CN111290360B - Multi-objective optimization method for casting production line - Google Patents

Multi-objective optimization method for casting production line Download PDF

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CN111290360B
CN111290360B CN202010215662.6A CN202010215662A CN111290360B CN 111290360 B CN111290360 B CN 111290360B CN 202010215662 A CN202010215662 A CN 202010215662A CN 111290360 B CN111290360 B CN 111290360B
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production line
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CN111290360A (en
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袁小芳
杨育辉
谭伟华
王耀南
肖祥慧
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Hunan University
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    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
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    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

The invention discloses a multi-objective optimization method for a casting production line, which comprises the following steps: obtaining production parameters in a current processing batch of a casting production line; the production parameters comprise the number of the operation machines for processing, the type of the workpieces to be processed, the processing procedures corresponding to the workpieces to be processed and the number of the workpieces to be processed; establishing a target function by taking relaxation time coefficient values which correspond to the to-be-processed working procedures one by one as optimization variables; acquiring at least one set consisting of relaxation time coefficient values according to a multi-objective chaotic optimization algorithm; only one relaxation time coefficient value exists in the set corresponding to the same procedure to be processed; and acquiring an optimal solution set according to the total electric energy consumption and the total processing time. By optimizing the processing time of each procedure, the working time and the idle time of the operation machine are reasonably allocated, and the production electric energy consumption is reduced.

Description

Multi-objective optimization method for casting production line
Technical Field
The invention relates to the technical field of casting production lines, in particular to a multi-objective optimization method for a casting production line.
Background
With the rise of the 4.0 technological revolution of industry, the foundry industry in China develops rapidly, castings are widely applied to industries such as aviation, aerospace, weaponry, ships, automobiles, electronics and the like, however, at present, the operation of a casting production line is mainly manual, so that the production line has low product efficiency, poor consistency and high energy consumption. The requirements of high-end equipment manufacture on precision, efficiency and energy conservation are difficult to meet. The casting production line with high efficiency, high intelligence, energy conservation and environmental protection becomes the trend of future industrial production.
Most of the traditional production scheduling problems aiming at the casting production line only consider the production cost and the production efficiency, and the production line is rebalanced and adjusted by taking the balance rate or the smooth coefficient as an optimization objective function. In recent years, with the concept of energy conservation and environmental protection getting more and more attentive, the requirement for energy conservation requires the production line to reasonably arrange the scheduling and processing of products while ensuring production. In the production scheduling link, it is usually assumed that the work machine performs operations at its maximum speed, and starts to run when scheduling is allowed, otherwise it is idle, which results in unnecessarily high power acceleration and longer idle time, and the idle machine still consumes power, which not only causes resource waste, but also increases production cost.
Disclosure of Invention
In view of the above, embodiments of the present invention provide a multi-objective optimization method for a casting production line, so as to solve the problem that in a production scheduling link of a production line in the prior art, it is usually assumed that a work machine performs operations at its maximum speed, and the work machine starts from when scheduling is allowed, or is idle, which results in unnecessary high power acceleration and longer idle time, and a machine is still consuming power during idle time, which not only causes resource waste, but also increases production cost.
The embodiment of the invention provides a multi-objective optimization method for a casting production line, which comprises the following steps:
obtaining production parameters in a current processing batch of a casting production line; the production parameters comprise the number of the operation machines for processing, the type of the workpieces to be processed, the processing procedures corresponding to the workpieces to be processed and the number of the workpieces to be processed;
defining and counting the types and the number of all workpieces to be processed in the casting production line, the corresponding processing procedures and the number parameters of production machines;
acquiring the total time from startup to shutdown of all the operation machines on the casting production line; summing the time from start to shut down and multiplying the result by the idle power PHObtaining the total idle electric energy consumption of the casting production line;
establishing a target function by taking the relaxation time coefficient values corresponding to the processing procedures as optimization variables;
acquiring at least one set consisting of relaxation time coefficient values according to a multi-objective chaotic optimization algorithm; there is only one relaxation time coefficient value in a set corresponding to the same process;
and acquiring an optimal solution set according to the total electric energy consumption and the total processing time.
