CN111367247A - Productivity optimization method for automatic casting mixed flow production line - Google Patents
Productivity optimization method for automatic casting mixed flow production line Download PDFInfo
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Abstract
The invention discloses a capacity optimization method for an automatic casting mixed flow production line, which comprises the following steps: acquiring the type of the workpieces to be produced and the total number of the workpieces to be produced corresponding to the type of the workpieces to be produced; and acquiring the workpiece type, the quantity set process of the single production batch and the maximum production quantity of the workpieces of the single production batch by a parallel chaotic optimization algorithm. The total production tasks are produced in batches, the quantity of workpieces produced in the batches and the processing sequence of the working procedures corresponding to the quantity of the workpieces are optimized respectively by using a parallel chaotic optimization algorithm (MPCOA), and the production efficiency in the production batches is ensured by reasonably distributing the quantity of the workpieces produced in the batches and reasonably arranging the processing sequence of the working procedures, so that the overall capacity of the production line is effectively optimized.
Description
Technical Field
The invention relates to the technical field of automatic production lines, in particular to a capacity optimization method for an automatic casting mixed flow production line.
Background
Casting production is an important method for manufacturing metal parts with complex structures, and castings are widely used in the fields of aerospace, ships, automobiles and the like. The casting production has the characteristics of large production batch, multiple product types, frequent product type change and the like, efficient planning and scheduling are difficult to realize in actual production, waste of time and production resources is caused, meanwhile, the process of the casting production process is complex, the resource consumption is huge, and low-efficiency casting production is very not beneficial to sustainable development, so that the method has important significance for optimizing the productivity of the casting production line.
The casting production line usually adopts a mixed flow production mode, the planned scheduling of the mixed flow production belongs to the NP difficult problem, and the problem of 'combined explosion' occurs when the size of workpieces reaches a certain quantity. At present, the solving time of the traditional algorithm (such as a branch and bound method, a plane segmentation method, a hidden enumeration method and the like) is exponentially increased along with the enlargement of the problem scale, and the problem of large-scale mixed flow production scheduling optimization is difficult to effectively solve.
Therefore, how to reasonably distribute the number of the workpieces in the production batch and reasonably arrange the processing sequence of the workpieces, improve the production efficiency of the mixed flow casting production line, and finally achieve the purpose of optimizing the production capacity of the production line is a problem that needs to be solved by the technical staff in the field.
Disclosure of Invention
In view of this, the embodiment of the invention provides a capacity optimization method for an automatic mixed casting flow production line, so as to solve the problem that in the prior art, the solution time of a traditional algorithm (such as a branch and bound method, a cut plane method, a hidden lift method and the like) increases exponentially with the enlargement of the problem scale, and the problem of scheduling optimization of large-scale mixed flow production is difficult to effectively solve.
The embodiment of the invention provides a capacity optimization method for an automatic casting mixed flow production line, which comprises the following steps:
acquiring the type of the workpieces to be produced and the total number of the workpieces to be produced corresponding to the type of the workpieces to be produced;
and acquiring the workpiece type, the quantity set process of the single production batch and the maximum production quantity of the workpieces of the single production batch by a parallel chaotic optimization algorithm.
Optionally, before obtaining the workpiece type in the single production batch, the quantity set process of the single production batch, and the maximum quantity of workpiece production of the single production batch through the parallel chaotic optimization algorithm, the method further includes: the capacity optimization model of the automatic casting mixed flow production line is constructed, and the capacity optimization model comprises the following steps:
setting production parameters in a single production batch;
the production parameters include: the number N of types of workpieces to be produced; production maximum number J of nth type workpieces in single production batchn(N ═ 1,2, …, N); producing the ith workpiece to include QiA step of QiEach process constitutes a process set Pi(ii) a The jth process of the ith workpiece is denoted as process pi,j(ii) a Setting M production machines, the kth production machine being numbered Mk(ii) a Set maximum number of workpiece productions U for a single production batchmax；
With the number of workpieces to be produced in a single production batch R ═ R1，R2，…，RNAnd taking the corresponding procedure processing sequence scheme op as an optimized decision variable, and recording: step pi,jHas a completion time of Ci,jProcedure pi,jIn the production machine MkHas a processing time of ti,j,kIn the production machine MkThe number of the processes arranged is XkIn the production machine MkThe completion time of the x-th working procedure is CMk,xIn the production machine MkThe processing time of the x procedure of the upper processing is tMk,xThe total number of workpieces produced in a batch is R ═ ∑ Rn(N ═ 1,2, …, N); if step p is carried outi,jAt the kth station production machine MkAt the x-th position of (e), then yi,j,k,xIs 1, otherwise is 0, then:
Cmax＝max{Ci,j}，(i＝1,2,…R*；j＝1,2,…,Qi) (2)；
wherein, CTpThe total process time is the sum of the processing time of all processes of all workpieces in a production batch on the corresponding production machines; cmaxThe batch production time, i.e. the maximum time for completing all processes in a production batch;
establishing an objective function:
wherein E is the production efficiency of the production batch, the number M of the production machines is a constant, and the total process time CTpAnd batch production time CmaxAre variables.
