CN108829036B - Optimized scheduling method for metal casting blank cutting forming machining process - Google Patents
Optimized scheduling method for metal casting blank cutting forming machining process Download PDFInfo
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- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/406—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by monitoring or safety
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention relates to an optimized scheduling method for a metal casting blank cutting forming machining process, and belongs to the technical field of intelligent optimized scheduling in a machining production process. The method comprises the steps of determining a scheduling model and an optimization target of a metal casting blank in a cutting forming machining process in a factory, and optimizing the target by using an optimized scheduling method based on an improved multi-objective wolf optimization algorithm; the scheduling model is established according to the number of processes, the processing time and the machine speed of each metal casting blank on each device, the first optimization target in the optimization targets is the minimum maximum completion time, and the second optimization target is the total carbon emission. The invention can reduce the production cost and carbon emission of a factory, improve the production efficiency of the factory, promote the green production mode of the factory, and effectively solve the problems of factory cost waste, low economic benefit and environmental pollution caused by improper processing sequence in the cutting and forming process of the metal casting blank.
Description
Technical Field
The invention relates to an optimized scheduling method for a metal casting blank cutting forming machining process, and belongs to the technical field of intelligent optimized scheduling in a machining production process.
Background
For a long time, the machine manufacturing industry is the prop industry of national economy in China, and mechanical equipment consisting of various parts meets the living and labor requirements of people, improves the labor efficiency, reduces the production cost and promotes the prosperous development of human society civilization. The mechanical parts have a certain shape and are composed of one or more specific surfaces with different properties, such as a spherical surface, a plane surface and the like.
In order to obtain mechanical parts with specific shape, size and surface roughness, which meet the requirements of mechanical equipment, the machining industry often adopts a cutting method to meet the requirements of large-scale industrial production. The cutting processing is a process technology method for cutting redundant materials on a metal casting blank by using a specific cutting tool to finally obtain a mechanical part product meeting the use requirements of various aspects of mechanical equipment. It is an important link in the current mechanical manufacturing process. The metal casting blank cutting forming processing is mainly in a batch production mode, and the processing time of each metal casting blank at each stage is greatly different due to different metal casting blank specifications and process requirements. Different metal strand processing sequences have a large impact on the completion time and the total carbon emission of the mass production plan, which directly affects the production cost and economic efficiency of the plant. At present, a factory dispatcher mainly adopts a minimum completion time-based allocation rule to dispatch, namely, the dispatching is performed according to the ascending sequence of completion time of each metal casting blank, and the ascending sequence is used as a processing sequence. The method can reduce the completion time and the carbon emission of the production plan to a certain extent, but cannot consider the coupling effect between metal casting blank processing sequences, and the scheduling scheme is single, so that the requirements of sudden events and diversity of the production plan cannot be met. Therefore, the optimal scheduling of the metal casting blank forming processing process is extremely important, and a good scheduling scheme can reduce the production cost of a factory to a great extent and improve the economic benefit of the factory.
The invention adopts the arrangement model and designs the optimized scheduling method based on the improved multi-objective wolf optimization algorithm, and can obtain the approximate optimal solution of the scheduling problem of the metal casting blank cutting forming processing process in a shorter time, thereby reducing the production cost and the carbon emission of a factory and improving the economic benefit of the factory.
Disclosure of Invention
The invention aims to solve the scheduling problem of the metal casting blank cutting forming processing process, provides an optimized scheduling method of the metal casting blank cutting forming processing process based on an improved multi-objective grey wolf optimization algorithm, and aims to solve the problems of factory cost waste, low economic benefit, serious environmental pollution and the like caused by improper processing sequencing in the conventional metal casting blank cutting processing process.
