CN109023596A - The method matched automatically for fiber in Spinning process - Google Patents
The method matched automatically for fiber in Spinning process Download PDFInfo
- Publication number
- CN109023596A CN109023596A CN201810830980.6A CN201810830980A CN109023596A CN 109023596 A CN109023596 A CN 109023596A CN 201810830980 A CN201810830980 A CN 201810830980A CN 109023596 A CN109023596 A CN 109023596A
- Authority
- CN
- China
- Prior art keywords
- scheme
- raw material
- cotton
- target
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- D—TEXTILES; PAPER
- D01—NATURAL OR MAN-MADE THREADS OR FIBRES; SPINNING
- D01G—PRELIMINARY TREATMENT OF FIBRES, e.g. FOR SPINNING
- D01G5/00—Separating, e.g. sorting, fibres
-
- D—TEXTILES; PAPER
- D01—NATURAL OR MAN-MADE THREADS OR FIBRES; SPINNING
- D01G—PRELIMINARY TREATMENT OF FIBRES, e.g. FOR SPINNING
- D01G13/00—Mixing, e.g. blending, fibres; Mixing non-fibrous materials with fibres
Landscapes
- Engineering & Computer Science (AREA)
- Textile Engineering (AREA)
- Yarns And Mechanical Finishing Of Yarns Or Ropes (AREA)
- Preliminary Treatment Of Fibers (AREA)
Abstract
The invention discloses a kind of methods matched automatically for fiber in Spinning process, comprising: the fibrous raw material of acquisition is classified automatically according to similar performance principle；The multiple-objection optimization distributed cotton of building spinning is distributed cotton model；User is allowed freely to set its all target and the importance of constraint condition, the different burdening modes for allowing user freely to set the optimization stringency of its different target, user is allowed freely to set single-piece apolegamy, the apolegamy of shipping mark number, classification apolegamy multi-objective Model；It freely participates in expanding by user making agent model meet the needs of different users model constraint requirements；The importance of the target and constraint condition that are freely set according to user and the stringency of objective optimization start each target weight distribution and constraint condition weight factor distribution automatically；Using algorithm solving model target, the cotton assorting scheme of batch is obtained.Distribute cotton process automation, cotton assorting scheme high quality can be achieved in the present invention.It can be the various cotton assorting schemes of different user optimization design.
Description
Technical field
The present invention relates to a kind of methods matched automatically for fiber in Spinning process.
Background technique
Raw material choice is a particularly significant and extremely complex job in Spinning process, be to raw material comprehensive performance and
Yarn quality carries out long-term follow, the process of judge and control, is directly related to product quality and economic benefit.
Raw material choice is dependent on resultant yarn purposes and material performance.Fiber have with it is short, be coarse and fine, have by force have it is weak, description
The index of its performance have length, fineness, intensity, elasticity, moisture absorption, containing it is miscellaneous, containing tens of items such as defect, color.These performances pair of raw material
Yarn quality, which has, to be significantly affected: first is that directly affecting.Fibre length, fineness are influenced containing miscellaneous, modulus, skin-friction coefficient etc.
Resultant yarn strand, strength, filoplume, knot are miscellaneous.Second is that influencing indirectly.Fibre property influences the formulation of spinning process, is associated with yarn quality.
Therefore, material matching selection is very necessary, is effectively tracked, is commented to raw material comprehensive performance during raw material choice
Sentence and control, it is also extremely complex for realizing that raw material by holding water is matched.
Theory and practice proves: resultant yarn purposes and material performance have significant diversity, therefore, even if in production for
The raw material of selection is not very much, but the scheme number of the various various raw material compositions, different blended ratio formed by their combinations is
Surprising, scheme is evaluated one by one to find that its workload of ideal scheme is huge incomparable, one ideal scheme of apolegamy is past
Toward as looked for a needle in a haystack.