Optionally, the constraint conditions of the capacity optimization model are as follows:
R*≤Umax(4)；
0＜Rn≤Jn(n＝1,2,…,N) (5)；
CMk,x-CMk,x-1≥tMk,x(k＝1,2,…,M；x＝2,3,…,Xk) (8)；
wherein, the formula (4) represents that the number of the produced workpieces does not exceed the maximum number of the produced workpieces in the production batch; equation (5) represents that the number of first workpieces produced in the production batch is greater than zero and does not exceed the total number of first workpieces required to be produced by the production line; equation (6) indicates that there is no common divisor other than 1 between the number of each workpiece produced in the first production lot; formula (7) indicates that the current process must be completed after the previous process of the first workpiece is completed; the formula (8) shows that the production machine can execute the current working procedure after the previous working procedure is finished; the formula (9) indicates that the processing is performed only once per step.
Optionally, the obtaining of the number of the workpieces corresponding to the workpiece type and the sequence of the process by using the parallel chaotic optimization algorithm includes the following steps:
initializing the number of workpieces to be produced;
calculating the production efficiency corresponding to the number of the workpieces to be produced;
updating the elite library of the number of the workpieces;
updating the chaotic sequence R;
and (5) iteratively solving the optimal number of workpieces.
Optionally, calculating the production efficiency corresponding to the number of the workpieces to be produced includes the following steps:
generating random procedure codes according to the number of the workpieces;
calculating the batch production time of the process codes;
updating a procedure coding elite library;
randomly crossing the procedure codes;
carrying out local variation on the process codes;
merging the child sets coded by the procedures;
iteratively solving the optimal procedure code;
and obtaining the production efficiency according to the processing time corresponding to the optimal procedure code.
Optionally, generating the random procedure code comprises:
numbering the workpieces to obtain a length of
The workpiece number vector JOB of {1,2, …, L };
wherein 1 st to 1 st of the workpiece number vector JOB
Each corresponding to a workpiece J1Number of workpiece number vector JOB
To get it ready
Each corresponding to a workpiece J2The numbering of (1) and so on;
the procedures are compiledThe number "k" refers to the number of all steps included in the workpiece with the number "k", and all the steps are randomly put into the length "k
In the process processing sequence vector op;
the number k indicates the ith process of the kth workpiece placed at the position for the ith time in the process machining sequence vector op.
Optionally, the updating the process code elite library comprises:
obtaining a processing procedure pa,bCorresponding production machine mkThe number d of the processes which are arranged at present and the processing time information of each process;
wherein, for the process sequence vector opiThe jth element op in (b)i,jWorking Process pa,bA (b) th step for the (a) th workpiece;
obtaining pa,b-1Completion time C ofa,b-1And process sequence vector opiAt mkThe corresponding processing time deltat;
traverse production machine mkD, searching whether a process position i meeting the following conditions exists in the currently arranged process:
Ck,i-tk,i≥Ck,i-1+Δt(i＝2,3,…,d) (10)
Ck,i-tk,i≥Ca,b-1+Δt(i＝2,3,…,d) (11)
if the process position i exists, the process p is carried outa,bArranged next to the production machine mkBefore the ith processing procedure, the processing procedure is taken as a new ith processing procedure, and all the subsequent procedures are sequentially extended backwards for 1 bit;
if there is no process position i, the process p is performeda,bIs placed on a production machine mkThe d +1 th position of (a).
Optionally, randomly interleaving the process step codes comprises:
selecting a pair of random procedure codes from all procedure codes, randomly selecting a preset number of procedures, respectively finding out corresponding procedure positions from the pair of random procedure codes, interchanging the procedure positions, and sequentially placing the other unselected procedures according to the original sequence to generate a new procedure code;
and carrying out process re-correspondence on the new process code according to the principle that the appearance order of the workpiece number represents the process order of the workpiece.
To prevent a post-machining process in the same workpiece after the interchange from being placed before a pre-machining process, thereby creating an infeasible process code.
Optionally, the chaotic sequence update R comprises:
at the beginning of optimization, Logistic chaotic sequence formula (r) is useds+1＝a×rs(1-rs),r∈(0,1),a∈(0,4]) And generates a random number r0∈ (0,1) as the initial value of the chaotic sequence, followed by an arbitrary rsFrom rs-1Mapping is carried out through a chaotic sequence formula to obtain a sufficiently long chaotic sequence;
for number of workpieces RiThe jth element of (1)
Setting an updated maximum step length st, and converting a chaos variable r into a chaos variable rsIs mapped to [ -st, st [ -st]Get the chaos variable z by roundingsAnd new elements
Wherein the content of the first and second substances,
for updated number of workpieces RiThe jth element of (1); the maximum step st is a positive integer;
for number of workpieces RiJ +1 th element of (2)
By rs+1Updating is carried out;
if the new number of the workpieces R is obtainediIf the constraint of the formula (6) is not satisfied, R is foundiGreatest common divisor of each element, with RiThe element in (1) is divided by the greatest common divisor, and R is divided byiReplacing the original Ri。
The embodiment of the invention has the following beneficial effects:
1. the embodiment of the invention produces the total production task in batches, optimizes the number of workpieces produced in the batches and the processing sequence of the working procedures corresponding to the number of the workpieces by using an improved parallel chaotic optimization algorithm (MPCOA), and ensures the production efficiency in the production batches by reasonably distributing the number of the workpieces produced in the batches and reasonably arranging the processing sequence of the working procedures, thereby effectively optimizing the overall capacity of the production line.
