CN109116816A - The Optimization Scheduling of printing process under a kind of Flexible Manufacture environment - Google Patents
The Optimization Scheduling of printing process under a kind of Flexible Manufacture environment Download PDFInfo
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- CN109116816A CN109116816A CN201810825846.7A CN201810825846A CN109116816A CN 109116816 A CN109116816 A CN 109116816A CN 201810825846 A CN201810825846 A CN 201810825846A CN 109116816 A CN109116816 A CN 109116816A
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- 230000008569 process Effects 0.000 title claims abstract description 80
- 238000007639 printing Methods 0.000 title claims abstract description 47
- 238000005457 optimization Methods 0.000 title claims abstract description 21
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 17
- 230000002068 genetic effect Effects 0.000 claims abstract description 10
- 210000000349 chromosome Anatomy 0.000 claims description 27
- 230000003044 adaptive effect Effects 0.000 claims description 21
- 238000012545 processing Methods 0.000 claims description 18
- 239000004744 fabric Substances 0.000 claims description 14
- 238000003780 insertion Methods 0.000 claims description 7
- 230000037431 insertion Effects 0.000 claims description 7
- 238000003754 machining Methods 0.000 claims description 7
- 238000001816 cooling Methods 0.000 claims description 5
- 239000007921 spray Substances 0.000 claims description 4
- 230000035772 mutation Effects 0.000 claims description 3
- 238000011112 process operation Methods 0.000 claims description 3
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- 230000002759 chromosomal effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- 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] or computer integrated manufacturing [CIM]
- G05B19/41865—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] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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- 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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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The present invention relates to a kind of Optimization Schedulings of printing process under Flexible Manufacture environment, belong to workshop intelligent optimization dispatching technique field.The present invention is by indicating uncertain process time and completion date with fuzzy number, then the scheduling model and optimization aim of printing process under Flexible Environment are determined, and optimization aim is optimized using the Optimized Operation scheme based on mixing meta-heuristic genetic algorithm.The present invention can obtain the high-quality solution of printing process scheduling problem under Flexible Environment within a short period of time, shorten printing time, improve the economic benefit of enterprise.
Description
Technical field
The present invention relates to a kind of Optimization Schedulings of printing process under Flexible Manufacture environment, and it is intelligently excellent to belong to workshop
Change dispatching technique field.
Background technique
Printing technology, which refers to, quickly, largely, is economically replicated the graph text information on original copy works using lesser cost
On the stock of various materials, and it can save, use for a long time, developing has the propagation of culture, science and technology
Vital effect.As market competition becomes increasingly fierce, printing industry threshold is lower and lower, printing how is effectively improved
The processing efficiency of each link in the process is the most important thing for improving publishing house's economic benefit and industrial competition.
System, print are shone in printing for books and periodicals class publication, the design, production, printing plate that process flow generally comprises original works
Five links such as brush, post-press finishing.Wherein printing link occupies the plenty of time in the printing process of publication, therefore prints
It is most important to the batch machining of printed article to shorten the production cycle for the rational management of journey.With gradually mentioning for people's living standard
Height also increasingly increases the individual demand of publication, this requires publishing business gradually from past small lot simply print to
The transformation of high-volume personal printing, printing skill of the contradiction of this yield and quality in the case where having expedited the emergence of Flexible Environment at the end of the 20th century
Art.By taking the Sherwood publishing house of printing firm of Nottingham, GBR as an example, 18 machines are shared in workshop, are divided into 7 work
The heart: printing is cut, and is folded, and plug-in card is embossed and draws a design, and every kind of publication requires to be printed according to specific process route
Processing, work centre is all Solid Warehouse in Flexible Manufacturing Workshop, and using high-speed full-automatic mult-functional printing press, every multi-functional printing machine can
The processing of kinds of processes is carried out, but due to being mult-functional printing press, needs to switch spray head, plate change adjustment, and processing environment is complicated,
So the actual processing time of each process has uncertainty, can only generally determine it is in a fuzzy range.Therefore
It is the characteristics of printing process under Flexible Environment, every one kind printable fabric selects a workable high speed according to customer demand
Full-automatic multi-functional printing machine carries out current process processing, and a printing machine at most can only process a printing material in synchronization
Then material selects available machines used to carry out the processing of the next step, until processing all process steps.