Optionally, before establishing the objective function by using the relaxation time coefficient values corresponding to the processing procedures as optimization variables, the method further includes constructing a production scheduling model:
the work machine for machining has M pieces, and is denoted as work machine M ═ M1,M2,…，MmN ═ N, which requires the processing of N workpieces to be processed1,N2,...,NnH, the kth workpiece NkComprising qkWorking procedure
Figure GDA0002982259010000021
Jth working procedure OT of kth workpiece(k，j)The fastest allowable machining time is
Figure GDA0002982259010000022
(k＝1,2,...,n,j＝1，2，...，qk) Working procedure OT(k，j)The slowest allowable processing time is
Figure GDA0002982259010000023
Recording the jth production procedure O of the kth workpiece to be produced(k，j)The machining end time of (A) is C(k，j)Production process O of the jth workpiece to be produced(k，j)The processing time of (A) is OT(k，j)Work machine MSThe processing number of the arranged working procedures is XsThe machining end time of the x-th step of machining on the working machine Ms is
Figure GDA0002982259010000031
The processing time of the xth process on the working machine Ms is as follows
Figure GDA0002982259010000032
The moment when the last procedure is finished on the production line is ClastAnd then:
C＝Clast (1)
Figure GDA0002982259010000033
Figure GDA0002982259010000034
Figure GDA0002982259010000035
wherein C is the total processing time to complete all processing procedures, E(k，j)To complete the working procedure O(k，j)Process power consumption of c1，c2，c3，c4，c5Is a constant associated with the working process and the working machine, alpha(k，j)Is a value of a time relaxation coefficient, EidleFor total idle power consumption of the production line, PHThe power at which the work machine is idle.
Optionally, the establishing the objective function by using the relaxation time coefficient values corresponding to the processing procedures in a one-to-one manner as the optimization variables further comprises:
obtaining a minimum total processing time objective function:
Min C (5)
acquiring a total electric energy consumption target function of a production line:
Figure GDA0002982259010000036
where Min C is the minimum total processing time, EtotalThe total electric energy consumption of the production line.
Optionally, after the production scheduling model is built, setting a constraint condition:
for the first workpiece to be machined, the current machining procedure of the first workpiece to be machined can be machined only after the previous machining procedure of the first workpiece to be machined is finished:
C(k，j)－C(k，j-1)≥OT(k，j)，(k＝1，2，...，n，j＝2，3，...，qk)；
for a first work machine, the current working process of the first work machine must be completed after the previous process of the first work machine:
Figure GDA0002982259010000037
the value range of the relaxation time coefficient value is more than or equal to 0 and less than or equal to the difference between the fastest processing time and the slowest processing time of a single processing procedure:
Figure GDA0002982259010000041
optionally, the obtaining at least one set of relaxation time coefficient values according to the multi-objective chaotic optimization algorithm comprises:
initializing a total iteration number H and a parallel number P of a multi-target chaotic optimization algorithm;
based on the number of parallel rows P, generating
Figure GDA0002982259010000042
The value range is [0,1 ]]As the initial value of the chaotic sequence, and obtaining
Figure GDA0002982259010000043
Taking the chaotic variables as a first parent population;
linearly mapping the chaotic variable to a corresponding optimized variable value range to obtain a P group value solution of an optimized relaxation time coefficient value;
solving and calculating the corresponding P groups of total processing time and electric energy consumption according to the P groups of values of the optimized relaxation time coefficient;
screening the P group value solutions of the optimized relaxation time coefficient value according to a preset time range and a preset electric energy consumption range respectively;
the first parent population is mutated through the chaotic sequence to generate a child population, and the child population is used as a second parent population for a new round of iterative search:
Zoffspring＝4*Zparent*(1-Zparent) (7)
wherein Z isparentIs the firstParent population, ZoffspringIs a progeny population.
Optionally, the screening the P group solutions for optimizing the relaxation time coefficient value according to the preset time range and the preset power consumption range respectively includes:
sorting the P group value solutions according to the non-dominant relationship;
the first non-dominant leading edge value solution is retained.
Optionally, after retaining the first non-dominant front edge value solution, further comprising:
carrying out congestion degree sequencing on the first non-dominated leading edge value solution;
and reserving the first non-dominant leading edge value solution with the lowest congestion degree as an iteration value solution.