2. The capacity optimization model of the automatic casting mixed flow production line established by the embodiment of the invention takes the maximum batch production efficiency as an objective function and contains related constraint conditions, and ensures the maximum production efficiency in production batches by reasonably distributing the number of workpieces produced in the batches and reasonably arranging the processing sequence of working procedures, thereby effectively optimizing the overall capacity of the production line.
3. And during searching, a new workpiece quantity is generated by utilizing the chaotic sequence, so that a search space is uniformly distributed in a solution space, a higher convergence speed is obtained, and an algorithm can obtain a global optimal solution by generating a random solution and carrying out cross evolution on a solution with high fitness.
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 method for optimizing capacity of an automated mixed flow casting line according to an embodiment of the present invention;
FIG. 2 is a second flow chart of the capacity optimization method for an automated mixed-flow casting line according to the embodiment of the present invention;
FIG. 3 is a third flow chart of a method for optimizing capacity of an automated mixed flow casting line according to an embodiment of the present invention;
FIG. 4 is a fourth flowchart of a capacity optimization method for an automated mixed-flow casting line according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating a sequence of processing in an embodiment of the present invention when the quantity of workpieces is determined by optimizing an improved parallel chaos optimization algorithm (MPCOA);
FIG. 6 is a diagram illustrating an encoded global interleaving in an embodiment of the present invention;
FIG. 7 is a flow chart illustrating a method for optimizing the number of workpieces in a production lot using the Modified Parallel Chaos Optimization Algorithm (MPCOA) according to an embodiment of the present invention;
FIG. 8 is a schematic diagram illustrating a process of generating a new number of workpieces by a chaotic sequence 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.
Example 1
The embodiment of the invention provides a capacity optimization method for an automatic mixed flow casting production line, which comprises the following steps of:
step S100, acquiring the type of the workpieces to be produced and the total quantity of the workpieces to be produced corresponding to the type of the workpieces to be produced.
In this embodiment, all the types of workpieces to be produced and all the numbers of workpieces to be produced at this time are confirmed through the order received by the workshop. In the specific embodiment, since the process machining sequence corresponding to the workpiece type is fixed, the process machining sequence of each workpiece is stored in the database in advance and called in the production process.
And S200, acquiring the workpiece type, the quantity set process of the single production batch and the maximum production quantity of the workpieces of the single production batch in the single production batch through a parallel chaotic optimization algorithm.
In this embodiment, the total production task is produced in batches, and the number of workpieces produced in the batch and the processing sequence of the corresponding number of workpieces are optimized by using an improved parallel chaotic optimization algorithm (MPCOA). The number of workpieces produced in a batch is reasonably distributed, and the processing sequence of the working procedures is reasonably arranged according to the number of the workpieces produced in the batch, so that the production efficiency in the production batch is ensured, and the whole capacity of the production line is effectively optimized.
In an embodiment, since the workshop may receive an order suddenly during the production process, which may cause the type and quantity of the workpieces to be produced to change, step S100 needs to be performed once before each production batch starts to produce, so as to update the production task of the current day, thereby implementing the automated production.
As an optional implementation manner, before step S100, the method further includes: step S010, constructing a productivity optimization model of the automatic casting mixed flow production line, comprising the following steps:
step S011, setting production parameters in a single production batch.
The production parameters include: the number N of types of workpieces to be produced; production maximum number J of nth type workpieces in single production batchn(N ═ 1,2, …, N); producing the ith workpiece to include QiA step of QiEach process constitutes a process set Pi(ii) a The jth process of the ith workpiece is denoted as process pi,j(ii) a Setting M production machines, the kth production machine being numbered Mk(ii) a Set maximum number of workpiece productions U for a single production batchmax；
With the number of workpieces to be produced in a single production batch R ═ R1，R2，…，RNAnd taking the corresponding procedure processing sequence scheme op as an optimized decision variable, and recording: ci,jIs a process step pi,jCompletion time of ti,j,kIs a process step pi,jIn the production machine MkIn the production machine MkThe number of the processes arranged is XkIn the production machine MkThe completion time of the x-th working procedure is CMk,xIn the production machine MkThe processing time of the x procedure of the upper processing is tMk,xThe total number of workpieces produced in a batch is R ═ ∑ Rn(N ═ 1,2, …, N); if step p is carried outi,jAt the kth station production machine MkAt the x-th position of (e), then yi,j,k,xIs 1, otherwise is 0, then:
Cmax＝max{Ci,j}，(i＝1,2,…R*；j＝1,2,…,Qi) (2)；
wherein, CTpThe total process time is the sum of the processing time of all processes of all workpieces in a production batch on the corresponding production machines; cmaxBatch production time, i.e., the maximum time to complete all the processes within a production batch.
Step S012, establishing an objective function:
wherein E is the production efficiency of the production batch, the number M of the production machines is a constant, and the total process time CTpAnd batch production time CmaxAre variables.
In this embodiment, by constructing the capacity optimization model of the automated mixed-flow casting production line, in the actual production process, the batch production efficiency can be maximized by only adjusting the parameters such as the type of the workpieces to be produced, the number of the workpieces to be produced, and the like in the capacity optimization model, and by reasonably allocating the number of the workpieces to be produced in the batch and reasonably arranging the processing sequence of the processes, the production efficiency in the production batch is ensured to be maximized, so that the overall capacity of the production line is effectively optimized.
