutions generated by the current iteration is smaller than the maximum number of new solutions Mit, and if so, turning to S2; otherwise, outputting the optimal solution X best Corresponding fitness function value fit best 。
At this time, the output optimal solution X best Corresponding to the number of the optimal production and transportation sequence of the products, the smelting workshop can carry out production and transportation of the products according to the number sequence. Whereas fit best Corresponding to the energy consumption generated when the production schedule is performed according to the serial numbers of the above-mentioned production and transportation sequences, that is, the minimum energy consumption Tc.
Thus, the whole flow of the production and transportation collaborative optimization method of the smelting workshop based on the hybrid algorithm is completed.
Example 2:
in a second aspect, the invention also provides a smelting workshop production and transportation collaborative optimization system based on a mixing algorithm, which comprises:
a processing unit for executing the following steps:
s1, initializing related parameters of a BRKGA algorithm and a TSA algorithm and a population; the related parameters comprise population scale N, elite individual proportion alpha, variant individual proportion beta and variant probability delta of a BRKGA algorithm, the proportion gamma is generated based on tree species in an iterative operator of the TSA algorithm, a trend control parameter ST is searched, and the maximum number Mit of new solutions is generated in the running process of the algorithm; current lot number of aerospace components k=1; start smelting time start for kth aircraft component k =0; the number n of aviation components which are required to be processed in the smelting workshop; the number m of the cooling chambers carrying the dies can be simultaneously placed at most; die size s of first aerospace component l Wherein, l=1, 2, n; the crucible capacity V in the smelting furnace; cooling time per unit size p l Wherein, l=1, 2, where,n; the energy consumption c of a smelting workshop in unit time;
S2, calculating fitness function values of all chromosomes in the current population, dividing alpha individuals with the best fitness function values into elite individual groups, and dividing the rest individuals into non-elite individual groups;
s3, reserving the elite individuals, and executing cross operation based on TSA algorithm on the non-elite individual group;
s4, calculating fitness function values of all chromosomes in the updated population, and executing variation operation based on TSA algorithm on the updated non-elite individual group with the updated fitness function values being in inverse beta N;
s5, judging whether the number of new solutions generated by the current iteration is smaller than the maximum number of new solutions Mit, if so, turning to S2, otherwise, ending the algorithm execution;
an output unit for outputting an optimal solution X best Corresponding fit best And corresponding schemes.
7. The system of claim 6, wherein the processing unit, when performing initializing the population in S1, generates an initial solution for the population according to a random key encoding rule, comprising:
randomly initializing a population of BRKGA algorithm, wherein the population comprises N chromosomes X= { X i I=1, 2,..n }, wherein the genome of chromosome i is X i ＝{x ij ∈(0，1)|j＝1，2，...，n}；
A numbering set a= { j|j=1, 2,.. i ＝{X i1 ，X i2 ，...，X in X is }, X ij One-to-one correspondence with the component number j;
all elements in the numbering set A are processed according to the corresponding X i The components in the reordered numbering set A are used as the smelting and transportation sequence of the components and are marked asThe corresponding die size set is { s } 1 ，s 2 ，...，s n Cooling time set per unit size is { p } 1 ，p 2 ，...，p n }。
8. The system of claim 6, wherein the processing unit, when performing S2, calculates fitness function values for each chromosome in the current population and classifies the α individuals with the best fitness function values into elite individual groups, and the remaining individuals into non-elite individual groups comprises:
s21, set k=1, let start k ＝0，
Wherein k represents the current batch number; start k Indicating the start smelting time of the kth batch;
s22, order/>
And let k=k+1, let again
wherein ,
finish k indicating the cooling end time of the k batch;
n represents the number of aviation components which are required to be processed in the smelting workshop;
m represents the number of dies which can be placed simultaneously at most in the cooling chamber carrying the dies;
s l the mold size for the first aerospace component, l=1, 2, n;