Printing time is longer inefficient under current Flexible Environment, and printing demand is mainly asking of facing of Publication Enterprises greatly
Topic.Obviously this is a kind of typical flexible job workshop (flexible Job-shop, FJS) scheduling problem, is demonstrate,proved already
The bright problem belongs to np hard problem, and the method for traditional mathematics planning can only solve small-scale problem, and Heuristic construction method without
Method guarantees majorization of solutions quality.
The present invention is for considering that printing process establishes a kind of scheduling model under Flexible Manufacture environment, if a kind of based on mixing
The Optimization Scheduling of meta-heuristic genetic algorithm can obtain printing process scheduling problem under Flexible Environment within a short period of time
High-quality solution shortens printing time, improves the economic benefit of enterprise.
Summary of the invention
The purpose of the present invention is the scheduling problems for printing process under Flexible Manufacture environment, are considering to print process time
In uncertain situation, a kind of Optimization Scheduling based on mixing meta-heuristic genetic algorithm is proposed, by acquiring flexible ring
The mode of the high-quality solution of printing process scheduling problem under border shortens printing time, improves the economic benefit of enterprise.
The technical scheme is that under a kind of Flexible Manufacture environment printing process Optimization Scheduling, by with mould
Paste number indicates uncertain process time and completion date, then determines under Flexible Environment the scheduling model of printing process and excellent
Change target, and optimization aim is optimized using the Optimized Operation scheme based on mixing meta-heuristic genetic algorithm；Wherein adjust
Degree model is established according to every batch of printable fabric machining the time on each machine, in actual production, by replacement paper,
Switching many uncertain factors such as spray head influences, and process time cannot accurately be predicted, and deadline and delivery date can only be estimated
Some section, therefore process time and deadline are indicated with Triangular Fuzzy Number herein；Each operation Oi,jIndicate printable fabric i
Jth procedure, it is in machine MkOn process time be expressed as with Triangular Fuzzy NumberWherein
Indicate the minimum process time,Indicate most probable process time,Indicate maximum process time；The deadline of each operation
It is expressed asWhereinIndicate the minimum completion time of the jth procedure of workpiece i,Indicate workpiece
The most probable deadline of the jth procedure of i,Indicate the longest finishing time of the jth procedure of workpiece i, degree of membership letter
Number may be expressed as:
Following constraint condition need to be met simultaneously: the same workpiece of any moment can only at most be added on a machine
Work；Same machine of any moment can only at most handle a procedure；Any operation cannot be interrupted during processing；It is same
The process of workpiece, which has to process in a upper process, just can be carried out the processing of next process；
Optimization aim is to minimize earliest completion date Cmax:
Cmax=maxCi
Wherein CiFor the machining the time of printable fabric i；
Specific step is as follows for the Optimized Operation scheme based on mixing meta-heuristic genetic algorithm:
Step1, initialization of population: generating the low dimensional individual of the high-dimensional individual of a popsize and same number, wherein
Popsize represents population scale, and the chromosome length of high-dimensional individual is 6, each chromosome can be appointing between 1 to 6
Meaning number, 6 kinds of rudimentary heuristic operations are as follows:
Rudimentary heuristic operation one: random crossover operation randomly selects two from operation coding and swaps；
Rudimentary heuristic operation two: preceding paragraph insertion operation selects a position from operation coding at random, will randomly choose
A process be inserted into before this position；
Rudimentary heuristic operation three: consequent insertion operation selects a position from operation coding at random, in this position
It is inserted into a randomly selected process later；
Rudimentary heuristic operation four: adjacent crossover operation randomly chooses a position, by its previous or the latter process
It is exchanged with him；
Rudimentary heuristic operation five: backout selects 3 to 4 continuous operations, by them in the sequence of operation at random
It reverses；
Rudimentary heuristic operation six: intercept operation randomly chooses 1 to 5 continuous operations, they is inserted into operation sequence
Column head end；
Low dimensional individual represents the printing process of each printable fabric, and with the corresponding each group of operation of high-dimensional chromosome
The solution of low dimensional is updated, the degree for solving quality represents the adaptive value of the solution, when calculating adaptive value, to keep scheduling tight as far as possible