For a long time, people did some explorations to matching cotton by computer, occurred linear programming technique (Chinese management science,
2002,10 (Special): 76-78), assembled scheme method (textile journal, 2002,23 (5): 59-60), improve assembled scheme method
(textile journal, 2005,26 (3): 38-40), hybrid genetic algorithm (journal of Zhejiang university (engineering version), 2009,43 (5): 801-
806), modified loop approach (textile journal, 2009,30 (3): 28-33), particle swarm algorithm (textile journal, 2011,32
) (2): 44-47 the related matching cotton by computer method such as, but the model of distributing cotton of these methods differs greatly with application demand, lacks
Practicability.Therefore, because lack efficiently it is advanced apolegamy fibrous raw material technology with and method, this complicated work of raw material choice
Make main or by manually empirically fuzzy operation, works hard tedious, effect of distributing cotton is difficult to most preferably.In this regard, spinning enterprise
The advanced technology and method of distributing cotton that forwardly waits in expectation is applied in production.
Summary of the invention
Distribute cotton process automation, cotton assorting scheme high quality are realized the purpose of the present invention is to provide a kind of, are different user
Being used for for (cotton spinning, wool spinning, bast fibre spinning, the spinning of various colors, other relevant industries) various cotton assorting schemes of optimization design is fine in Spinning process
Tie up the method matched automatically.
The technical solution of the invention is as follows:
A method of it is matched automatically for fiber in Spinning process, it is characterized in that: including the following steps:
(1) fibrous raw material detecting instrument, yarn qualities detecting instrument are connected with computer by cable, by fibrous raw material
Kuku deposit data, material performance data, the acquisition of yarn quality data imported into computer of distributing cotton；
(2) fibrous raw material of acquisition is classified automatically according to similar performance principle；
(3) multiple-objection optimization that the following spinning of a form is distributed cotton is constructed automatically by computer to distribute cotton model；
Model multiple target:
1) min cotton assorting scheme cost
2) each fibrous raw material shipping mark performance dispersion in each cotton assorting scheme of min
3) min connects each cotton assorting scheme and last scheme performance difference when criticizing
4) the irregular CV value of min resultant yarn strand
5) max yarn strength
6) min is miscellaneous at knot
7) min resultant yarn filoplume
8) min resultant yarn slubbing
9) min resultant yarn details
10) max resultant yarn is worth
Model constraint condition:
1) each raw material shipping mark selects radix, i.e. discrete magnitude；
2) each shipping mark selects packet number≤each shipping mark inventory's packet number
3) the total amount kg that distributes cotton of each cotton assorting scheme≤bale plucker spreads packet total amount kg
4) it is chosen the record strip number of the raw material storage after shipping mark serial number≤screening
5) inventory concerns, subdivision are obscured are as follows:
A) take into account as far as possible inventory mostly with few raw material
B) raw material for selecting part inventory relatively more as far as possible
C) raw material for selecting part inventory few as far as possible
6) it obscures collocation to require, subdivision are as follows:
A) new old raw material is arranged in pairs or groups selection as far as possible
B) as far as possible arrange in pairs or groups part new raw material
C) as far as possible arrange in pairs or groups partial obsolescence raw material
7) multiple shipping marks and other products raw material obscure requirement, subdivision are as follows:
A) marks more as far as possible are matched and use other products raw material less
B) the mark apolegamys more as far as possible in specified shipping mark number
C) do not had to as far as possible by the raw material that other products are selected
8) the fuzzy apolegamy of classification raw material requires, subdivision are as follows:
A) the classification raw material of selection is used
B) inhomogeneity is used with the classification raw material of selection and as far as possible
C) with the classification raw material of selection and as far as possible with similar
(4) user is allowed freely to set its all target and constraint condition the multi-objective Model of step (3) building
Importance, four grades that these importance are divided into that critically important, important, general, it doesn't matter；
(5) optimization that the multi-objective Model constructed for step (3) also allows user freely to set its different target is stringent
Property, it is strictly more excellent and fuzzy more excellent to be divided into target；Obscure more excellent subdivision are as follows:
1) yarn quality is paid the utmost attention to
2) ingredient cost and yarn quality are paid the utmost attention to
3) ingredient cost is paid the utmost attention to
4) it pays the utmost attention to ingredient cost and material performance is stablized
5) resultant yarn value is paid the utmost attention to
6) material performance is paid the utmost attention to stablize
7) yarn quality meets lock-in range
8) cost is minimum when yarn quality meets lock-in range
9) consider that performance is stablized under preferential ingredient cost
10) preferential performance stablizes lower ingredient consideration cost
11) yarn quality is considered under preferential ingredient cost
12) cost is considered under preferential quality
13) yarn quality lock-in range is considered under preferential ingredient cost
14) cost is considered under preferential yarn quality lock-in range；
(6) multi-objective Model constructed for step (3) also allows user freely to set single-piece apolegamy, shipping mark number is matched,
The different burdening modes of classification apolegamy；
(7) it is freely participated in by user, i.e., the expansion that requires to simulated target of step (4), step (5) and step (6) are right
The extension of model constraint requirements, the agent model for constructing step (3) meet the needs of different users；
(8) importance of the target and constraint condition freely set according to user and the stringency of objective optimization, automatically
Start each target weight distribution and constraint condition weight factor distribution；
(9) following algorithm solving model target is used, the cotton assorting scheme of batch is obtained.