The technical scheme of the invention is as follows: an optimized scheduling method for a metal casting blank cutting forming processing process is characterized in that a scheduling model and an optimized target of the metal casting blank cutting forming processing process in a factory are determined, and the target is optimized by using an optimized scheduling method based on an improved multi-objective wolf grey optimization algorithm; wherein the scheduling model is arranged at each equipment according to each metal casting blankThe number of processes, the machining time and the machine speed are established, and the first optimization target in the optimization targets is to minimize the maximum completion time f1＝Cmax(π), the second optimization objective is total carbon emissions f2＝TCE：
min{f1,f2}＝min{Cmax(π),TCE}
Cmax(π)＝Cπ(n),m
Cπ(i),j＝max{Cπ(i-1),j,Cπ(i),j-1}+pπ(i),j/Vπ(i),j
Cπ(1),j＝Cπ(1),j-1+pπ(1),j/Vπ(1),j
Cπ(i),1＝Cπ(i-1),1+pπ(i),1/Vπ(i),1
Cπ(1),1＝pπ(1),1/Vπ(1),1
Wherein n represents the number of metal billets; m represents the number of machines; s represents a machine processing speed set, and each machine has S types of processing speeds to choose from; after each machining operation speed of the machine is selected, the speed cannot be changed before the machining operation is completed, and one solution of the optimal scheduling problem is pi ═ { pi (1), pi (2),. ·, pi (n) }, where pi represents a machining sequence of casting blanks in a factory, and pi (i) represents a casting blank at the ith position in pi; o isi,jRepresenting the operation of the metal strand i on the machine j and specifying that no interruption will be allowed after the start of the operation, each operation Oi,jAll have corresponding standard processing time Pi,jWhen machine j is at speed Vi,jWorking operation Oi,jAt this time, the working operation Oi,jIs changed to Pi,j/Vi,jThe processing energy consumption per unit time is PPj,vEach of the machining operations Oi,jHas a completion time of Ci,j(ii) a When the machine is in an idle state, the machine will be in a standby mode, SPjRepresents the standby energy consumption per unit time;specifying for the same operation Oi,jIf the machine speed is increased, the processing time is correspondingly reduced, and the energy consumption is correspondingly increased, namely the machine speed is increased from v to u, p is simultaneously satisfiedi,j/u＜pi,j/v，PPj,u·pi,j/u＞PPj,v·pi,j/v；Xj(t) a value of 1 indicates that the machine j is in a machining state at time t, and 0 indicates that the machine j is in a standby state; ε represents the conversion factor between energy consumption and carbon emissions, typically 0.7559;
the optimized scheduling method based on the improved multi-target wolf optimization algorithm specifically comprises the following steps:
step1, population initialization: generating an initialization population Initpop by adopting a random method until the number of initial solutions meets the requirement of population scale; wherein the population size is NP;
step2, Archive population initialization: constructing an Archive population by using a non-dominated solution in the initial population, and setting the maximum number of individuals of the Archive population as AP;
step3, population updating: each individual in the population is a solution, the Archive population is grouped by using a roulette strategy, 3 non-dominant solutions, namely wolves where the optimal solution, the optimal solution and the suboptimal solution are respectively marked as alpha, beta and delta wolves, the other individuals are marked as omega wolves, in the food hunting process, the wolves update respective positions under the guidance of the alpha, beta and delta wolves, the food positions, namely the global optimal solution, are approximated, and a guidance equation is as follows:
because the improved multi-target gray wolf optimization algorithm is based on a continuous real number domain and the cutting forming processing process of the metal casting blank is based on a discrete variable, the real number coding is carried out on the procedure sequencing of the metal casting blank by adopting a random key coding mode, and then a one-to-one mapping relation between the real number coding and the integer coding is established according to an LOV rule, so that the conversion from the real number coding to the procedure sequencing of the metal casting blank is realized; dpRepresenting the position of the wolf approaching the food; x represents the position of the gray wolf, XpIndicating preyThe guiding position of (a), i.e. the position of the alpha, beta, delta wolf; t represents the number of iterations; θ represents a control parameter; maxt represents the maximum iteration number; c and A represent guidance coefficients; gamma ray1、γ2Is [0,1 ]]A random number within a range;
step4, Archive population updating: calculating an objective function value of each individual in the wolf group, selecting a non-dominant solution in the wolf group, comparing the non-dominant solution with the individuals in the Archive population one by one, and updating the Archive population if the solution in the Archive population is dominated by the non-dominant solution;
step5, local search based on Swap and Insert: sequentially executing 'Swap' and 'Insert' operations on all individuals in the Archive population, replacing the individuals obtained by local search if the individuals are better than the current individuals, and taking the current population as a new generation Archive population;
step6, end conditions: setting the termination condition as the operation time T of the algorithm to be 50 multiplied by n, and if the algorithm meets the maximum iteration times Maxt or the operation time T, outputting an 'optimal solution'; otherwise, go to Step3, and repeat iteration until the termination condition is satisfied.