It causes, decoding low dimensional individual is decoded using greedy mobilization, successively right using greedy strategy according to the operation in process operations string
The each machine for being capable of processing the operation carries out mobilization decoding, then that shortest machine of completion date is selected to be added
Work needs to use the summation of Triangular Fuzzy Number, subtracts each other when calculating adaptive value, takes big operation, wherein summation, phase reducing are used
In calculating the fuzzy deadline, for two Triangular Fuzzy Number X=(x1,x2,x3) and Y=(y1,y2,y3), sum operation definition
Are as follows:
X+Y=(x1+y1,x2+y2,x3+y3)
Subtract each other Operation Definition are as follows:
X-Y=(x1-y1,x2-y2,x3-y3)
Sequence compares operation for the relatively fuzzyyer deadline, for two Triangular Fuzzy Number X=(x1,x2,x3) and Y=
(y1,y2,y3), following foundation is used when sequence:
According to 1: calculatingBy Z1Primary foundation as sequence；
According to 2: if two TFN are equal, defining Z2=x2Secondary foundation as sequence；
According to 3: if two TFN the first two foundations are all equal, using Z3=x3-x1As sort by；
It is big according to that can be ranked up and take to Triangular Fuzzy Number according to three above；
Initial solution is wanted compliance problem feature and is required, meanwhile, the quantity of solution will reach population scale, enable the number of iterations gen
=1；
Initial temperature T in Step2, set algorithm0: local optimum ability is jumped out for further increase algorithm, whole
Simulated annealing is embedded among a algorithm to optimize the search capability of rudimentary heuristic operation, among algorithm, setting is cooled down
Control table, when each iteration, cool down according to cooling ratio α, and temperature T influences rudimentary heuristic operation by following formula:
Wherein Δ f indicates the difference of each new explanation and old solution adaptive value, since the targeted problem target value of the present invention is
Fuzzy number carrys out de-fuzzy used here as the mean value of two fuzzy numbers, obtains a real number, each rudimentary to open when each iteration
If the new explanation that hairdo operates is better than old solution, old solution is replaced, otherwise the random real number r generated between one 0 to 1, if r
Then receive this poor solution greater than probability P；
Step3, update high-dimensional population: using the mode of roulette first, according to the adaptive value of high-dimensional chromosome into
Row screening, twice of individual for selecting number of individuals in population are put into candidate pool and are ready for cross and variation；It later will be in candidate pool
Individual take out two-by-two, carry out two-point crossover operation, that is, randomly select the two o'clock on one of chromosome, position is more forward
Point before chromosome and position point more rearward after chromosome retain, then select chromosome completion from another individual
The part of upper individual missing forms feasible schedule solution, and is made a variation according to the adaptive mutation rate of setting, random to select
It selects a chromosome and is inserted into a random position, this helps to jump out locally optimal solution；
Step4, work piece operations sequence is updated according to high-dimensional population: by forming newly high-dimensional after cross and variation
Population, low heuristic operation is according to corresponding to individual each in high-dimensional population come more new process sequence；
Step5, higher-dimension population and problem population protect it is excellent: new population and old population are put together according to adaptive value
Old population of the good individual as next iteration is selected in rearrangement；
Step6, termination condition: the number of iterations is arranged to N, judges whether to reach the condition for terminating iteration, is to terminate
Otherwise iteration reduces temperature, jump to Step3, iterates until meeting termination condition.
The beneficial effects of the present invention are: the present invention, which considers printing process under Flexible Manufacture environment, establishes a kind of scheduling model, if
A kind of Optimization Scheduling based on mixing meta-heuristic genetic algorithm is counted, can obtain and be printed under Flexible Environment within a short period of time
The high-quality solution of process scheduling problem shortens printing time, improves the economic benefit of enterprise.
Detailed description of the invention
Fig. 1 is whole design flow chart of the invention；
Fig. 2 is printing process schematic diagram under the Flexible Manufacture environment in the present invention；
Fig. 3 is algorithm flow chart in the present invention；
Fig. 4 is the expression schematic diagram of Fig. 2 solution in the present invention；
Fig. 5 is high-dimensional chromosomal variation schematic diagram of the invention；
Fig. 6 is high-dimensional chiasma schematic diagram of the invention.