Optionally, the sorting the P group solution by non-dominated relationship includes:
the number X of the dominated solution of each solution in the P group value solutiondInitialized to 0, and the dominance solution set SdInitializing to phi;
for the first solution I therein1If the dominant first solution I is found1Second solution I of2Then, the first solution I1Is determined by the number of solutions XdPlus 1, i.e. Xd＝Xd+1；
If the first solution I1Dominating the second solution I2Then the second solution I2Put into the first solution I1Dominant solution set S ofdIn, i.e. Sd＝Sd∪{I2}。
Optionally, the performing congestion degree sorting on the first non-dominant leading edge solution includes:
initializing the congestion degree Y [ i ] of each solution in the first non-dominated leading edge value solution to 0;
substituting all the first non-dominated leading edge value solutions into a minimum total processing time objective function and a production line total electric energy consumption objective function, and respectively sequencing from small to large according to the minimum total processing time objective function value and the production line total electric energy consumption objective function value;
setting the crowdedness of the first dereferencing solution and the last dereferencing solution corresponding to the minimum total processing time objective function after sorting to be infinite;
setting the crowdedness of the first value solution and the last value solution corresponding to the total electric energy consumption objective function value of the production line after sequencing to be infinite;
respectively obtaining the crowdedness of the value solutions on the non-boundary corresponding to the minimum total processing time objective function and the production line total electric energy consumption objective function value:
Figure GDA0002982259010000051
summing the single crowdedness obtained by the solution according to the minimum total processing time objective function and the production line total electric energy consumption objective function to obtain the total crowdedness;
wherein, Y [ i ]]M represents the value of the ith solution on the mth objective function,
Figure GDA0002982259010000052
represents the maximum value on the mth objective function among all solutions,
Figure GDA0002982259010000053
represents the minimum value on the mth objective function among all solutions.
Optionally, after the first non-dominant leading edge solution with the lowest congestion degree is retained as the iterative solution, the method further includes:
judging whether the number of iteration value solutions and the total iteration times exceed preset values or not;
if the number of the iteration value solutions or the total iteration times exceeds a preset value, stopping the iteration;
keeping an iteration value solution with the lowest congestion degree; and the relaxation time coefficient value corresponding to the iterative value solution is the optimal solution of the casting production line under the condition of considering the total processing time and the electric energy consumption.
The embodiment of the invention has the following beneficial effects:
by obtaining the production parameters in the current processing batch of the casting production line, a multi-objective optimized production dispatching model of the casting production line is constructedThe objective function is established with a minimum of electric energy consumption and a minimum of total processing time, and then in the solving process, with a relaxation time coefficient alpha related to the specific processing time of the process(k，j)The value of (a) is used as an optimization variable, and a multi-objective chaotic optimization algorithm is utilized to optimize and determine a relaxation time coefficient alpha(k，j)The casting production line production and processing scheme with short total processing time and less electric energy consumption is obtained, and the multi-objective optimization solving problem of the casting production line is solved. The production and processing scheme of the casting production line obtained by the method ensures the production efficiency of the production line and reduces the power consumption, thereby reducing the production cost.
Drawings
The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:
FIG. 1 is a flow chart of a multi-objective optimization method for a casting line in an embodiment of the present invention;
FIG. 2 is a flow chart of a multi-objective chaotic optimization algorithm according to an embodiment of the present invention;
FIG. 3 is a graph showing the relationship between processing time and power consumption for a single process step in an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating an ordering of non-dominated relationships and congestion levels according to an embodiment of the invention;
FIG. 5 is a diagram illustrating a multi-objective optimization terminal of a casting production line according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a multi-objective optimization method for a casting production line, which comprises the following steps of:
and S100, acquiring production parameters in the current processing batch of the casting production line. The production parameters include the number of working machines for machining, the type of the workpiece to be machined, the machining process corresponding to the workpiece to be machined, and the number of the workpieces to be machined.
In this embodiment, the production strategy needs to be adjusted in real time according to the actual order requirement, so that the type and the number of the workpieces to be processed, the corresponding processing procedure, and the number of the working machines that can be put into production need to be obtained. Because the working procedure of the workpiece to be processed and the power range of the corresponding working machine are determined, the processing time consumption range requirements of different working procedures can be obtained.
Defining and counting the types and the number of all workpieces to be processed in a casting production line, corresponding processing procedures and the number parameters of production machines;
acquiring the total time from startup to shutdown of all operation machines on a casting production line; summing the time from start to shut down and multiplying the result by the idle power PHAnd obtaining the total idle electric energy consumption of the casting production line.