For above-mentioned Model for Multi-Objective Optimization, be first randomly generated meet constraint condition scale be popsize it is initial
Population calculates the multiple target value of each cotton assorting scheme in initial population, according to the size and its power of each cotton assorting scheme target value
Arrangement position rank of each scheme in all schemes is counted again, according to rank value from big to small to schemes ranking, rejects w
It is arranged in the equal scheme of last rank, then paternal to randomly select w in the population of popsiaze-w from remaining scale
Case is mated two-by-two, is generatedPopulation is added in a new departure, is to new scalePopulation in
Scheme calculates target value and sequence again, then rejects worst scheme, so recycles, until the target value of each scheme is not improved to
Only, multiple high-quality cotton assorting schemes of batch are ultimately formed；
(10) using control hot key dynamic regulation algorithm parameter, cotton assorting scheme design process is controlled；
(11) multiple high-quality cotton assorting scheme list displays of batch are arranged in computer screen according to the target that user specifies
Sequence requirement, respective objects value distance between each cotton assorting scheme of programming count, according to apart from size to schemes ranking, by optimal case
It is listed in table end；Or show scheme by target value curvilinear figure, user passes through the mouse screening high-quality side of distributing cotton on the graph
Case.
Step (2) described performance includes the indexs such as fibre length, fineness, intensity, maturity.
Step (10) is to control cotton assorting scheme design process using hot key dynamic regulation algorithm parameter control as follows:
Distribute cotton process automation, cotton assorting scheme high quality can be achieved in the present invention.It can be different user (cotton spinning, wool spinning, fiber crops
Spin, various colors are spun, other relevant industries) the various cotton assorting schemes of optimization design.
In existing document, model of distributing cotton mainly has 2 classes: one kind is that one kind is the mixed performance indicator of fiber and reason
Property scale error minimize, another kind of is cost minimization, then plus the mixed performance indicator of fiber and rationality scale error most
Smallization.
The first kind (journal of Zhejiang university (engineering version), 2009,43 (5): 801-806；Textile journal, 2011,32 (2):
44-47)
Objective function is defined as the minimum of the summation of the difference of fiber items average behavior and fiber items ideal performance
Change.Constraint condition is defined as the control of fiber average behavior in some section, and the fiber apolegamy ratio in some area is no more than limit
Amount.Here fiber average behavior refers to the mixed performance of various fibers of apolegamy.Fibre property refers to fibre length, fineness etc..
Objective function
Min F=∑ | fiber average behavior-fiber ideal performance |
Constraint condition
The a certain a certain UPS upper performance score of performance≤fiber of a certain performance limits≤fiber of fiber
The fiber apolegamy ratio≤ratio limitation in some area
Second class (textile journal, 2009,30 (3): 28-33)
Objective function is defined as to select the totle drilling cost, fiber items average behavior and fiber items ideal performance of fiber
The minimum of the summation of difference.Constraint condition is defined as the control of fiber average behavior in some section, the fiber in some area
Apolegamy ratio is no more than limitation.Here fiber average behavior refers to the mixed performance of various fibers of apolegamy.Fibre property refers to
Fibre length, fineness etc..