Specific embodiment
Embodiment 1: as shown in figures 1 to 6, the Optimization Scheduling of printing process, passes through scene under a kind of Flexible Manufacture environment
Measurement or priori knowledge indicate uncertain process time and completion date with fuzzy number, and table 1 is process time example, false
If three mult-functional printing press, three kinds of printable fabrics, the printing of first two is machined with two procedures, and latter has three, in table
" * " indicates that the process can not process on this machine；
Table 1 process time example
Then the scheduling model and optimization aim of printing process under Flexible Environment are determined, and using based on mixing meta-heuristic
The Optimized Operation scheme of genetic algorithm optimizes optimization aim；Wherein scheduling model is according to every batch of printable fabric in each machine
Machining the time on device is established, and in actual production, is influenced by many uncertain factors such as replacement paper, switching spray heads,
Process time cannot accurately be predicted, and deadline and delivery date can only be estimated in some section, thus herein with Triangular Fuzzy Number come
Indicate process time and deadline；Each operation Oi,jIndicate the jth procedure of printable fabric i, it is in machine MkOn processing
Time is expressed as with Triangular Fuzzy NumberWhereinIndicate the minimum process time,Indicate most probable
Process time,Indicate maximum process time；The deadline of each operation is expressed asWhereinIndicate the minimum completion time of the jth procedure of workpiece i,When indicating that the most probable of the jth procedure of workpiece i is completed
Between,Indicate the longest finishing time of the jth procedure of workpiece i, subordinating degree function may be expressed as:
Following constraint condition need to be met simultaneously: the same workpiece of any moment can only at most be added on a machine
Work；Same machine of any moment can only at most handle a procedure；Any operation cannot be interrupted during processing；It is same
The process of workpiece, which has to process in a upper process, just can be carried out the processing of next process；
Optimization aim is to minimize earliest completion date Cmax:
Cmax=maxCi
Wherein CiFor the machining the time of printable fabric i；
Specific step is as follows for the Optimized Operation scheme based on mixing meta-heuristic genetic algorithm:
Step1, initialization of population: generating the low dimensional individual of the high-dimensional individual of a popsize and same number, wherein
Popsize represents population scale, and the chromosome length of high-dimensional individual is 6, each chromosome can be appointing between 1 to 6
Meaning number, 6 kinds of rudimentary heuristic operations are as follows:
Rudimentary heuristic operation one: random crossover operation randomly selects two from operation coding and swaps；
Rudimentary heuristic operation two: preceding paragraph insertion operation selects a position from operation coding at random, will randomly choose
A process be inserted into before this position；
Rudimentary heuristic operation three: consequent insertion operation selects a position from operation coding at random, in this position
It is inserted into a randomly selected process later；
Rudimentary heuristic operation four: adjacent crossover operation randomly chooses a position, by its previous or the latter process
It is exchanged with him；
Rudimentary heuristic operation five: backout selects 3 to 4 continuous operations, by them in the sequence of operation at random
It reverses；
Rudimentary heuristic operation six: intercept operation randomly chooses 1 to 5 continuous operations, they is inserted into operation sequence
Column head end；
Low dimensional individual represents the printing process of each printable fabric, and with the corresponding each group of operation of high-dimensional chromosome
The solution of low dimensional is updated, the degree for solving quality represents the adaptive value of the solution, when calculating adaptive value, to keep scheduling tight as far as possible
It causes, decoding low dimensional individual is decoded using greedy mobilization, successively right using greedy strategy according to the operation in process operations string
The each machine for being capable of processing the operation carries out mobilization decoding, then that shortest machine of completion date is selected to be added
Work needs to use the summation of Triangular Fuzzy Number, subtracts each other when calculating adaptive value, takes big operation, wherein summation, phase reducing are used
In calculating the fuzzy deadline, for two Triangular Fuzzy Number X=(x1,x2,x3) and Y=(y1,y2,y3), sum operation definition
Are as follows:
X+Y=(x1+y1,x2+y2,x3+y3)
Subtract each other Operation Definition are as follows:
X-Y=(x1-y1,x2-y2,x3-y3)
Sequence compares operation for the relatively fuzzyyer deadline, for two Triangular Fuzzy Number X=(x1,x2,x3) and Y=
(y1,y2,y3), following foundation is used when sequence:
According to 1: calculatingBy Z1Primary foundation as sequence；