The invention has the beneficial effects that: the invention provides a scheduling model and an optimization target of a metal casting blank cutting and forming process, which can obtain an excellent solution of the scheduling problem of the metal casting blank cutting and forming process in a short time, reduce the production cost and carbon emission of a factory, improve the production efficiency of the factory, promote the green production mode of the factory, and effectively solve the problems of factory cost waste, low economic benefit and environmental pollution caused by improper processing and sequencing in the metal casting blank cutting and forming process.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of an optimized scheduling method of the present invention;
FIG. 3 is a schematic representation of the problem solution of the present invention;
FIG. 4 is a schematic diagram of a basic "Insert" field variation of the present invention;
figure 5 is a schematic diagram of a basic "Swap" domain variation of the present invention.
Detailed Description
Example 1: as shown in fig. 1-5, an optimized scheduling method for a metal casting blank cutting and forming process is implemented by determining a scheduling model and an optimized target of a metal casting blank cutting and forming process in a factory, and optimizing the target by using an optimized scheduling method based on an improved multi-target gray wolf optimization algorithm; the scheduling model is established according to the number of processes, the processing time and the machine speed of each metal casting blank on each device, and the first optimization target in the optimization targets is the minimum maximum completion time f1＝Cmax(π), the second optimization objective is total carbon emissions f2＝TCE：
min{f1,f2}＝min{Cmax(π),TCE}
Cmax(π)＝Cπ(n),m
Cπ(i),j＝max{Cπ(i-1),j,Cπ(i),j-1}+pπ(i),j/Vπ(i),j
Cπ(1),j＝Cπ(1),j-1+pπ(1),j/Vπ(1),j
Cπ(i),1＝Cπ(i-1),1+pπ(i),1/Vπ(i),1
Cπ(1),1＝pπ(1),1/Vπ(1),1
Wherein n represents the number of metal billets; m represents the number of machines; s represents a machine processing speed set, and each machine has S types of processing speeds to choose from; after each machining operation speed of the machine is selected, the speed cannot be changed before the machining operation is completed, and one solution of the optimal scheduling problem is pi ═ { pi (1), pi (2),. ·, pi (n) }, where pi represents a machining sequence of casting blanks in a factory, and pi (i) represents a casting blank at the ith position in pi; o isi,jRepresenting the operation of the metal strand i on the machine j and specifying that no interruption will be allowed after the start of the operation, each operation Oi,jAll have corresponding standard processingP betweeni,jWhen machine j is at speed Vi,jWorking operation Oi,jAt this time, the working operation Oi,jIs changed to Pi,j/Vi,jThe processing energy consumption per unit time is PPj,vEach of the machining operations Oi,jHas a completion time of Ci,j(ii) a When the machine is in an idle state, the machine will be in a standby mode, SPjRepresents the standby energy consumption per unit time; specifying for the same operation Oi,jIf the machine speed is increased, the processing time is correspondingly reduced, and the energy consumption is correspondingly increased, namely the machine speed is increased from v to u, p is simultaneously satisfiedi,j/u＜pi,j/v，PPj,u·pi,j/u＞PPj,v·pi,j/v；Xj(t) a value of 1 indicates that the machine j is in a machining state at time t, and 0 indicates that the machine j is in a standby state; ε represents the conversion factor between energy consumption and carbon emissions, typically 0.7559;
the optimized scheduling method based on the improved multi-target wolf optimization algorithm specifically comprises the following steps:
step1, population initialization: generating an initialization population Initpop by adopting a random method until the number of initial solutions meets the requirement of population scale; wherein the population size is NP;
step2, Archive population initialization: constructing an Archive population by using a non-dominated solution in the initial population, and setting the maximum number of individuals of the Archive population as AP;