Objective function
Fiber totle drilling cost+∑ of Min F=apolegamy | fiber average behavior-fiber ideal performance |
Constraint condition
The a certain a certain UPS upper performance score of performance≤fiber of a certain performance limits≤fiber of fiber
The fiber apolegamy ratio≤ratio limitation in some area
Note: in above-mentioned two class model, 3 points are insufficient: first is that fiber ideal performance formulation be it is less scientific, it is assorted
It is ideal performance, is had no basis；Second is that fiber properties difference is merged, an objective function is formd, sternly
Say it is not multiple target in lattice meaning；Third is that fiber properties difference merge it is bad because different performance refers to subject matter
It is different to manage unit, quantity variance is very big, such as fibre length is usually tens millimeters, and fineness is up to five or six thousand public branch.Therefore,
Method of distributing cotton based on above-mentioned model is without practicability.
The model feature that the application uses is:
Objective function had both included fibre property difference, also included yarn quality, includes also cost of material etc., forms multiple mesh
Mark.
Constraint condition includes shipping mark selection radix, fibrous raw material obscures inventory concerns, fuzzy collocation requires, fuzzy use is wanted
Multiple constraints such as ask.
Here, it is however emphasized that 4 points:
1, about objective function, fibre property difference includes two kinds of situations here: first is that various in the same cotton assorting scheme
The performance difference between fibrous raw material matched, i.e. fibre property dispersion；Second is that the cotton assorting scheme of current design and last issue
The material performance difference of cotton assorting scheme connects batch material performance difference.Do you why to consider to connect batch material performance difference? this be because
For, spinning material is that a collection of take over uses in production, it is preceding a batch raw material be finished, behind will be according to new cotton assorting scheme
A collection of new raw material being connected, so, to consider to connect batch material performance difference.
2, in two class models listed by existing document, " mixed cotton quality " is also said sometimes, this refers to that cotton fiber is mixed
Fiber items average behavior, such as average fineness, the average length of fiber, do not refer to the quality of yarn.Yarn is different from fiber, at
Yarn quality refers to the yarn qualities that will be measured after fiber spun yarn, usually there is yarn evenness, yarn strength etc..
3, the importance of the target and constraint condition of herein described model can be freely set as very heavy by user
It wants, is important, is general, it doesn't matter four grades；Optimization requirement can also be set as stringent more excellent and fuzzy more excellent.Therefore, this Shen
Please model maximum feature be fuzzy optimization, be not the non-fuzzy optimization of two class models listed by existing document.
4, why herein described model sets the constraint of shipping mark selection radix, is because of workers carry's cotton packet in production
When, it is expected that the cotton packet being handled upside down is whole packet, or is at most split as 2 half packets for a whole packet, do not receive to split a whole packet
It is that zero scrappy broken fritter block is carried again, therefore, sets shipping mark selection radix (such as 1,2,3).Such as setting shipping mark selection
Radix=2, then, the cotton packet number that each fiber is selected is exactly 2 packets, 4 packets, 6 packets etc., and this cotton assorting scheme is convenient for worker behaviour
Make.In two class models listed by existing document, after certain fiber is matched by a certain percentage, it is converted to very possible when cotton packet number
For the cotton packet number with decimal, such as 2.18 packets, such worker can not be operated, therefore not applicable.
The model of the application is real multiple target.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples.
Fig. 1 is that the acquisition of fibrous raw material detecting instrument data imported into computer schematic diagram of distributing cotton.
Fig. 2 is spinning cotton assorting scheme objective design process schematic diagram.
Fig. 3 is that sequence schematic diagram is comprehensively compared in the spinning cotton assorting scheme of batch.