v represents the capacity of a crucible in the smelting furnace;
r represents the time required for smelting the metallic material of all the aerospace components;
p l The cooling time per unit size is indicated, l=1, 2,. -%, n;
t represents the time required from time 0 to the time required for the last component to be transported to the next stage;
c represents the energy consumption of a smelting workshop in unit time;
s23, judging whether (k-1) m is less than or equal to n, if so, turning to S22, otherwiseOutputting Tc=finish=c, taking the Tc=finish=c as the fitness value of the chromosome, and calculating the fitness value fit= { fit of each chromosome in the current population i |i＝1，2，...，N}；
S24, arranging chromosomes in the population in an ascending order according to fitness values, and marking individuals corresponding to the chromosomes arranged at the front alpha as elite individual groupsThe remaining chromosomes were scored as non-elite individuals
9. The system of claim 8, wherein the processing unit, when performing S3, performs a TSA algorithm-based interleaving operation on the group of non-elite individuals comprising:
s31, marking the current t generation non-elite individual group asSetting i=αn+1;
s32, aiming at non-elite individualsIf the random number rand (0, 1) is less than or equal to ST, then according to +.>Randomly generating a tree species->Otherwise according to->Randomly generating a tree species->Judging whether the tree species solution is better than the original tree species solution, if so, replacing the solution of the original tree species;
wherein ST represents a search trend A potential control parameter;a j-th gene for any chromosome other than the best chromosome in the elite group of individuals; alpha ij Is a random scaling factor;
s33, repeating the S32 operation gamma N times;
wherein, gamma represents the tree species generation ratio in the iteration operator based on TSA algorithm; n represents the population size of the BRKGA algorithm;
s34, if i > N, the loop is ended, otherwise let i=i+1 and go to S32.
10. The system of claim 6, wherein the processing unit, when performing S4, performing a variation operation based on a TSA algorithm on the updated non-elite individual group having an updated fitness function value at an inverse βn comprises:
s41, marking the current t generation reciprocal beta N non-elite individuals asSetting i= (1- β) n+1; />
S42, for X nep Any non-elite individual of (t)Setting j=1;
s43, if random number rand (0, 1) is less than or equal to 0.5, makingNo-> wherein ,the j-th gene of the best chromosome at present; />Is the elite number except the best chromosomeAny somatic j-th gene of the somatic group;
s44, let j=j+1, judge j is less than or equal to n and is established, if established, go to S43, otherwise finish the cycle.
It may be understood that the production and transportation collaborative optimization system of the smelting shop based on the hybrid algorithm provided by the embodiment of the invention corresponds to the production and transportation collaborative optimization method of the smelting shop based on the hybrid algorithm, and the explanation, the examples, the beneficial effects and the like of the relevant content can refer to the corresponding content in the production and transportation collaborative optimization method of the smelting shop based on the hybrid algorithm, which is not repeated herein.
In summary, compared with the prior art, the method has the following beneficial effects:
1. according to the technical scheme, firstly, the number of aviation components and the real number code are in one-to-one correspondence based on a random key coding mode, the smelting and transportation sequence of the components is determined, secondly, the full-load principle of a mould box is utilized, so that the span time T from 0 to the time before the transportation to the next stage of all the aviation components is calculated, tc is calculated, then, the chromosome population is randomly initialized based on a BRKGA algorithm and a TSA algorithm mixed algorithm, and the chromosome population is continuously subjected to iterative update through population classification and TSA-based cross and mutation operation, so that the approximate optimal solution is finally obtained. According to the technical scheme, the mixed algorithm based on the BRKGA algorithm and the TSA algorithm can provide a better search advancing direction in the solving process, has better convergence, and simultaneously takes account of the advantages of parallel distributed computing of the population, so that the solving process is more efficient and accurate, the production efficiency of enterprises is improved, and the energy consumption of the enterprises is reduced.