According to 2: if two TFN are equal, defining Z2=x2Secondary foundation as sequence；
According to 3: if two TFN the first two foundations are all equal, using Z3=x3-x1As sort by；
It is big according to that can be ranked up and take to Triangular Fuzzy Number according to three above；
Initial solution is wanted compliance problem feature and is required, meanwhile, the quantity of solution will reach population scale, enable the number of iterations gen
=1；
Initial temperature T in Step2, set algorithm0: local optimum ability is jumped out for further increase algorithm, whole
Simulated annealing is embedded among a algorithm to optimize the search capability of rudimentary heuristic operation, among algorithm, setting is cooled down
Control table, when each iteration, cool down according to cooling ratio α, and temperature T influences rudimentary heuristic operation by following formula:
Wherein Δ f indicates the difference of each new explanation and old solution adaptive value, since the targeted problem target value of the present invention is
Fuzzy number carrys out de-fuzzy used here as the mean value of two fuzzy numbers, obtains a real number, each rudimentary to open when each iteration
If the new explanation that hairdo operates is better than old solution, old solution is replaced, otherwise the random real number r generated between one 0 to 1, if r
Then receive this poor solution greater than probability P；
Step3, update high-dimensional population: using the mode of roulette first, according to the adaptive value of high-dimensional chromosome into
Row screening, twice of individual for selecting number of individuals in population are put into candidate pool and are ready for cross and variation；It later will be in candidate pool
Individual take out two-by-two, carry out two-point crossover operation, that is, randomly select the two o'clock on one of chromosome, position is more forward
Point before chromosome and position point more rearward after chromosome retain, then select chromosome completion from another individual
The part of upper individual missing forms feasible schedule solution, and is made a variation according to the adaptive mutation rate of setting, random to select
It selects a chromosome and is inserted into a random position, this helps to jump out locally optimal solution；
Step4, work piece operations sequence is updated according to high-dimensional population: by forming newly high-dimensional after cross and variation
Population, low heuristic operation is according to corresponding to individual each in high-dimensional population come more new process sequence；
Step5, higher-dimension population and problem population protect it is excellent: new population and old population are put together according to adaptive value
Old population of the good individual as next iteration is selected in rearrangement；
Step6, termination condition: the number of iterations is arranged to N, judges whether to reach the condition for terminating iteration, is to terminate
Otherwise iteration reduces temperature, jump to Step3, iterates until meeting termination condition.
Popsize is set as 150, initial temperature T0200 are set as, cooling ratio α is set as 0.8, and the number of iterations N is
100。
Obtained target function value in the case of the different problem scales of table 2
Above in conjunction with attached drawing, the embodiment of the present invention is explained in detail, but the present invention is not limited to above-mentioned
Embodiment within the knowledge of a person skilled in the art can also be before not departing from present inventive concept
Put that various changes can be made.
Claims (1)
1. the Optimization Scheduling of printing process under a kind of Flexible Manufacture environment, it is characterised in that: by being indicated with fuzzy number
Then uncertain process time and completion date determine the scheduling model and optimization aim of printing process under Flexible Environment, and
Optimization aim is optimized using the Optimized Operation scheme based on mixing meta-heuristic genetic algorithm；Wherein scheduling model foundation
The machining the time of every batch of printable fabric on each machine is established, in actual production, by replacement paper, switching spray head etc.
Many uncertain factors influence, and process time cannot accurately be predicted, and deadline and delivery date can only be estimated in some section, therefore
Process time and deadline are indicated with Triangular Fuzzy Number herein；Each operation Oi,jIndicate the jth procedure of printable fabric i,
It is in machine MkOn process time be expressed as with Triangular Fuzzy NumberWhereinIndicate minimum process
Time,Indicate most probable process time,Indicate maximum process time；The deadline of each operation is expressed asWhereinIndicate the minimum completion time of the jth procedure of workpiece i,Indicate the jth of workpiece i
The most probable deadline of procedure,Indicate the longest finishing time of the jth procedure of workpiece i, subordinating degree function can table
It is shown as:
Following constraint condition need to be met simultaneously: the same workpiece of any moment can only at most be processed on a machine；Appoint