As an optional implementation manner, the constraint conditions of the capacity optimization model in step S010 are as follows:
R*≤Umax(4)；
0＜Rn≤Jn，(n＝1,2,…,N) (5)；
CMk,x-CMk,x-1≥tMk,x(k＝1,2,…,M；x＝2,3,…,Xk) (8)；
wherein, the formula (4) represents that the number of the produced workpieces does not exceed the maximum number of the produced workpieces in the production batch; equation (5) represents that the number of first workpieces produced in the production batch is greater than zero and does not exceed the total number of first workpieces required to be produced by the production line; equation (6) indicates that there is no common divisor other than 1 between the number of each workpiece produced in the first production lot; formula (7) indicates that the current process must be completed after the previous process of the first workpiece is completed; the formula (8) shows that the production machine can execute the current working procedure after the previous working procedure is finished; the formula (9) indicates that the processing is performed only once per step.
In this embodiment, the capacity optimization model takes the maximum batch production efficiency as an objective function and includes related constraint conditions, and by reasonably allocating the number of workpieces produced in a batch and reasonably arranging the processing sequence of the processes, the maximum production efficiency in a production batch is ensured, so that the overall capacity of the production line is effectively optimized.
As an alternative implementation, as shown in fig. 3, the step S200 specifically includes:
step S210, initializing the number of workpieces to be produced.
In the present embodiment, pop2 randomly generated workpieces satisfying the constraint number are grouped into a set R ═ { R ═ R1,R2,…,Rpop2I th number of workpieces
Step S220, calculating the production efficiency corresponding to the number of the workpieces to be produced.
In this embodiment, all elements in R are traversed in a manner of improving a parallel chaos optimization algorithm (MPCOA) for a first batch to obtain an optimal process sequence vector and a batch production time corresponding to each number of workpieces in R, and a total process time CT corresponding to each number of workpieces in R is obtainedpAnd (4) calculating the production efficiency corresponding to each element in the R by using the formula (3) to form a set E _ R.
Step S230, updating the workpiece quantity elite library.
In the embodiment, the size of the workpiece number eligibility library is f, and the workpiece number corresponding to the currently obtained optimal front f production efficiencies is put into the workpiece number eligibility library during each iteration;
in step S240, the chaotic sequence updates R.
In the present embodiment, a total of pop2 elements in the current workpiece number set R and workpiece number elite library are randomly selected for n3And performing secondary local variation, generating a pop2 new workpiece quantity meeting the constraint by utilizing the chaotic sequence based on the varied pop2 elements, and forming a workpiece quantity set R as a starting point of a new round of iterative search.
And step S250, iteratively solving the optimal workpiece quantity.
In this embodiment, the steps S220 to S240 are repeatedly executed in sequence until the iteration reaches the maximum solving step number GEN2, and the number of workpieces corresponding to the maximum production efficiency is output, that is, the optimal number of workpieces. And during searching, a new workpiece quantity is generated by utilizing the chaotic sequence, so that a search space is uniformly distributed in a solution space, a higher convergence speed is obtained, and an algorithm can obtain a global optimal solution by generating a random solution and carrying out cross evolution on a solution with high fitness.
As an alternative embodiment, as shown in fig. 4, step S220 further includes:
step S221, generating a random process code according to the number of the workpieces.
In this embodiment, the number of workpieces RiThe process (2) is encoded as a decision variable for the lot, and pop1 process sequence vectors are randomly generated and recorded as a set OP ═ { OP [ ]1，op2，…，oppop1Each element of OP is an ordered vector containing all the processes to be processed in the production lot.
In step S222, the batch production time of the process code is calculated.
In the present embodiment, the processing order vector op is setiIn op, subject to constraintsiThe process arrangement is carried out according to the principle that the process arranged in the middle row is arranged in the front, so as to obtain the opiCorresponding Cmax,iAnd repeating the process to traverse all the elements in the working procedure processing sequence vector set OP to obtain the batch production time corresponding to each element.
And step S223, updating the procedure coding elite library.
In this embodiment, the size of the process coding elite library is e, and the currently obtained optimal process processing sequence vector corresponding to the previous e batches of production time is placed in the process coding elite library during each iteration.
Step S224 is to randomly interleave the process code.
In the present embodiment, e is generated in such a manner that the random code is generated in step S2211A new process machining sequence vector, e process machining sequence vectors and a new e in the process coding elite library1The processing sequence vectors of the procedures form a Parent procedure processing sequence vector set OP _ Parent, and n is carried out among elements in the OP _ Parent1The secondary random crossing obtains a Child process processing order vector set OP _ Child _ Global.
Step S225, locally mutates the process code.
In this embodiment, n is performed on a part of elements in the process code elite library2Sub-random local variation to generate e2Forming a Child process processing sequence vector set OP _ Child _ Local by using the new process processing sequence vectors;
in step S226, child sets of process codes are merged.
In this embodiment, the OP _ Child _ Global and the OP _ Child _ Local are merged into one set, and the previous OP set is replaced by the set, which is used as the starting point of a new round of iterative search.
And step S227, iteratively solving the optimal process code.
In the present embodiment, steps S222 to S226 are repeatedly executed in order until the maximum number of solution steps GEN1 is reached, and the number of workpieces R is outputiAnd the optimal process sequence vector and the corresponding batch production time.
And step S228, obtaining the production efficiency according to the processing time corresponding to the optimal process code.
In the present embodiment, the number of output workpieces R is determined according toiThe maximum production efficiency corresponding to the number of the workpieces to be produced can be obtained through the optimal working procedure processing sequence vector and the corresponding batch production time.