And step S200, establishing an objective function by taking the relaxation time coefficient values which are in one-to-one correspondence with the processing procedures as optimization variables.
In this embodiment, the value of the relaxation time coefficient refers to a time scheduling variable parameter of the current processing procedure of the same operation machine or the same workpiece to be processed on the basis of the shortest production time. For a single work machine or workpiece to be machined, the total power consumption of the work machine or production line during a machining cycle is the power consumption of the work machine in the working process plus the power consumption in the idle state. By establishing the objective function by taking the relaxation time coefficient values of the processing procedures as optimization variables, under the condition of the same type and number of workpieces to be processed, the corresponding processing procedures and the number of working machines which can be put into production, the total electric energy consumption and the total processing time consumption of the production line corresponding to different relaxation time coefficient values can be calculated.
And step S300, acquiring at least one set consisting of relaxation time coefficient values according to a multi-objective chaotic optimization algorithm. There is only one relaxation time coefficient value in a set for the same process.
In this embodiment, a plurality of solutions of the relaxation time coefficient value corresponding to a single process may be obtained by the multi-objective chaotic optimization algorithm. The relaxation time coefficient values are grouped into sets according to the processes, one set includes relaxation time coefficient values corresponding to all the processes one by one, and one set has one and only one solution for the relaxation time coefficient value corresponding to one process.
And S400, acquiring an optimal solution set according to the total electric energy consumption and the total processing time.
In the embodiment, the optimal solution in the optimized relaxation time coefficient values corresponding to shorter total processing time and less electric energy consumption is selected to determine the production processing scheme of the casting production line, and a proper procedure processing time is selected while the production requirement is ensured, so that the aim of reducing the processing electric energy consumption is fulfilled.
As an optional implementation manner, before step S200, the method further includes: and step S110, constructing a production scheduling model. The specific mode is as follows:
the work machine for machining has M pieces, and is denoted as work machine M ═ M1,M2,…，MmN ═ N, which requires the processing of N workpieces to be processed1,N2,...,NnH, the kth workpiece NkComprising qkWorking procedure
Figure GDA0002982259010000081
Jth working procedure OT of kth workpiece(k，j)The fastest allowable machining time is
Figure GDA0002982259010000082
(k＝1,2,...,n,j＝1,2,...,qk) Working procedure OT(k，j)The slowest allowable processing time is
Figure GDA0002982259010000083
Recording the jth production procedure O of the kth workpiece to be produced(k，j)The machining end time of (A) is C(k，j)Production process O of the jth workpiece to be produced(k，j)The processing time of (A) is OT(k，j)Work machine MSThe processing number of the arranged working procedures is XsThe machining end time of the x-th step of machining on the working machine Ms is
Figure GDA0002982259010000091
The processing time of the xth process on the working machine Ms is as follows
Figure GDA0002982259010000092
The moment when the last procedure is finished on the production line is ClastAnd then:
C＝Clast (1)
Figure GDA0002982259010000093
Figure GDA0002982259010000094
Figure GDA0002982259010000095
wherein C is the total processing time to complete all processing procedures, E(k,j)To complete the working procedure O(k，j)Process power consumption of c1,c2,c3,c4，c5Is a constant associated with the working process and the working machine, alpha(k,j)Is a value of a time relaxation coefficient, EidleFor total idle power consumption of the production line, PHThe power at which the work machine is idle.
In this embodiment, all the workpieces to be processed involved in the production line are first processedDefining and counting the parameters such as type, quantity, corresponding processing procedure and production machine quantity. The m C in the formula (4) is the total time from start to shut down of all the working machines on the production line, and the following summation formula is the time of the real machining process in the time period from start to shut down multiplied by the idle power PHI.e. the total idle power consumption of the production line.
As an optional implementation manner, step S200 further includes:
step S201, obtaining a minimum total machining time objective function:
Min C (5)
step S202, obtaining a total electric energy consumption objective function of the production line:
Figure GDA0002982259010000096
where Min C is the minimum total processing time, EtotalThe total electric energy consumption of the production line.
In the present embodiment, the total power consumption value of the casting line in the case of determining the kind, number of working machines, process, and machining time consumption of the workpieces to be machined is calculated by formula (6) on the premise that the total machining time is minimized.