2. Compared with the traditional BRKGA algorithm, the method can guide the searching direction to advance towards the current optimized direction in the solving process, so that the solving speed is improved, and meanwhile, the solving accuracy is improved;
3. According to the technical scheme, the mutation operation based on the TSA algorithm is used, the genes of elite individuals are introduced, and compared with the mutation operation of the traditional BRKGA algorithm, the effectiveness of searching is ensured to a certain extent.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A method for collaborative optimization of production and transportation in a smelting plant based on a hybrid algorithm, the method comprising:
s1, initializing related parameters and populations of a BRKGA algorithm and a TSA algorithm; the related parameters comprise population scale N, elite individual proportion alpha, variant individual proportion beta and variant probability delta of a BRKGA algorithm, the proportion gamma is generated based on tree species in an iterative operator of the TSA algorithm, a trend control parameter ST is searched, and the maximum number Mit of new solutions is generated in the running process of the algorithm; current lot number of aerospace components k=1; start smelting time start for kth aircraft component k =0; individual aircraft parts which are to be treated in a smelting plantA number n; the number m of the cooling chambers carrying the dies can be simultaneously placed at most; die size s of first aerospace component l Wherein, l=1, 2, n; the crucible capacity V in the smelting furnace; cooling time per unit size p l Wherein, l=1, 2, n; the energy consumption c of a smelting workshop in unit time;
s2, calculating fitness function values of all chromosomes in the current population, dividing alpha individuals with the best fitness function values into elite individual groups, and dividing the rest individuals into non-elite individual groups;
s3, reserving the elite individuals, and executing cross operation based on TSA algorithm on the non-elite individual group;
S4, calculating fitness function values of all chromosomes in the updated population, and executing variation operation based on TSA algorithm on the updated non-elite individual group with the updated fitness function values being in inverse beta N;
s5, judging whether the number of new solutions generated by the current iteration is smaller than the maximum number of new solutions Mit, if so, turning to S2, otherwise, outputting an optimal solution X best And the corresponding fitness function value fit best And (5) a corresponding collaborative optimization scheduling scheme is adopted.
2. The method of claim 1, wherein upon initializing the population in S1, generating an initial solution for the population according to a random key encoding rule, specifically comprises:
randomly initializing a population of BRKGA algorithm, wherein the population comprises N chromosomes X= { X i I=1, 2,..n }, wherein the genome of chromosome i is X i ＝{x ij ∈(0,1)|j＝1,2,...,n}；
A numbering set a= { j|j=1, 2,.. i ＝{X i1 ,X i2 ,...,X in X is }, X ij One-to-one correspondence with the component number j;
all elements in the numbering set A are processed according to the corresponding X i Arranging from small to large, taking the part numbering sequence in the reordered numbering set A as the smelting and transporting sequence of the parts, andis recorded asThe corresponding die size set is { s } 1 ,s 2 ,...,s n Cooling time set per unit size is { p } 1 ,p 2 ,...,p n }。
3. The method of claim 1, wherein S2 calculates fitness function values for each chromosome in the current population and classifies the α individuals with the best fitness function values as elite individual groups, and the remaining individuals as non-elite individual groups comprises:
s21, set k=1, let start k ＝0，
Wherein k represents the current batch number; start k Indicating the start smelting time of the kth batch;
s22, order
And let k=k+1, let again
wherein ,
finish k indicating the cooling end time of the k batch;
n represents the number of aviation components which are required to be processed in the smelting workshop;
m represents the number of dies which can be placed simultaneously at most in the cooling chamber carrying the dies;
s l the mold size for the first aerospace component, l=1, 2, n;
v represents the capacity of a crucible in the smelting furnace;
r represents the time required for smelting the metallic material of all the aerospace components;
p l the cooling time per unit size is indicated, l=1, 2,. -%, n;
t represents the time required from time 0 to the time required for the last component to be transported to the next stage;
c represents the energy consumption of a smelting workshop in unit time;
s23, judging whether (k-1) m is less than or equal to n and is true, if true, turning to S22, otherwise outputting Tc=finish x c, taking the Tc=finish x c as fitness value of the chromosome, and calculating fitness value fit= { fit of each chromosome in the current population i |i＝1,2,...,N}；
S24, arranging chromosomes in the population in an ascending order according to fitness values, and marking individuals corresponding to the chromosomes arranged at the front alpha as elite individual groupsIndividuals corresponding to the remaining chromosomes are marked as non-elite individuals
4. The method of claim 3, wherein said performing a TSA algorithm-based crossover operation on said group of non-elite individuals in S3 comprises:
s31, marking the current t generation non-elite individual group asSetting i=αn+1;
s32, aiming at non-elite individualsIf the random number rand (0, 1) is less than or equal to ST, the method is as followsRandomly generating a tree species->Otherwise according to->Randomly generating a tree species->Judging whether the solution of the tree species is better than that of the original tree species, and if so, replacing the solution of the original tree species;
wherein ST denotes a search trend control parameter;a j-th gene for any chromosome other than the best chromosome in the elite group of individuals; alpha ij Is a random scaling factor;
s33, repeating the S32 operation gamma N times;
wherein, gamma represents the tree species generation ratio in the iteration operator based on TSA algorithm; n represents the population size of the BRKGA algorithm;
s34, if i > N, the loop is ended, otherwise let i=i+1 and go to S32.