As an alternative embodiment, step S221 includes:
step S2211, numbering the workpieces to obtain the length of
Workpiece number vector JOB of {1,2, …, L }.
Wherein 1 st to 1 st of the workpiece number vector JOB
Each corresponding to a workpiece J1Number of workpiece number vector JOB
To get it ready
Each corresponding to a workpiece J2The numbering of (c), and so on.
Step S2212, numbering the processes, numbering all the processes contained in the workpiece with the number k as the process k, and connecting the processesHas working procedures of randomly putting into the length of
In the process machining order vector op.
The number k indicates the ith process of the kth workpiece placed at the position for the ith time in the process machining sequence vector op.
As an alternative embodiment, step S223 includes:
step S2231, obtaining a processing procedure pa,bCorresponding production machine mkThe number d of the processes which are arranged at present and the processing time information of each process;
wherein, for the process sequence vector opiThe jth element op in (b)i,jWorking Process pa,bIs the b-th process of the a-th workpiece.
Step S2232, obtaining pa,b-1Completion time C ofa,b-1And process sequence vector opiAt mkCorresponding machining time deltat.
Step S2233, traverse the production machine mkD, searching whether a process position i meeting the following conditions exists in the currently arranged process:
Ck,i-tk,i≥Ck,i-1+Δt(i＝2,3,…,d) (10)
Ck,i-tk,i≥Ca,b-1+Δt(i＝2,3,…,d) (11)
in step S2234, if there is a process position i, the process p is performeda,bArranged next to the production machine mkBefore the ith machining step (b), all the subsequent steps are sequentially extended backward by 1 bit as a new ith machining step.
In step S2235, if there is no process position i, the process p is performeda,bIs placed on a production machine mkThe d +1 th position of (a).
In this embodiment, the function value of the procedure code is calculated by actively scheduling and arranging the procedure.
As an alternative embodiment, step S224 randomly interleaves the process code, and includes:
step S2241, selecting a pair of random procedure codes from all procedure codes, randomly selecting a preset number of procedures, respectively finding out corresponding procedure positions from the pair of random procedure codes, interchanging the procedure positions, and sequentially placing the other unselected procedures according to the original sequence to generate a new procedure code;
step S2242, the new process code is subjected to the process remap according to the principle that the appearance order of the workpiece number represents the process order of the workpiece.
In the embodiment, in the mode of randomly crossing the process codes, the new process codes are subjected to process remap according to the principle that the appearance order of the workpiece number represents the process order of the workpiece, so that the problem that the process codes which are exchanged and then processed later in the same workpiece are placed before the process which is processed earlier, and thus the process codes are not feasible is effectively prevented.
As an alternative embodiment, the step S240 of updating the chaotic sequence R includes:
step S241, when the optimization starts, using a Logistic chaotic sequence formula (r)s+1＝a×rs(1-rs),r∈(0,1),a∈(0,4]) And generates a random number r0∈ (0,1) as the initial value of the chaotic sequence, followed by an arbitrary rsFrom rs-1The chaotic sequence is obtained by mapping through a chaotic sequence formula, so that a sufficiently long chaotic sequence is obtained.
Step S242, for the number R of workpiecesiThe jth element of (1)
Setting an updated maximum step length st, and converting a chaos variable r into a chaos variable rsIs mapped to [ -st, st [ -st]Get the chaos variable z by roundingsAnd new elements
Wherein the content of the first and second substances,
for renewed workNumber RiThe jth element of (1); the maximum step st is a positive integer.
Step S243, for the number of workpieces RiJ +1 th element of (2)
By rs+1The process of step S2142 is repeated for updating.
Step S244, if the new workpiece quantity R is obtainediIf the constraint of the formula (6) is not satisfied, R is foundiGreatest common divisor of each element, with RiThe element in (1) is divided by the greatest common divisor, and R is divided byiReplacing the original Ri。
Example 2
Referring to fig. 5, a flow chart of a sequence of processing when the number of workpieces is determined by optimizing the improved parallel chaotic optimization algorithm (MPCOA), specifically includes the following steps:
1) generating a random procedure code: for number of workpieces RiThe process (2) is encoded as a decision variable for the lot, and pop1 process sequence vectors are randomly generated and recorded as a set OP ═ { OP [ ]1，op2，…，oppop1Each element of OP is an ordered vector containing all the processes to be processed in the production lot.
2) Calculating the batch production time of the process code: for the processing order vector opiIn op, subject to constraintsiThe process arrangement is carried out according to the principle that the process arranged in the middle row is arranged in the front, so as to obtain the opiCorresponding Cmax,iAnd repeating the process to traverse all the elements in the working procedure processing sequence vector set OP to obtain the batch production time corresponding to each element.
3) Updating a procedure coding elite library: and the size of the procedure coding elite library is e, and the currently obtained optimal procedure processing sequence vector corresponding to the previous e batches of production time is put into the procedure coding elite library during each iteration.
4) Procedure coding global crossing: generating e in a manner to generate random codes in step 1)1A new process processing sequence vector and a process coding elite libraryE process sequence vectors and new e1The processing sequence vectors of the procedures form a Parent procedure processing sequence vector set OP _ Parent, and n is carried out among elements in the OP _ Parent1The secondary random crossing obtains a Child process processing order vector set OP _ Child _ Global.