As an optional implementation manner, after step S110, the method further includes:
step S111, setting constraint conditions:
for the first workpiece to be machined, the current machining procedure of the first workpiece to be machined can be machined only after the previous machining procedure of the first workpiece to be machined is finished:
C(k，j)－C(k，j-1)≥OT(k,j)，(k＝1，2，...，n，j＝2，3，...，qk)。
for a first work machine, the current working process of the first work machine must be completed after the previous process of the first work machine:
Figure GDA0002982259010000101
the value range of the relaxation time coefficient value is more than or equal to 0 and less than or equal to the difference between the fastest processing time and the slowest processing time of a single processing procedure:
Figure GDA0002982259010000102
in the embodiment, the conflict task of the production line is avoided through the first two constraint conditions, and the whole-course stable work of the production line is ensured. FIG. 2 is a flowchart illustrating a multi-objective chaotic optimization algorithm according to an embodiment of the present invention. And (3) constraining the value of the relaxation time coefficient value, and combining the formula (3), when the value of each processing procedure of the operation machine is consumed, the value falls between the fastest processing time and the slowest processing time required by the processing procedure of the corresponding operation machine, so that the processing time which cannot occur in the actual processing process is avoided.
As an alternative embodiment, step S300 includes:
step S301, initializing the total iteration times H and the parallel number P of the multi-objective chaotic optimization algorithm.
Step S302, according to the parallel number P, generating
Figure GDA0002982259010000103
The value range is [0,1 ]]As the initial value of the chaotic sequence, and obtaining
Figure GDA0002982259010000104
And taking the chaotic variable as a first parent population.
Step S303, mapping the chaotic variable to a corresponding optimized variable value range to obtain a P group value solution of the optimized relaxation time coefficient value.
And step S304, calculating the corresponding P groups of total processing time and electric energy consumption according to the P groups of value solutions of the optimized relaxation time coefficient value.
Step S305, the P group value solutions of the optimized relaxation time coefficient value are screened according to the preset time range and the preset electric energy consumption range respectively.
Step S306, the first parent population is mutated through the chaotic sequence to generate a child population, and the child population is used as a second parent population for a new round of iterative search:
Zoffspring＝4*Zparent*(1-Zparent) (7)
wherein Z isparentIs the first parent population, ZoffspringIs a progeny population.
In the present embodiment, fig. 3 shows a graph of the relationship between the processing time and the power consumption of a single process, and as the processing time increases, the power consumption of the working machine decreases and then increases, so the target value solution in the present embodiment is an optimized relaxation time coefficient value corresponding to a smaller power consumption and a smaller processing time, and the total power consumption are optimized by the power consumption and the time consumption of each process. Through the chaos sequence formula (7), after the value interval is determined, only a part of values are required to be selected from the value interval, and the whole value interval can be obtained through iteration.
As an alternative embodiment, step S305 includes:
and S3051, sequencing the non-dominated relationship of the P group value solutions.
And step S3052, retaining the value solution of the first non-dominant leading edge.
In this embodiment, if the number of dominated solutions for the first optimized slack time coefficient value is 0, the first optimized slack time coefficient value belongs to the first non-dominated front edge and the first optimized slack time coefficient value is saved. As shown in fig. 4, the first non-dominant leading edge solutions are directly selectable optimized relaxation time coefficient values, and the electric energy consumption of the production line production processing schemes corresponding to the solutions is reduced compared with the existing processing schemes.
And S3053, reserving the first non-dominated front edge dereferencing solution with the lowest crowding degree as an iterative dereferencing solution.
In this embodiment, only the first non-dominant leading edge value solution with the lowest congestion degree is reserved for the next iteration.
As an optional implementation manner, after step S3053, the method further includes:
and S3054, carrying out congestion degree sequencing on the first non-dominated leading edge dereferencing solution.
And S3055, selecting a first non-dominant leading edge value solution with the lowest crowding degree.
In this embodiment, the congestion degree sorting is performed on the first non-dominant leading edge value solution, so as to obtain an optimal solution. As shown in fig. 4, only the optimal solution F1 of the first non-dominant leading edge valued solutions is retained.
As an alternative embodiment, step S3051 includes:
the number X of the dominated solution of each solution in the P group value solutiondInitialized to 0, and the dominance solution set SdInitialized to Φ.