step3, population updating: each individual in the population is a solution, the Archive population is grouped by using a roulette strategy, 3 non-dominant solutions, namely wolves where the optimal solution, the optimal solution and the suboptimal solution are respectively marked as alpha, beta and delta wolves, the other individuals are marked as omega wolves, in the food hunting process, the wolves update respective positions under the guidance of the alpha, beta and delta wolves, the food positions, namely the global optimal solution, are approximated, and a guidance equation is as follows:
the improved multi-target gray wolf optimization algorithm is based on continuousThe method comprises the steps that a real number domain is adopted, and the metal casting blank cutting forming processing process is based on discrete variables, so that real number coding is carried out on the procedure sequencing of the metal casting blank in a random key coding mode, then a one-to-one mapping relation between the real number coding and the integer coding is established according to an LOV rule, and further the conversion from the real number coding to the metal casting blank procedure sequencing is realized; dpRepresenting the position of the wolf approaching the food; x represents the position of the gray wolf, XpIndicating the leading position of the prey, namely the positions of alpha, beta and delta wolves; t represents the number of iterations; θ represents a control parameter; maxt represents the maximum iteration number; c and A represent guidance coefficients; gamma ray1、γ2Is [0,1 ]]A random number within a range;
step4, Archive population updating: calculating an objective function value of each individual in the wolf group, selecting a non-dominant solution in the wolf group, comparing the non-dominant solution with the individuals in the Archive population one by one, and updating the Archive population if the solution in the Archive population is dominated by the non-dominant solution;
step5, local search based on Swap and Insert: sequentially executing 'Swap' and 'Insert' operations on all individuals in the Archive population, replacing the individuals obtained by local search if the individuals are better than the current individuals, and taking the current population as a new generation Archive population;
step6, end conditions: setting the termination condition as the operation time T of the algorithm to be 50 multiplied by n, and if the algorithm meets the maximum iteration times Maxt or the operation time T, outputting an 'optimal solution'; otherwise, go to Step3, and repeat iteration until the termination condition is satisfied.
Population size NP is set to 50, Archive population size AP is set to 10, machine speed gear is set to S ═ {1,1.1,1.2,1.3,1.4}, PPj,v＝4×v2，SPjThe maximum number of iterations Maxt is set to 100, 1. The values of the objective functions obtained for different problem scales are given in table 1.
TABLE 1 values of objective function obtained for different problem scales
| n×m | 30×5 | 30×10 | 50×5 | 50×10 | 70×5 | 70×10 |
| Cmax(π) | 837 | 770 | 1756 | 1053 | 2743 | 2032 |
| TCE | 8971 | 8432 | 15374 | 12980 | 29564 | 25883 |
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (1)
1. An optimized scheduling method for a metal casting blank cutting forming processing process is characterized by comprising the following steps: the method comprises the steps of determining a scheduling model and an optimization target of a metal casting blank cutting and forming process in a factory, and optimizing the target by using an optimized scheduling method based on an improved multi-objective grey wolf optimization algorithm; the scheduling model is established according to the number of processes, the processing time and the machine speed of each metal casting blank on each device, and the first optimization target in the optimization targets is the minimum maximum completion time f1＝Cmax(π), the second optimization objective is total carbon emissions f2＝TCE：
min{f1,f2}＝min{Cmax(π),TCE}
Cmax(π)＝Cπ(n),m
Cπ(i),j＝max{Cπ(i-1),j,Cπ(i),j-1}+pπ(i),j/Vπ(i),j
Cπ(1),j＝Cπ(1),j-1+pπ(1),j/Vπ(1),j
Cπ(i),1＝Cπ(i-1),1+pπ(i),1/Vπ(i),1
Cπ(1),1＝pπ(1),1/Vπ(1),1