5) Encoding local variation: n is carried out on partial elements in procedure coding elite library2Sub-random local variation to generate e2And forming a Child process processing sequence vector set OP _ Child _ Local by the new process processing sequence vectors.
6) Merging the descendant OP: the OP _ Child _ Global and the OP _ Child _ Local are merged into a set, and the previous OP set is replaced by the set to be used as the starting point of a new round of iterative search.
7) Iterative solution of optimal procedure encoding: repeatedly executing the steps 2) to 6) in sequence until the iteration reaches the maximum solving step number GEN1, and outputting the workpiece number RiAnd the optimal process sequence vector and the corresponding batch production time.
The specific mode for generating the random procedure code is as follows:
1) numbering the workpieces to obtain a length of
Workpiece number vector JOB {1,2, …, L }, where the 1 st to the 1 st of JOB
Each corresponding to a workpiece J1Number of JOB
To get it ready
Each corresponding to a workpiece J2The numbering of (1) and so on;
2) numbering the processes, wherein all the processes contained in the workpiece with the number of k are numbered as the process k, and all the processes are randomly put into the workpiece with the length of k
In the process machining order vector op, the number k indicates the ith process of the kth workpiece placed at the position of the ith process in the process machining order vector op.
When calculating the function value of the procedure code, the procedure is arranged by active scheduling, and the specific mode is as follows:
1) for opiThe jth element op in (b)i,jIt means the b-th step of the a-th workpiece, i.e. pa,bWhile finding the processing pa,bMachine m ofkThe number d of the processes which have been arranged at present, and the processing time information of each process.
2) Find pa,b-1Is the completion time of Ca,b-1Find the opiAt mkCorresponding machining time Δ t, traverse mkD, searching whether a process position i meeting the following conditions exists in the currently arranged process:
Ck,i-tk,i≥Ck,i-1+Δt(i＝2,3,…,d) (10)
Ck,i-tk,i≥Ca,b-1+Δt(i＝2,3,…,d) (11)
if there is such a position i (there may be a plurality of positions, and in this case, the position is arranged first), the step p is performeda,bArranged next to mkBefore the ith processing procedure, the processing procedure is taken as a new ith processing procedure, and all the subsequent procedures are sequentially extended backwards for 1 bit;
if not, the step p is carried outa,bIs placed on mkThe d +1 th position of (a).
Referring to fig. 6, a schematic diagram of encoding global interleaving is shown, and a specific manner of encoding random interleaving by the process is illustrated as follows:
1) randomly selecting a pair of codes in all the codes, namely the parent code in FIG. 6, wherein the codes are the process order of the workpiece for the purpose of auxiliary explanation; randomly selecting 5 working procedures as the exchange working procedures, wherein the working procedures are respectively as follows: 1 st process of 4 workpieces, 3 rd process of 3 workpieces, 2 nd process of 4 workpieces, 2 nd process of 1 workpiece, 3 rd process of 4 workpieces; the positions corresponding to these processes are found in the pair of codes respectively, as shown in fig. 6, the corresponding positions of the selected processes are exchanged, and the remaining unselected processes are placed in the original order.
2) In the child codes shown in fig. 6, the 3 rd step of one coded workpiece 3 is prior to the 2 nd step thereof due to the crossing, and is a non-feasible code, so that the new codes are subjected to the step re-correspondence according to the principle that the appearance order of the workpiece number represents the step order of the workpiece.
Referring to fig. 7, a flow chart for optimizing the number of workpieces in a production lot by using an improved parallel chaotic optimization algorithm (MPCOA) includes the following specific steps:
1) initialization: randomly generating pop2 workpiece quantities satisfying the constraint to form a set R ═ R1,R2,…,Rpop2I th number of workpieces
2) Calculating the production efficiency corresponding to the number of the workpieces: traversing all elements in the R in a mode of improving a parallel chaos optimization algorithm (MPCOA) for a first batch to obtain an optimal working procedure processing sequence vector and batch production time corresponding to each workpiece quantity in the R, and obtaining total working procedure time CT corresponding to each workpiece quantity in the RpAnd (4) calculating the production efficiency corresponding to each element in the R by using the formula (3) to form a set E _ R.
3) Updating the elite library of the number of workpieces: the size of the workpiece quantity elite library is f, and the workpiece quantity corresponding to the currently obtained optimal front f production efficiencies is put into the workpiece quantity elite library during each iteration.
4) Updating R by the chaotic sequence: randomly selecting a total pop2 elements in a current workpiece number set R and a workpiece number elite library for n3And performing secondary local variation, generating a pop2 new workpiece quantity meeting the constraint by utilizing the chaotic sequence based on the varied pop2 elements, and forming a workpiece quantity set R as a starting point of a new round of iterative search.
5) Iteratively solving the optimal number of workpieces: and (5) repeatedly executing the steps 2) to 4) in sequence until iteration is carried out to the maximum solving step number GEN2, and outputting the number of workpieces corresponding to the maximum production efficiency, namely the optimal number of workpieces.
Referring to fig. 8, a schematic diagram of a process for generating a new workpiece quantity by using a chaotic sequence is shown, and the following illustrates a manner for generating a new workpiece quantity by using a chaotic sequence:
1) at the beginning of optimization, selecting Logistic sequence parameter a to 4, and generating a random number r0As an initial value, 0.84, a sufficiently long chaotic sequence is obtained by the chaotic sequence formula, and fig. 4 shows the first 15 of the sequence.