For the first solution I therein1If the dominant first solution I is found1Second solution I of2Then, the first solution I1Is determined by the number of solutions XdPlus 1, i.e. Xd＝Xd+1。
If the first solution I1Dominating the second solution I2Then the second solution I2Put into the first solution I1Dominant solution set S ofdIn, i.e. Sd＝Sd∪{I2}。
In this embodiment, the non-dominant relationship is initialized first, and then the P-group solution is traversed to obtain the dominant solution set Sd＝Sd∪{I2And obtaining the relevance between the value solutions.
As an alternative embodiment, step S3054 includes:
the congestion degree Y [ i ] of each of the first non-dominant leading edge value solutions is initialized to 0.
Substituting all the first non-dominated leading edge value solutions into a minimum total processing time objective function and a production line total electric energy consumption objective function, and respectively sequencing from small to large according to the minimum total processing time objective function value and the production line total electric energy consumption objective function value;
setting the crowdedness of the first dereferencing solution and the last dereferencing solution corresponding to the minimum total processing time objective function after sorting to be infinite;
setting the crowdedness of the first value solution and the last value solution corresponding to the total electric energy consumption objective function value of the production line after sequencing to be infinite;
respectively obtaining the crowdedness of the value solutions on the non-boundary corresponding to the minimum total processing time objective function and the production line total electric energy consumption objective function value:
Figure GDA0002982259010000131
summing the single crowdedness obtained by the solution according to the minimum total processing time objective function and the production line total electric energy consumption objective function to obtain the total crowdedness;
wherein, Y [ i ]]M represents the value of the ith solution on the mth objective function,
Figure GDA0002982259010000132
represents the maximum value on the mth objective function among all solutions,
Figure GDA0002982259010000133
represents the minimum value on the mth objective function among all solutions.
In this embodiment, there are two objective functions, and there may be a case where the value-taking scheme is excellent on one objective function and performs poorly on the other objective function, and the application effects of various schemes can be compared when there are multiple targets by the congestion degree calculation. And the total congestion degree is the sum of the single congestion degrees corresponding to each value solution, and an optimal scheme is selected according to the total congestion degree.
As an optional implementation manner, after step S3053, the method further includes:
and judging whether the number of iteration value solutions and the total iteration times exceed preset values.
And if the number of the iteration value solutions or the total iteration times exceeds a preset value, stopping the iteration.
Keeping an iteration value solution with the lowest congestion degree; and the relaxation time coefficient value corresponding to the iterative value solution is the optimal solution of the casting production line under the condition of considering the total processing time and the electric energy consumption. .
In this embodiment, when the algorithm is executed to the maximum iteration number or a sufficient number of iteration solutions are obtained, the iteration is stopped, all the iteration solutions are subjected to congestion degree sorting, and the optimal solution is selected as the parameter optimization relaxation time coefficient value α(k，j)The solution of (1).
In the embodiment, a multi-objective optimization production scheduling model of the casting production line is constructed by acquiring production parameters in the current processing batch of the casting production line, so as to establish an objective function by minimizing the electric energy consumption and minimizing the total processing time, and then a relaxation time coefficient alpha related to the specific processing time of the process is used in the solving process(k，j)The value of (a) is used as an optimization variable, and a multi-objective chaotic optimization algorithm is utilized to optimize and determine a relaxation time coefficient alpha(k，j)The value of (2) is obtained, and the casting production line production and processing scheme with short total processing time and less electric energy consumption is obtained, so that the multi-objective optimization solving problem of the casting production line is solved. The production and processing scheme of the casting production line obtained by the method ensures the production efficiency of the production line and reduces the power consumption, thereby reducing the production cost.
The embodiment of the invention also provides a casting line multi-objective optimization terminal, as shown in fig. 5, the casting line multi-objective optimization terminal may include a processor 51 and a memory 52, wherein the processor 51 and the memory 52 may be connected by a bus or in other manners, and fig. 5 illustrates the connection by the bus.
The processor 51 may be a Central Processing Unit (CPU). The Processor 51 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 52, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor 51 executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory 52, namely, implementing the casting line multi-objective optimization method in the above-described method embodiments.