Specific embodiment
A method of it is matched automatically for fiber in Spinning process, it is characterized in that: including the following steps:
(1) fibrous raw material detecting instrument, yarn qualities detecting instrument are connected with computer by cable, by fibrous raw material
Kuku deposit data, material performance data, the acquisition of yarn quality data imported into computer of distributing cotton；
(2) fibrous raw material of acquisition is classified automatically according to similar performance principle；
(3) multiple-objection optimization that the following spinning of a form is distributed cotton is constructed automatically by computer to distribute cotton model；
Model multiple target:
Model multiple target:
1) min cotton assorting scheme cost
2) each fibrous raw material shipping mark performance dispersion in each cotton assorting scheme of min
3) min connects each cotton assorting scheme and last scheme performance difference when criticizing
4) the irregular CV value of min resultant yarn strand
5) max yarn strength
6) min is miscellaneous at knot
7) min resultant yarn filoplume
8) min resultant yarn slubbing
9) min resultant yarn details
10) max resultant yarn is worth
Model constraint condition:
1) each raw material shipping mark selects radix, i.e. discrete magnitude；
2) each shipping mark selects packet number≤each shipping mark inventory's packet number
3) the total amount kg that distributes cotton of each cotton assorting scheme≤bale plucker spreads packet total amount kg
4) it is chosen the record strip number of the raw material storage after shipping mark serial number≤screening
5) inventory concerns, subdivision are obscured are as follows:
A) take into account as far as possible inventory mostly with few raw material
B) raw material for selecting part inventory relatively more as far as possible
C) raw material for selecting part inventory few as far as possible
6) it obscures collocation to require, subdivision are as follows:
A) new old raw material is arranged in pairs or groups selection as far as possible
B) as far as possible arrange in pairs or groups part new raw material
C) as far as possible arrange in pairs or groups partial obsolescence raw material
7) multiple shipping marks and other products raw material obscure requirement, subdivision are as follows:
A) marks more as far as possible are matched and use other products raw material less
B) the mark apolegamys more as far as possible in specified shipping mark number
C) do not had to as far as possible by the raw material that other products are selected
8) the fuzzy apolegamy of classification raw material requires, subdivision are as follows:
A) the classification raw material of selection is used
B) inhomogeneity is used with the classification raw material of selection and as far as possible
C) with the classification raw material of selection and as far as possible with similar
(4) user is allowed freely to set its all target and constraint condition the multi-objective Model of step (3) building
Importance, four grades that these importance are divided into that critically important, important, general, it doesn't matter；
(5) optimization that the multi-objective Model constructed for step (3) also allows user freely to set its different target is stringent
Property, it is strictly more excellent and fuzzy more excellent to be divided into target；Obscure more excellent subdivision are as follows:
1) yarn quality is paid the utmost attention to
2) ingredient cost and yarn quality are paid the utmost attention to
3) ingredient cost is paid the utmost attention to
4) it pays the utmost attention to ingredient cost and material performance is stablized
5) resultant yarn value is paid the utmost attention to
6) material performance is paid the utmost attention to stablize
7) yarn quality meets lock-in range
8) cost is minimum when yarn quality meets lock-in range
9) consider that performance is stablized under preferential ingredient cost
10) preferential performance stablizes lower ingredient consideration cost
11) yarn quality is considered under preferential ingredient cost
12) cost is considered under preferential quality
13) yarn quality lock-in range is considered under preferential ingredient cost
14) cost is considered under preferential yarn quality lock-in range；
(6) multi-objective Model constructed for step (3) also allows user freely to set single-piece apolegamy, shipping mark number is matched,
The different burdening modes of classification apolegamy；
(7) it is freely participated in by user, i.e., the expansion that requires to simulated target of step (4), step (5) and step (6) are right
The extension of model constraint requirements, the agent model for constructing step (3) meet the needs of different users；
(8) importance of the target and constraint condition freely set according to user and the stringency of objective optimization, automatically
Start each target weight distribution and constraint condition weight factor distribution；
(9) following algorithm solving model target is used, the cotton assorting scheme of batch is obtained.