2) Setting the updated maximum step length st to be 3, and setting the chaos variable rsIs mapped to the step space [ -3,3 ] by the value range (0,1)]And rounding the step length to obtain zsBy passing
Then the updated number of workpieces RiThe jth element of (1).
3) For number of workpieces RiJ +1 th element of (2)
By rs+1And repeating the process of the step 2) for updating.
4) If R is obtainediDoes not satisfy the constraint of equation (6), R updated as shown in FIG. 43Then find R3The greatest common divisor of each element is 2, with R3The element in (b) is divided by 2 to obtain the final R3。
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 (9)
1. The capacity optimization method for the automatic casting mixed flow production line is characterized by comprising the following steps of:
acquiring the type of workpieces to be produced and the total number of the workpieces to be produced corresponding to the type of the workpieces to be produced;
and acquiring the workpiece type in a single production batch, the quantity set process of the single production batch and the maximum production quantity of the workpieces of the single production batch by a parallel chaotic optimization algorithm.
2. The productivity optimization method for the automated mixed casting flow production line according to claim 1, wherein before the obtaining of the workpiece type, the quantity set process of the single production batch and the maximum quantity of the workpieces produced by the single production batch by the parallel chaotic optimization algorithm, the method further comprises:
the capacity optimization model of the automatic casting mixed flow production line is constructed, and the capacity optimization model comprises the following steps:
setting production parameters in the single production batch;
the production parameters include: the number N of types of workpieces to be produced; production maximum number J of n-th type workpieces in the single production batchn(N ═ 1,2, …, N); producing the ith workpiece to include QiA step ofiEach process constitutes a process set Pi(ii) a The jth process of the ith workpiece is represented as process pi,j(ii) a Setting M production machines, the kth production machine being numbered Mk(ii) a Setting the maximum number of workpieces produced U of the single production batchmax；
The number of the workpieces to be produced in the single production batch is R ═ R1，R2，…，RNAnd taking the corresponding procedure processing sequence scheme op as an optimized decision variable, and recording: said process pi,jHas a completion time of Ci,jThe process pi,jIn the production machine MkHas a processing time of ti,j,kIn said production machine MkThe number of the processes arranged is XkIn said production machine MkThe completion time of the x-th working procedure is CMk,xIn said production machine MkProcessing time of the x-th working procedureIs tMk,xThe total number of workpieces produced in a batch is R ═ ∑ Rn(N ═ 1,2, …, N); if said process step p isi,jAt the k-th station production machine MkAt the x-th position of (e), then yi,j,k,xIs 1, otherwise is 0, then:
Cmax＝max{Ci,j}，(i＝1,2,…R*；j＝1,2,…,Qi) (2)；
wherein, CTpThe total process time is the sum of the processing time of all processes of all workpieces in the production batch on the corresponding production machines; cmaxIs batch production time, i.e. the maximum time to complete all processes within the production batch;
establishing an objective function:
wherein E is the production efficiency of the production batch, the number M of the production machines is a constant, and the total process time CTpAnd the batch production time CmaxAre variables.
3. The productivity optimization method for the automatic mixed flow casting production line according to claim 2, wherein the constraints of the productivity optimization model are as follows:
R*≤Umax(4)；
0＜Rn≤Jn(n＝1,2,…,N) (5)；
CMk,x-CMk,x-1≥tMk,x(k＝1,2,…,M；x＝2,3,…,Xk) (8)；
wherein equation (4) represents that the number of produced workpieces does not exceed the maximum number of produced workpieces within the production lot; equation (5) represents that the number of first workpieces produced in the production lot is greater than zero and does not exceed the total number of first workpieces required to be produced by the production line; equation (6) indicates that there is no common divisor other than 1 between the number of each workpiece produced in the first production lot; formula (7) indicates that the current process must be completed after the previous process of the first workpiece is completed; the formula (8) indicates that the production machine can execute the current working procedure after the previous working procedure is finished; the formula (9) indicates that the processing is performed only once per step.
4. The productivity optimization method of the automatic mixed flow casting production line according to claim 3, wherein the step of obtaining the number of the workpieces and the processing sequence corresponding to the workpiece type through a parallel chaotic optimization algorithm comprises the following steps:
initializing the number of workpieces to be produced;
calculating the production efficiency corresponding to the number of the workpieces to be produced;
updating the elite library of the number of the workpieces;
updating the chaotic sequence R;
and (5) iteratively solving the optimal number of workpieces.
5. The productivity optimization method for the automatic mixed flow casting production line according to claim 4, wherein calculating the production efficiency corresponding to the number of the workpieces comprises the following steps:
generating random procedure codes according to the number of the workpieces;
calculating the batch production time of the process codes;
updating a procedure coding elite library;
randomly crossing the process codes;
locally mutating the process code;
merging the child sets of the procedure codes;
iteratively solving the optimal procedure code;
and obtaining the production efficiency according to the processing time corresponding to the optimal procedure code.
6. The productivity optimization method for an automated mixed flow casting production line according to claim 5, wherein the generating of the random process code comprises:
numbering the workpieces to obtain a length of
The workpiece number vector JOB of {1,2, …, L };
wherein the 1 st to the 1 st of the workpiece number vector JOB
Each corresponding to a workpiece J1The number of the workpiece number vector JOB
To get it ready
Each corresponding to a workpiece J2The numbering of (1) and so on;
numbering the processes, numbering all the processes contained in the workpiece with the number of k as a process k, and randomly putting all the processes into a position with the length of k
In the process processing sequence vector op;
the number k indicates the ith process of the kth workpiece placed at the position for the ith time in the process machining sequence vector op.