Wherein n represents the number of metal billets; m represents the number of machines; s represents a machine processing speed set, and each machine has S types of processing speeds to choose from; after each machining operation speed of the machine is selected, the speed cannot be changed before the machining operation is completed, and one solution of the optimal scheduling problem is pi ═ { pi (1), pi (2),. ·, pi (n) }, where pi represents a machining sequence of casting blanks in a factory, and pi (i) represents a casting blank at the ith position in pi; o isi,jRepresenting the operation of the metal strand i on the machine j and specifying that no interruption will be allowed after the start of the operation, each operation Oi,jAll have corresponding standardsMachining time Pi,jWhen machine j is at speed Vi,jWorking operation Oi,jAt this time, the working operation Oi,jIs changed to Pi,j/Vi,jThe processing energy consumption per unit time is PPj,vEach of the machining operations Oi,jHas a completion time of Ci,j(ii) a When the machine is in an idle state, the machine will be in a standby mode, SPjRepresents the standby energy consumption per unit time; specifying for the same operation Oi,jIf the machine speed is increased, the processing time is correspondingly reduced, and the energy consumption is correspondingly increased, namely the machine speed is increased from v to u, p is simultaneously satisfiedi,j/u＜pi,j/v，PPj,u·pi,j/u＞PPj,v·pi,j/v；Xj(t) a value of 1 indicates that the machine j is in a machining state at time t, and 0 indicates that the machine j is in a standby state; ε represents the conversion factor between energy consumption and carbon emissions, typically 0.7559;
the optimized scheduling method based on the improved multi-target wolf optimization algorithm specifically comprises the following steps:
step1, population initialization: generating an initialization population Initpop by adopting a random method until the number of initial solutions meets the requirement of population scale; wherein the population size is NP;
step2, Archive population initialization: constructing an Archive population by using a non-dominated solution in the initial population, and setting the maximum number of individuals of the Archive population as AP;
step3, population updating: each individual in the population is a solution, the Archive population is grouped by using a roulette strategy, 3 non-dominant solutions, namely wolves where the optimal solution, the optimal solution and the suboptimal solution are respectively marked as alpha, beta and delta wolves, the other individuals are marked as omega wolves, in the food hunting process, the wolves update respective positions under the guidance of the alpha, beta and delta wolves, the food positions, namely the global optimal solution, are approximated, and a guidance equation is as follows:
improved multi-target gray wolf optimization algorithmBased on a continuous real number domain, the metal casting blank cutting forming processing process is based on discrete variables, so that the real number coding is carried out on the procedure sequencing of the metal casting blank by adopting a random key coding mode, and then a one-to-one mapping relation between the real number coding and the integer coding is established according to an LOV rule, so that the conversion from the real number coding to the procedure sequencing of the metal casting blank is realized; dpRepresenting the position of the wolf approaching the food; x represents the position of the gray wolf, XpIndicating the leading position of the prey, namely the positions of alpha, beta and delta wolves; t represents the number of iterations; θ represents a control parameter; maxt represents the maximum iteration number; c and A represent guidance coefficients; gamma ray1、γ2Is [0,1 ]]A random number within a range;
step4, Archive population updating: calculating an objective function value of each individual in the wolf group, selecting a non-dominant solution in the wolf group, comparing the non-dominant solution with the individuals in the Archive population one by one, and updating the Archive population if the solution in the Archive population is dominated by the non-dominant solution;
step5, local search based on Swap and Insert: sequentially executing 'Swap' and 'Insert' operations on all individuals in the Archive population, replacing the individuals obtained by local search if the individuals are better than the current individuals, and taking the current population as a new generation Archive population;
step6, end conditions: setting the termination condition as the operation time T of the algorithm to be 50 multiplied by n, and if the algorithm meets the maximum iteration times Maxt or the operation time T, outputting an 'optimal solution'; otherwise, go to Step3, and repeat iteration until the termination condition is satisfied.
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