For above-mentioned Model for Multi-Objective Optimization, be first randomly generated meet constraint condition scale be popsize it is initial
Population calculates the multiple target value of each cotton assorting scheme in initial population, according to the size and its power of each cotton assorting scheme target value
Arrangement position rank of each scheme in all schemes is counted again, according to rank value from big to small to schemes ranking, rejects w
It is arranged in the equal scheme of last rank, then paternal to randomly select w in the population of popsiaze-w from remaining scale
Case is mated two-by-two, generates 2cwPopulation is added in 2 new departures, is to new scalePopulation in
Scheme calculates target value and sequence again, then rejects worst scheme, so recycles, until the target value of each scheme is not improved to
Only, multiple high-quality cotton assorting schemes of batch are ultimately formed；
(10) using control hot key dynamic regulation algorithm parameter, cotton assorting scheme design process is controlled；
(11) multiple high-quality cotton assorting scheme list displays of batch are arranged in computer screen according to the target that user specifies
Sequence requirement, respective objects value distance between each cotton assorting scheme of programming count, according to apart from size to schemes ranking, by optimal case
It is listed in table end；Or show scheme by target value curvilinear figure, user passes through the mouse screening high-quality side of distributing cotton on the graph
Case.
Step (2) described performance includes fibre length, fineness, intensity, maturity etc..
Step (10) is to control cotton assorting scheme design process using hot key dynamic regulation algorithm parameter control as follows:
Claims (3)
1. a kind of method matched automatically for fiber in Spinning process, it is characterized in that: including the following steps:
(1) fibrous raw material detecting instrument, yarn qualities detecting instrument are connected with computer by cable, by fibrous raw material Kuku
Deposit data, material performance data, the acquisition of yarn quality data imported into computer of distributing cotton；
(2) fibrous raw material of acquisition is classified automatically according to similar performance principle；
(3) multiple-objection optimization that the following spinning of a form is distributed cotton is constructed automatically by computer to distribute cotton model；
Model multiple target:
1) min cotton assorting scheme cost
2) each fibrous raw material shipping mark performance dispersion in each cotton assorting scheme of min
3) min connects each cotton assorting scheme and last scheme performance difference when criticizing
4) the irregular CV value of min resultant yarn strand
5) max yarn strength
6) min is miscellaneous at knot
7) min resultant yarn filoplume
8) min resultant yarn slubbing
9) min resultant yarn details
10) max resultant yarn is worth
Model constraint condition:
1) each raw material shipping mark selects radix, i.e. discrete magnitude；
2) each shipping mark selects packet number≤each shipping mark inventory's packet number
3) the total amount kg that distributes cotton of each cotton assorting scheme≤bale plucker spreads packet total amount kg
4) it is chosen the record strip number of the raw material storage after shipping mark serial number≤screening
5) inventory concerns, subdivision are obscured are as follows:
A) take into account as far as possible inventory mostly with few raw material
B) raw material for selecting part inventory relatively more as far as possible
C) raw material for selecting part inventory few as far as possible
6) it obscures collocation to require, subdivision are as follows:
A) new old raw material is arranged in pairs or groups selection as far as possible
B) as far as possible arrange in pairs or groups part new raw material
C) as far as possible arrange in pairs or groups partial obsolescence raw material
7) multiple shipping marks and other products raw material obscure requirement, subdivision are as follows:
A) marks more as far as possible are matched and use other products raw material less
B) the mark apolegamys more as far as possible in specified shipping mark number
C) do not had to as far as possible by the raw material that other products are selected
8) the fuzzy apolegamy of classification raw material requires, subdivision are as follows:
A) the classification raw material of selection is used
B) inhomogeneity is used with the classification raw material of selection and as far as possible
C) with the classification raw material of selection and as far as possible with similar
(4) user is allowed freely to set the important of its all target and constraint condition the multi-objective Model of step (3) building