7. The productivity optimization method for the mixed flow automatic casting production line according to claim 5, wherein the step of updating the coded elite library comprises the following steps:
obtaining a processing procedure pa,bCorresponding production machine mkThe number d of the processes which are arranged at present and the processing time information of each process;
wherein, for the process sequence vector opiThe jth element op in (b)i,jThe above-mentioned working Process pa,bA (b) th step for the (a) th workpiece;
obtaining pa,b-1Completion time C ofa,b-1And process sequence vector opiAt mkThe corresponding processing time deltat;
traverse the production machine mkD, searching whether a process position i meeting the following conditions exists in the currently arranged process:
Ck,i-tk,i≥Ck,i-1+Δt(i＝2,3,…,d) (10)
Ck,i-tk,i≥Ca,b-1+Δt(i＝2,3,…,d) (11)
if the process position i exists, the process p is carried outa,bArranged next to said production machine mkBefore the ith processing procedure, the processing procedure is taken as a new ith processing procedure, and all the subsequent procedures are sequentially extended backwards for 1 bit;
if the process position i does not exist, the process p is carried outa,bIs placed on the production machine mkThe d +1 th position of (a).
8. The productivity optimization method for an automated mixed flow casting line according to claim 5, wherein the randomly crossing the process code comprises:
selecting a pair of random procedure codes from all procedure codes, randomly selecting a preset number of procedures, respectively finding out corresponding procedure positions from the pair of random procedure codes, interchanging the procedure positions, and sequentially placing the other unselected procedures according to the original sequence to generate a new procedure code;
and carrying out process re-correspondence on the new process code according to the principle that the appearance order of the workpiece number represents the process order of the workpiece.
To prevent a post-machining process in the same workpiece after the interchange from being placed before a pre-machining process, thereby creating an infeasible process code.
9. The productivity optimization method for the automatic mixed flow casting production line according to claim 5, wherein the chaotic sequence updating R comprises:
at the beginning of optimization, Logistic chaotic sequence formula (r) is useds+1＝a×rs(1-rs),r∈(0,1),a∈(0,4]) And generates a random number r0∈ (0,1) as the initial value of the chaotic sequence, followed by an arbitrary rsFrom rs-1Mapping is carried out through a chaotic sequence formula to obtain a sufficiently long chaotic sequence;
for number of workpieces RiThe jth element of (1)
Setting an updated maximum step length st, and converting a chaos variable r into a chaos variable rsIs mapped to [ -st, st [ -st]Get the chaos variable z by roundingsAnd new elements
Wherein the content of the first and second substances,
for updated number of workpieces RiThe jth element of (1); the maximum step st is a positive integer;
for number of workpieces RiJ +1 th element of (2)
By rs+1Updating is carried out;
if the new number of the workpieces R is obtainediThe constraint of the formula (6) is not satisfied,then find RiGreatest common divisor of each element, with RiThe element in (1) is divided by the greatest common divisor, and R is divided byiReplacing the original Ri。
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101162884A (en) * | 2007-09-14 | 2008-04-16 | 湖南大学 | Inverse model control method of generator excitation system |
| US20090210668A1 (en) * | 2008-02-19 | 2009-08-20 | Luick David A | System and Method for Optimization Within a Group Priority Issue Schema for a Cascaded Pipeline |
| CN103078729A (en) * | 2012-01-13 | 2013-05-01 | 河南科技大学 | Dual-precision chaotic signal generator based on FPGA (field programmable gate array) |
| CN104036380A (en) * | 2014-06-26 | 2014-09-10 | 广东工业大学 | Chaos immune algorithm based multi-vehicle assembly line logistics transportation scheduling method |
| CN110288126A (en) * | 2019-05-30 | 2019-09-27 | 湖南大学 | A kind of robot foundry production line production capacity optimization method |
| CN110728349A (en) * | 2019-09-19 | 2020-01-24 | 武汉大学 | Optimization method of mixed bat algorithm and optimization method of multilayer perceptron |
-
2020
- 2020-03-23 CN CN202010207789.3A patent/CN111367247B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101162884A (en) * | 2007-09-14 | 2008-04-16 | 湖南大学 | Inverse model control method of generator excitation system |
| US20090210668A1 (en) * | 2008-02-19 | 2009-08-20 | Luick David A | System and Method for Optimization Within a Group Priority Issue Schema for a Cascaded Pipeline |
| CN103078729A (en) * | 2012-01-13 | 2013-05-01 | 河南科技大学 | Dual-precision chaotic signal generator based on FPGA (field programmable gate array) |
| CN104036380A (en) * | 2014-06-26 | 2014-09-10 | 广东工业大学 | Chaos immune algorithm based multi-vehicle assembly line logistics transportation scheduling method |
| CN110288126A (en) * | 2019-05-30 | 2019-09-27 | 湖南大学 | A kind of robot foundry production line production capacity optimization method |
| CN110728349A (en) * | 2019-09-19 | 2020-01-24 | 武汉大学 | Optimization method of mixed bat algorithm and optimization method of multilayer perceptron |
Non-Patent Citations (1)
| Title |
|---|
| 张庭: "《混沌优化算法及其在交通图像分割中的应用》", 《工程科技Ⅱ辑》 * |
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