The memory 52 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 51, and the like. Further, the memory 52 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 52 may optionally include memory located remotely from the processor 51, and these remote memories may be connected to the processor 51 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more modules are stored in the memory 52 and, when executed by the processor 51, perform the casting line multi-objective optimization method of the embodiment shown in FIGS. 1-3.
The specific details of the multi-objective optimization terminal for the casting production line can be understood by referring to the corresponding related descriptions and effects in the embodiments shown in fig. 1 to 4, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, and the program can be stored in a computer readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.

Claims (10)

1. A multi-objective optimization method for a casting production line is characterized by comprising the following steps:
obtaining production parameters in a current processing batch of a casting production line; the production parameters comprise the number of working machines for processing, the type of workpieces to be processed, processing procedures corresponding to the workpieces to be processed and the number of the workpieces to be processed;
defining and counting the types and the number of all workpieces to be processed in the casting production line, the corresponding processing procedures and the number parameters of production machines;
acquiring the total time from startup to shutdown of all the operation machines on the casting production line; summing the time from start to shut down and multiplying the result by the idle power PHObtaining the total idle electric energy consumption of the casting production line;
establishing a target function by taking the relaxation time coefficient values which are in one-to-one correspondence with the processing procedures as optimization variables;
acquiring at least one set consisting of the relaxation time coefficient values according to a multi-objective chaotic optimization algorithm; there is only one said relaxation time coefficient value in said one set corresponding to the same said process step;
and acquiring an optimal solution set according to the total electric energy consumption and the total processing time.
2. The multi-objective optimization method for a casting line according to claim 1, further comprising, before establishing the objective function using the relaxation time coefficient values corresponding to the machining processes as optimization variables, constructing a production scheduling model:
the work machine for machining has M pieces, and is denoted as work machine M ═ M1,M2,…,MmN ═ N, which requires the processing of N workpieces to be processed1,N2,...,NnH, the kth workpiece NkComprising qkWorking procedure
Figure FDA0002982259000000011
Jth working procedure OT of kth workpiece(k，j)The fastest allowable machining time is
Figure FDA0002982259000000012
(k＝1，2，...，n，j＝1，2，...，qk) The working procedure OT(k，j)The slowest allowable processing time is
Figure FDA0002982259000000021
Recording the jth production procedure O of the kth workpiece to be produced(k，j)The machining end time of (A) is C(k，j)The jth production process O of the kth workpiece to be produced(k，j)The processing time of (A) is OT(k，j)The number of processes arranged on the working machine Ms is XsThe machining end time of the x-th step of machining on the working machine Ms is
Figure FDA0002982259000000022
The processing time of the xth process on the working machine Ms is as follows
Figure FDA0002982259000000023
The moment when the last procedure is finished on the production line is ClastAnd then:
C＝Clast (1)
Figure FDA0002982259000000024
Figure FDA0002982259000000025
Figure FDA0002982259000000026
wherein C is the total processing time to complete all processing procedures, E(k，j)To complete the working procedure O(k，j)Process power consumption of c1，c2，c3，c4，c5Is a constant, a, associated with the working process and the working machine(k，j)As said relaxation time coefficient value, EidleFor total idle power consumption of the production line, PHIs the power at which the work machine is idle.
3. The multi-objective optimization method for a casting line according to claim 2, wherein the establishing of the objective function using the relaxation time coefficient values corresponding to the machining processes as the optimization variables further comprises:
obtaining a minimum total processing time objective function:
Min C (5)
acquiring a total electric energy consumption target function of a production line:
Figure FDA0002982259000000027
wherein Min C is the minimum total processing time, EtotalThe total power consumption of the production line.