5. The method of claim 1, wherein said performing a TSA algorithm-based mutation operation on said updated non-elite group of individuals having updated fitness function values at inverse βn in S4 comprises:
S41, marking the current t generation reciprocal beta N non-elite individuals asSetting i= (1- β) n+1;
s42, for X nep Any non-elite individual of (t)Setting j=1;
s43, if random number rand (0, 1) is less than or equal to 0.5, makingNo->
wherein ,the j-th gene of the best chromosome at present; />Any of the j-th genes of the elite panel of individuals currently except the best chromosome;
s44, let j=j+1, judge j is less than or equal to n and is established, if established, go to S43, otherwise finish the cycle.
6. A hybrid algorithm-based collaborative optimization system for production and transportation of a smelting plant, the system comprising:
a processing unit for executing the following steps:
s1, initializing related parameters of a BRKGA algorithm and a TSA algorithm and a population; the related parameters comprise population scale N, elite individual proportion alpha, variant individual proportion beta and variant probability delta of a BRKGA algorithm, the proportion gamma is generated based on tree species in an iterative operator of the TSA algorithm, a trend control parameter ST is searched, and the maximum number Mit of new solutions is generated in the running process of the algorithm; current lot number of aerospace components k=1; start smelting time start for kth aircraft component k =0; the number n of aviation components which are required to be processed in the smelting workshop; the number m of the cooling chambers carrying the dies can be simultaneously placed at most; die size s of first aerospace component l Wherein, l=1, 2, n; the crucible capacity V in the smelting furnace; cooling time per unit size p l Wherein, l=1, 2, n; the energy consumption c of a smelting workshop in unit time;
s2, calculating fitness function values of all chromosomes in the current population, dividing alpha individuals with the best fitness function values into elite individual groups, and dividing the rest individuals into non-elite individual groups;
s3, reserving the elite individuals, and executing cross operation based on TSA algorithm on the non-elite individual group;
s4, calculating fitness function values of all chromosomes in the updated population, and executing variation operation based on TSA algorithm on the updated non-elite individual group with the updated fitness function values being in inverse beta N;
s5, judging whether the number of new solutions generated by the current iteration is smaller than the maximum number of new solutions Mit, if so, turning to S2, otherwise, ending the algorithm execution;
an output unit for outputting an optimal solution X best Corresponding fit best And corresponding schemes.
7. The system of claim 6, wherein the processing unit, when performing initializing the population in S1, generates an initial solution for the population according to a random key encoding rule, comprising:
randomly initializing a population of BRKGA algorithm, wherein the population comprises N chromosomes X= { X i I=1, 2,..n }, wherein the genome of chromosome i is X i ＝{x ij ∈(0,1)|j＝1,2,...,n}；
A numbering set a= { j|j=1, 2,.. i ＝{X i1 ,X i2 ,...,X in X is }, X ij One-to-one correspondence with the component number j;
all elements in the numbering set A are processed according to the corresponding X i The components in the reordered numbering set A are used as the smelting and transportation sequence of the components and are marked asThe corresponding die size set is { s } 1 ,s 2 ,...,s n Cooling time set per unit size is { p } 1 ,p 2 ,...,p n }。
8. The system of claim 6, wherein the processing unit, when performing S2, calculates fitness function values for each chromosome in the current population and classifies the α individuals with the best fitness function values into elite individual groups, and the remaining individuals into non-elite individual groups comprises:
s21, set k=1, let start k ＝0，
Wherein k represents the current batch number; start k Indicating the start smelting time of the kth batch;
s22, order
And let k=k+1, let again
wherein ,
finish k indicating the cooling end time of the k batch;
n represents the number of aviation components which are required to be processed in the smelting workshop;
m represents the number of dies which can be placed simultaneously at most in the cooling chamber carrying the dies;
s l The mold size for the first aerospace component, l=1, 2, n;
v represents the capacity of a crucible in the smelting furnace;