One moment, same machine can only at most handle a procedure；Any operation cannot be interrupted during processing；Same workpiece
Process have to process in a upper process and just can be carried out the processing of next process；
Optimization aim is to minimize earliest completion date Cmax:
Cmax=maxCi
Wherein CiFor the machining the time of printable fabric i；
Specific step is as follows for the Optimized Operation scheme based on mixing meta-heuristic genetic algorithm:
Step1, initialization of population: generating the low dimensional individual of the high-dimensional individual of a popsize and same number, wherein
Popsize represents population scale, and the chromosome length of high-dimensional individual is 6, each chromosome can be appointing between 1 to 6
Meaning number, 6 kinds of rudimentary heuristic operations are as follows:
Rudimentary heuristic operation one: random crossover operation randomly selects two from operation coding and swaps；
Rudimentary heuristic operation two: preceding paragraph insertion operation selects a position from operation coding at random, by randomly selected one
A process is inserted into before this position；
Rudimentary heuristic operation three: consequent insertion operation selects a position from operation coding at random, after this position
It is inserted into a randomly selected process；
Rudimentary heuristic operation four: adjacent crossover operation randomly chooses a position, by its previous or the latter process and he
Exchange；
Rudimentary heuristic operation five: backout selects 3 to 4 continuous operations, by their backwards in the sequence of operation at random
Arrangement；
Rudimentary heuristic operation six: intercept operation randomly chooses 1 to 5 continuous operations, they is inserted into sequence of operation head
End；
Low dimensional individual represents the printing process of each printable fabric, and with the corresponding each group of operation of high-dimensional chromosome come more
The solution of new low dimensional, the degree for solving quality represents the adaptive value of the solution, when calculating adaptive value, to keep scheduling compact as far as possible,
It decodes low dimensional individual to decode using greedy mobilization, according to the operation in process operations string, using greedy strategy successively to every
One machine for being capable of processing the operation carries out mobilization decoding, then that shortest machine of completion date is selected to be processed,
It when calculating adaptive value, needs to use the summation of Triangular Fuzzy Number, subtracts each other, take big operation, wherein summation, phase reducing are based on
The fuzzy deadline is calculated, for two Triangular Fuzzy Number X=(x1,x2,x3) and Y=(y1,y2,y3), sum operation is defined as:
X+Y=(x1+y1,x2+y2,x3+y3)
Subtract each other Operation Definition are as follows:
X-Y=(x1-y1,x2-y2,x3-y3)
Sequence compares operation for the relatively fuzzyyer deadline, for two Triangular Fuzzy Number X=(x1,x2,x3) and Y=(y1,
y2,y3), following foundation is used when sequence:
According to 1: calculatingBy Z1Primary foundation as sequence；
According to 2: if two TFN are equal, defining Z2=x2Secondary foundation as sequence；
According to 3: if two TFN the first two foundations are all equal, using Z3=x3-x1As sort by；
It is big according to that can be ranked up and take to Triangular Fuzzy Number according to three above；
Initial solution is wanted compliance problem feature and is required, meanwhile, the quantity of solution will reach population scale, enable the number of iterations gen=1；
Initial temperature T in Step2, set algorithm0: local optimum ability is jumped out for further increase algorithm, in entire algorithm
Among insertion simulated annealing optimize the search capability of rudimentary heuristic operation, among algorithm, cooling control table is set,
Cooled down when each iteration according to cooling ratio α, temperature T influences rudimentary heuristic operation by following formula:
Wherein Δ f indicates the difference of each new explanation and old solution adaptive value, since the targeted problem target value of the present invention is fuzzy
Number, carrys out de-fuzzy used here as the mean value of two fuzzy numbers, obtains a real number, each rudimentary heuristic when each iteration
If operating obtained new explanation better than old solution, old solution is replaced, otherwise the random real number r generated between one 0 to 1, if r is greater than
Probability P then receives this difference solution；
Step3, it updates high-dimensional population: using the mode of roulette first, sieved according to the adaptive value of high-dimensional chromosome
Choosing, twice of individual for selecting number of individuals in population are put into candidate pool and are ready for cross and variation；Later by candidate pool
Body takes out two-by-two, carries out two-point crossover operation, that is, the two o'clock on one of chromosome is randomly selected, by the more forward point in position