4. The multi-objective optimization method for a casting production line according to claim 2, further comprising setting constraints after the construction of the production scheduling model:
for a first workpiece to be machined, a current machining process of the first workpiece to be machined must be completed after a previous machining process of the first workpiece to be machined is completed, and the current machining process of the first workpiece to be machined comprises the following steps:
C(k，j)-C(k，j-1)≥OT(k，j)，(k＝1，2，...，n，j＝2，3，...，qk)；
for a first work machine, the current work process of the first work machine must be completed after the previous process of the first work machine:
Figure FDA0002982259000000031
the value range of the relaxation time coefficient value is more than or equal to 0 and less than or equal to the difference between the fastest processing time and the slowest processing time of a single processing procedure:
Figure FDA0002982259000000032
5. the multi-objective optimization method for a casting production line according to claim 3, wherein obtaining at least one set of relaxation time coefficient values according to a multi-objective chaotic optimization algorithm comprises:
initializing the total iteration times H and the parallel number P of the multi-target chaotic optimization algorithm;
based on the number of parallel rows P, generating
Figure FDA0002982259000000033
The value range is [0,1 ]]As the initial value of the chaotic sequence, and obtaining
Figure FDA0002982259000000034
Taking the chaotic variables as a first parent population;
linearly mapping the chaotic variable to a corresponding optimized variable value range to obtain a P group value solution of an optimized relaxation time coefficient value;
solving and calculating the corresponding P groups of total processing time and electric energy consumption according to the P groups of values of the optimized relaxation time coefficient;
screening the P group value solutions of the optimized relaxation time coefficient value according to a preset time range and a preset electric energy consumption range respectively;
and (3) carrying out variation on the first parent population through the chaotic sequence to generate a child population, and taking the child population as a second parent population of a new round of iterative search:
Zoffspring＝4*Zparent*(1-Zparent) (7)
wherein Z isparentIs the first parent population, ZoffspringIs the progeny population.
6. The multi-objective optimization method for the casting production line according to claim 5, wherein the screening of the solution of the P group of the optimized relaxation time coefficient values according to the preset time range and the preset power consumption range respectively comprises:
sorting the P group value solutions according to the non-dominant relationship;
the first non-dominant leading edge value solution is retained.
7. The multi-objective optimization method for a casting line of claim 6, further comprising, after retaining the first non-dominant-front value solution:
carrying out congestion degree sorting on the first non-dominated leading edge value solution;
and reserving the first non-dominant leading edge value solution with the lowest congestion degree as an iteration value solution.
8. The multi-objective optimization method for a casting production line of claim 6, wherein the non-dominated sorting of the P group solutions comprises:
the dominated solution number X of each solution in the P group value solutiondInitialized to 0, and the dominance solution set SdInitializing to phi;
for the first solution I therein1If the solution I is found to dominate the first solution I1Second solution I of2Then the first solution I1Is determined by the number of solutions XdPlus 1, i.e. Xd＝Xd+1；
If the first solution I1Dominating the second solution I2Then the second solution I is2Put into the first solution I1Dominant solution set S ofdIn, i.e. Sd＝Sd∪{I2}。
9. The multi-objective optimization method for a casting line of claim 7, wherein the crowdedness ranking the first non-dominated front solutions comprises:
initializing the congestion degree Y [ i ] of each solution in the first non-dominant leading edge value solution to 0;
substituting all the first non-dominated leading edge value solutions into the minimum total processing time objective function and the production line total electric energy consumption objective function, and respectively sequencing from small to large according to the minimum total processing time objective function value and the production line total electric energy consumption objective function value;
setting the crowdedness of the first dereferencing solution and the last dereferencing solution corresponding to the minimum total processing time objective function after sorting to be infinite;
setting the crowdedness of the first value solution and the last value solution corresponding to the total electric energy consumption objective function value of the production line after sequencing to be infinite;
respectively obtaining the crowdedness of the value solutions on the non-boundary corresponding to the minimum total processing time objective function and the production line total electric energy consumption objective function value:
Figure FDA0002982259000000051
summing the single crowdedness obtained by the solution according to the minimum total processing time objective function and the production line total electric energy consumption objective function to obtain total crowdedness;
wherein, Y [ i ]]M represents the value of the ith solution on the mth objective function,
Figure FDA0002982259000000052
represents the maximum value on the mth objective function in all solutions,
Figure FDA0002982259000000053
represents the minimum of said solutions on said mth objective function.
10. The multi-objective optimization method for a casting production line according to claim 7, wherein after the first non-dominated leading edge solution with the lowest crowding degree is retained as an iterative solution, the method further comprises:
judging whether the number of the iteration value solutions and the total iteration times exceed preset values or not;
if the number of the iteration value solutions or the total iteration times exceeds a preset value, stopping the iteration;
keeping the iteration value solution with the lowest crowding degree; and the relaxation time coefficient value corresponding to the iterative value solution is the optimal solution of the casting production line under the condition of considering the total processing time and the electric energy consumption.
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