r represents the time required for smelting the metallic material of all the aerospace components;
p l the cooling time per unit size is indicated, l=1, 2,. -%, n;
t represents the time required from time 0 to the time required for the last component to be transported to the next stage;
c represents the energy consumption of a smelting workshop in unit time;
s23, judging whether (k-1) m is less than or equal to n and is true, if true, turning to S22, otherwise outputting Tc=finish x c, taking the Tc=finish x c as fitness value of the chromosome, and calculating fitness value fit= { fit of each chromosome in the current population i |i＝1,2,...,N}；
S24, dyeing the population according to the fitness valueThe chromosomes are arranged in ascending order, and individuals corresponding to the chromosomes arranged in the front alpha are marked as elite individual groupsThe remaining chromosomes are marked as non-elite group of individuals +.>
9. The system of claim 8, wherein the processing unit, when performing S3, performs a TSA algorithm-based interleaving operation on the group of non-elite individuals comprising:
s31, marking the current t generation non-elite individual group asSetting i=αn+1;
s32, aiming at non-elite individualsIf the random number rand (0, 1) is less than or equal to ST, the method is as followsRandomly generating a tree species- >Otherwise according to->Randomly generating a tree species->Judging whether the tree species solution is better than the original tree species solution, if so, replacing the solution of the original tree species;
wherein ST denotes a search trend control parameter;a j-th gene for any chromosome other than the best chromosome in the elite group of individuals; alpha ij Is a random scaling factor;
s33, repeating the S32 operation gamma N times;
wherein, gamma represents the tree species generation ratio in the iteration operator based on TSA algorithm; n represents the population size of the BRKGA algorithm;
s34, if i > N, the loop is ended, otherwise let i=i+1 and go to S32.
10. The system of claim 6, wherein the processing unit, when performing S4, performing a variation operation based on a TSA algorithm on the updated non-elite individual group having an updated fitness function value at an inverse βn comprises:
s41, marking the current t generation reciprocal beta N non-elite individuals asSetting i= (1- β) n+1;
s42, for X nep Any non-elite individual of (t)Setting j=1;
s43, if random number rand (0, 1) is less than or equal to 0.5, makingNo-> wherein ,/>The j-th gene of the best chromosome at present; />As the wayAny of the j-th genes of the elite panel of individuals except the best chromosome;
S44, let j=j+1, judge j is less than or equal to n and is established, if established, go to S43, otherwise finish the cycle.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008133536A (en) * 2006-10-27 2008-06-12 Kobe Steel Ltd Steel having excellent corrosion resistance for ship use
CN103530702A (en) * 2013-10-14 2014-01-22 西安建筑科技大学 Large-scale operation workshop scheduling method based on bottleneck equipment decomposition
CN107392402A (en) * 2017-09-11 2017-11-24 合肥工业大学 Production and transport coordinated dispatching method and system based on modified Tabu search algorithm
CN107506956A (en) * 2017-06-12 2017-12-22 合肥工业大学 Based on improvement particle cluster algorithm supply chain production and transport coordinated dispatching method and system
CN111027773A (en) * 2019-12-11 2020-04-17 河北工业大学 Hybrid optimization method for flexible workshop scheduling based on genetic algorithm

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107301473B (en) * 2017-06-12 2018-06-15 合肥工业大学 Similar parallel machine based on improved adaptive GA-IAGA batch dispatching method and system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008133536A (en) * 2006-10-27 2008-06-12 Kobe Steel Ltd Steel having excellent corrosion resistance for ship use
CN103530702A (en) * 2013-10-14 2014-01-22 西安建筑科技大学 Large-scale operation workshop scheduling method based on bottleneck equipment decomposition
CN107506956A (en) * 2017-06-12 2017-12-22 合肥工业大学 Based on improvement particle cluster algorithm supply chain production and transport coordinated dispatching method and system
CN107392402A (en) * 2017-09-11 2017-11-24 合肥工业大学 Production and transport coordinated dispatching method and system based on modified Tabu search algorithm
CN111027773A (en) * 2019-12-11 2020-04-17 河北工业大学 Hybrid optimization method for flexible workshop scheduling based on genetic algorithm

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