Property, four grades that these importance are divided into that critically important, important, general, it doesn't matter；
(5) also allow user freely to set the optimization stringency of its different target the multi-objective Model of step (3) building, divide
It is strictly more excellent and fuzzy more excellent for target；Obscure more excellent subdivision are as follows:
1) yarn quality is paid the utmost attention to
2) ingredient cost and yarn quality are paid the utmost attention to
3) ingredient cost is paid the utmost attention to
4) it pays the utmost attention to ingredient cost and material performance is stablized
5) resultant yarn value is paid the utmost attention to
6) material performance is paid the utmost attention to stablize
7) yarn quality meets lock-in range
8) cost is minimum when yarn quality meets lock-in range
9) consider that performance is stablized under preferential ingredient cost
10) preferential performance stablizes lower ingredient consideration cost
11) yarn quality is considered under preferential ingredient cost
12) cost is considered under preferential quality
13) yarn quality lock-in range is considered under preferential ingredient cost
14) cost is considered under preferential yarn quality lock-in range；
(6) user is also allowed freely to set single-piece apolegamy, the apolegamy of shipping mark number, classification the multi-objective Model of step (3) building
The different burdening modes of apolegamy；
(7) it is freely participated in by user, i.e., the expansion that requires to simulated target of step (4), step (5) and step (6) are to model
The extension of constraint requirements, the agent model for constructing step (3) meet the needs of different users；
(8) importance of the target and constraint condition freely set according to user and the stringency of objective optimization, it is automatic to start
Each target weight distribution and constraint condition weight factor distribution；
(9) following algorithm solving model target is used, the cotton assorting scheme of batch is obtained.
For above-mentioned Model for Multi-Objective Optimization, it is first randomly generated the initial population for meeting that the scale of constraint condition is popsize,
The multiple target value for calculating each cotton assorting scheme in initial population is counted according to the size and its weight of each cotton assorting scheme target value
Arrangement position rank of each scheme in all schemes rejects w and is arranged according to rank value from big to small to schemes ranking
Last rank equal scheme, then randomly select w paternal cases from the population that remaining scale is popsiaze-w and carry out
It mates, generates two-by-twoPopulation is added in a new departure, is to new scalePopulation in scheme again
Target value and sequence are calculated, then rejects worst scheme, is so recycled, until the target value of each scheme is not improved, most
The batches of multiple high-quality cotton assorting schemes of end form；
(10) using control hot key dynamic regulation algorithm parameter, cotton assorting scheme design process is controlled；
(11) multiple high-quality cotton assorting scheme list displays of batch are wanted in computer screen according to the goal ordering that user specifies
It asks, respective objects value distance between each cotton assorting scheme of programming count, to schemes ranking, optimal case is listed according to apart from size
Table end；Or show scheme by target value curvilinear figure, user passes through mouse screening high-quality cotton assorting scheme on the graph.
2. the method according to claim 1 matched automatically for fiber in Spinning process, it is characterized in that: step (2) institute
Stating performance includes fibre length, fineness, intensity, maturity.
3. the method according to claim 1 matched automatically for fiber in Spinning process, it is characterized in that: step (10) is
Using hot key dynamic regulation algorithm parameter control as follows, cotton assorting scheme design process is controlled:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810830980.6A CN109023596B (en) | 2018-07-26 | 2018-07-26 | Method for automatically matching fibers in spinning production |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810830980.6A CN109023596B (en) | 2018-07-26 | 2018-07-26 | Method for automatically matching fibers in spinning production |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109023596A true CN109023596A (en) | 2018-12-18 |
| CN109023596B CN109023596B (en) | 2020-08-18 |
Family
ID=64645351