Chromosome after the point of chromosome and position more rearward before retains, then selects one in chromosome completion from another individual
The part of individual missing forms feasible schedule solution, and is made a variation according to the adaptive mutation rate of setting, random selection one
Position chromosome is inserted into a random position, this helps to jump out locally optimal solution；
Step4, work piece operations sequence is updated according to high-dimensional population: by forming new high-dimensional kind after cross and variation
Group, low heuristic operation is according to corresponding to individual each in high-dimensional population come more new process sequence；
Step5, higher-dimension population and problem population protect it is excellent: new population and old population are put together according to adaptive value again
Old population of the good individual as next iteration is selected in sequence；
Step6, termination condition: the number of iterations is arranged to N, judges whether to reach the condition for terminating iteration, is to terminate iteration,
Otherwise temperature is reduced, Step3 is jumped to, is iterated until meeting termination condition.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810825846.7A CN109116816A (en) | 2018-07-25 | 2018-07-25 | The Optimization Scheduling of printing process under a kind of Flexible Manufacture environment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810825846.7A CN109116816A (en) | 2018-07-25 | 2018-07-25 | The Optimization Scheduling of printing process under a kind of Flexible Manufacture environment |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108983719A (en) * | 2018-07-24 | 2018-12-11 | 昆明理工大学 | The Optimization Scheduling of panel beating process in a kind of manufacture of computer cabinet |
| CN110991732A (en) * | 2019-11-28 | 2020-04-10 | 武汉理工大学 | Building material equipment manufacturing process optimization scheduling method based on energy consumption clustering |
| CN111596622A (en) * | 2020-04-15 | 2020-08-28 | 无锡市江南橡塑机械有限公司 | Flexible job shop scheduling method of ECM (electronic model control) rule distribution estimation algorithm |
| CN111985705A (en) * | 2020-08-13 | 2020-11-24 | 复旦大学 | Multipoint position shortest path calculation method |
| CN116224946A (en) * | 2023-03-24 | 2023-06-06 | 华中科技大学 | Optimized scheduling method and system for production and logistics integration of mechanical part processing workshop |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030105616A1 (en) * | 2001-11-07 | 2003-06-05 | Institute For Information Industry | Transportation crew dispatch method based on single day business |
| CN104808629A (en) * | 2015-04-20 | 2015-07-29 | 广东工业大学 | Flexible robot manufacturing unit scheduling method |
| CN105550751A (en) * | 2015-12-15 | 2016-05-04 | 重庆大学 | Steelmaking-continuous casting scheduling method utilizing priority policy hybrid genetic algorithm |
| CN107831745A (en) * | 2017-11-09 | 2018-03-23 | 西南交通大学 | A kind of flexible job shop inserts single action state method for optimizing scheduling |
-
2018
- 2018-07-25 CN CN201810825846.7A patent/CN109116816A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030105616A1 (en) * | 2001-11-07 | 2003-06-05 | Institute For Information Industry | Transportation crew dispatch method based on single day business |
| CN104808629A (en) * | 2015-04-20 | 2015-07-29 | 广东工业大学 | Flexible robot manufacturing unit scheduling method |
| CN105550751A (en) * | 2015-12-15 | 2016-05-04 | 重庆大学 | Steelmaking-continuous casting scheduling method utilizing priority policy hybrid genetic algorithm |
| CN107831745A (en) * | 2017-11-09 | 2018-03-23 | 西南交通大学 | A kind of flexible job shop inserts single action state method for optimizing scheduling |
Non-Patent Citations (4)
| Title |
|---|
| 孙亮，等: "自适应超启发式遗传算法求解随机型生产调度问题", 《小型微型计算机系统》 * |
| 巴黎，等: "考虑不确定加工时间的工艺规划与调度集成问题研究", 《中国机械工程》 * |
| 彭运芳，等: "不确定条件下基于遗传算法的作业车间调度问题", 《上海大学学报（自然科学版）》 * |
| 方伯芃，等: "不确定环境下的产业链生产与配送协同调度优化", 《计算机集成制造系统》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108983719A (en) * | 2018-07-24 | 2018-12-11 | 昆明理工大学 | The Optimization Scheduling of panel beating process in a kind of manufacture of computer cabinet |
| CN110991732A (en) * | 2019-11-28 | 2020-04-10 | 武汉理工大学 | Building material equipment manufacturing process optimization scheduling method based on energy consumption clustering |
| CN111596622A (en) * | 2020-04-15 | 2020-08-28 | 无锡市江南橡塑机械有限公司 | Flexible job shop scheduling method of ECM (electronic model control) rule distribution estimation algorithm |
| CN111596622B (en) * | 2020-04-15 | 2021-07-27 | 无锡市江南橡塑机械有限公司 | Flexible job shop scheduling method of ECM (electronic model control) rule distribution estimation algorithm |
| CN111985705A (en) * | 2020-08-13 | 2020-11-24 | 复旦大学 | Multipoint position shortest path calculation method |
| CN116224946A (en) * | 2023-03-24 | 2023-06-06 | 华中科技大学 | Optimized scheduling method and system for production and logistics integration of mechanical part processing workshop |
| CN116224946B (en) * | 2023-03-24 | 2023-11-14 | 华中科技大学 | Optimized scheduling method and system for production and logistics integration of mechanical part processing workshop |
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