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810830980.6A Active CN109023596B (en) | 2018-07-26 | 2018-07-26 | Method for automatically matching fibers in spinning production |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109023596B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110806764A (en) * | 2019-09-24 | 2020-02-18 | 深圳富畅智能系统有限公司 | Method for synthesizing and recycling manufacturing available nozzle material according to proportion |
| CN113106574A (en) * | 2021-04-07 | 2021-07-13 | 北京智棉科技有限公司 | Method, system and storage medium for automatically grouping, selecting and matching cotton |
| US20220318863A1 (en) * | 2019-06-28 | 2022-10-06 | Covestro Llc | Methods for graphical depiction of a value of a property of a material |
-
2018
- 2018-07-26 CN CN201810830980.6A patent/CN109023596B/en active Active
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220318863A1 (en) * | 2019-06-28 | 2022-10-06 | Covestro Llc | Methods for graphical depiction of a value of a property of a material |
| US11854053B2 (en) * | 2019-06-28 | 2023-12-26 | Covestro Llc | Methods for graphical depiction of a value of a property of a material |
| CN110806764A (en) * | 2019-09-24 | 2020-02-18 | 深圳富畅智能系统有限公司 | Method for synthesizing and recycling manufacturing available nozzle material according to proportion |
| CN113106574A (en) * | 2021-04-07 | 2021-07-13 | 北京智棉科技有限公司 | Method, system and storage medium for automatically grouping, selecting and matching cotton |
| CN113106574B (en) * | 2021-04-07 | 2022-06-07 | 北京智棉科技有限公司 | Method, system and storage medium for automatically grouping, selecting and matching cotton |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109023596B (en) | 2020-08-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109023596A (en) | The method matched automatically for fiber in Spinning process | |
| Joshi et al. | Estimation of total factor productivity in the Indian garment industry | |
| DE102004023267A1 (en) | Determine a policy parameter for an entity in a supply chain | |
| Shahroodi et al. | Application of analytical hierarchy process (AHP) technique to evaluate and selecting suppliers in an effective supply chain | |
| US20120089636A1 (en) | Dimensional data explorer | |
| Oliveira et al. | An extended goal programming model for the multiobjective integrated lot-sizing and cutting stock problem | |
| Cherkashin et al. | Firm heterogeneity and costly trade: A new estimation strategy and policy experiments | |
| Keramatpanah et al. | Application of analytical hierarchy process (AHP) technique to evaluate and selecting suppliers in an effective supply chain | |
| Chakraborty et al. | A quality function deployment-based expert system for cotton fibre selection | |
| Pal et al. | Effects of green improvement and pricing policies in a double dual-channel competitive supply chain under decision-making power strategies | |
| Zheng et al. | Reactive production with preprocessing restriction in supply chains with forecast updates | |
| Putri et al. | Cost of production price analysis using job order costing method in CV. Alam Lestari Jaya | |
| Mitra et al. | Determination of quality value of cotton fiber using integrated best-worst method-revised analytic hierarchy process | |
| Islam et al. | Optimizing yarn properties through response surface methodology: Finding the ideal flax and cotton fiber proportion in blended yarns by using design‐expert software | |
| Xie et al. | Research on Systematic Construction of Organizational Project Management (OPM) for Large Business | |
| Kang et al. | A simplified optimization in cotton bale selection and laydown | |
| US20180237958A1 (en) | Cotton mixes homogenization without categorizing bales in inventory | |
| Axunbabayev et al. | New technologies on manufacturing of multicomponent textile products from a mixture of fiber waste and other fibers in the processes of cocoon growing and their recycling | |
| Astini et al. | Implementation of Quality Control Thermal Bag Using the DMAIC Method (Case Study PT XYZ) | |
| Brown et al. | Lifting the wool curtain: recent reforms and new opportunities in the Chinese wool market with special reference to the" up-country" mills | |
| Dulange et al. | Quantification of factors influencing productivity using AHP technique: an approach towards productivity improvement | |
| Palokangas | Labour market regulation, productivity-improving R&D and endogenous growth | |
| Dotti et al. | A PERFORMANCE MEASUREMENT SYSTEM FOR THE TEXTILE AND CLOTHING INDUSTRY: THE PERFORMANCE BOX. | |
| Feng et al. | Analyzing Textile Industry by Linear Programming | |
| CN110443569A (en) | A kind of mineral exploration project management system based on intelligent batch processing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230818 Address after: 226000 No. 9 Siyuan Road, Chongchuan District, Nantong City, Jiangsu Province Patentee after: Nantong University Technology Transfer Center Co.,Ltd. Address before: 226019 Jiangsu city of Nantong province sik Road No. 9 Patentee before: NANTONG University |