CN109102112A - A kind of Optimization Scheduling using clothing factory's line flow procedure - Google Patents
A kind of Optimization Scheduling using clothing factory's line flow procedure Download PDFInfo
- Publication number
- CN109102112A CN109102112A CN201810843276.4A CN201810843276A CN109102112A CN 109102112 A CN109102112 A CN 109102112A CN 201810843276 A CN201810843276 A CN 201810843276A CN 109102112 A CN109102112 A CN 109102112A
- Authority
- CN
- China
- Prior art keywords
- wolf
- cargo
- optimization
- clothing factory
- indicate
- 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.)
- Pending
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 27
- 238000000034 method Methods 0.000 title claims abstract description 23
- 241000282461 Canis lupus Species 0.000 claims abstract description 46
- 238000004519 manufacturing process Methods 0.000 claims abstract description 15
- 238000009958 sewing Methods 0.000 claims description 15
- 238000012545 processing Methods 0.000 claims description 12
- 238000003754 machining Methods 0.000 claims description 3
- 230000003247 decreasing effect Effects 0.000 claims description 2
- 238000013459 approach Methods 0.000 claims 1
- 238000012804 iterative process Methods 0.000 claims 1
- 238000012163 sequencing technique Methods 0.000 claims 1
- 230000000694 effects Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000004753 textile Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06316—Sequencing of tasks or work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Educational Administration (AREA)
- Evolutionary Computation (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Manufacturing & Machinery (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
Abstract
The present invention relates to a kind of Optimization Schedulings applied to clothing factory's line flow procedure, belong to the intelligent optimization scheduling field of manufacturing industry production process.The present invention proposes mixing grey wolf optimization algorithm for the model and is optimized accordingly to it using the process of cargo in clothing factory's production order as scheduling model and optimization aim；It is established the time required to wherein scheduling model processes in the order clothes according to each seat in the plane in clothing factory, optimization aim is minimizes Maximal Makespan.The present invention can provide clothing factory's line flow procedure more reasonable production decision within a short period of time.
Description
Technical field
The present invention relates to a kind of Optimization Schedulings using clothing factory's line flow procedure, belong to manufacturing industry production
The intelligent optimization scheduling field of process.
Background technique
Always in occupation of critical role in the national economic development in China, Clothing Export also begins textile clothing industry for many years
Remain No. 1 in the world eventually.But the clothes production enterprise in China is analyzed, and it is at present still typical labor-intensive enterprises, it is raw
It produces management and does not all catch up with quick customer demand, clothes production enterprise's informationization is imperative.Shanghai on March 12 is handed within 2004
Big great High-Tech Building, has held the information-based new trend of A SP garment enterprise --- and the information-based science and technology of garment enterprise is ground
It begs for, A SP mode is just developed once release with swift and violent speed, especially in garment enterprise.Therefore, many colleges and universities are also numerous and confused
It is added to the research of this project.In September, 2003, Donghua University cooperate to start the 211 Project with high fashion group
" garment enterprise the is information-based " project subsidized, develops garment production information management system.By the effort close to 1 year, take
The Production MIS that initial achievements are developed according to enterprise practical situation was obtained, it can be to the full mistake for going out fortune from order to ready-made clothes
Journey is managed and monitors, and collects the creation data in product (WIP), existing quality monitoring, and labour cost is calculated.
Clothing factory's line flow procedure needs to examine following problems in the actual production process with regard to particularly important
Consider: 1, the working ability of clothing factory sewing machine itself being accounted for；2, it the proficiency of worker: 3, in production process is likely to occur
Various emergency cases.For this problem, if being solved completely with exact algorithm, often with the expansion of problem scale and
Cause excessive calculation amount, it is difficult to reach the requirement of quick response.
Requirement due to different cargos to sewing machine is different, secondly sewing machine situation itself different as same in processed
Time required for cargo is different.Therefore, reasonable processing scheme can be minimized Maximal Makespan.The present invention is with clothing factory
The process for producing cargo in order is scheduling model and optimization aim, proposes mixing grey wolf optimization algorithm to it for the model
Optimized accordingly, allow the resource rational utilization of clothing factory, reduced production cost, finally, maximize the economic effect of clothing factory
Benefit.
Summary of the invention
The technical problem to be solved in the invention is to provide reasonable processing side within the relatively small number of time for clothing factory
Case allows each sewing machine to be reasonably utilized, to realize energy-saving.It is provided in a relatively short period of time for clothing factory rationally
Processing scheme.
The technical scheme is that a kind of Optimization Scheduling using clothing factory's line flow procedure, feature
It is: using the process of cargo in clothing factory's production order as scheduling model and optimization aim, is proposed for the model mixed
Grey wolf optimization algorithm is closed it is carried out to optimize to minimize its Maximal Makespan C accordinglymax(π)；Detailed process is as follows:
Maximal Makespan are as follows:
Cmax(π)=C (jn,m)
In formula, k=1,2 ..., m indicate that the sewing machine number of clothing factory, i=1,2 ..., n indicate the cargo number in order,
ti,kIndicate process time of the cargo i on sewing machine k, c (ji, k) and indicate cargo jiIn the completion of processing on sewing machine k
Between, π=(j1,j2,…,jn) indicate cargo sequence.
The mixing grey wolf optimization algorithm optimizes that specific step is as follows to scheduling model:
A, the initialization of population: initial population, a kind of processing of the corresponding cargo of each of group individual are generated at random
Sequentially, the scale of population is NP；
B, the more new stage: by where optimal solution in wolf pack, excellent solution, suboptimal solution wolf (be respectively labeled as α, β, γ wolf, remaining
Individual mark is w wolf), during hunting, wolf pack is approached under the guidance of α, β, γ wolf to food position (globally optimal solution),
Guide equation as follows:
D=| C*Xp(t)-X(t)|
Xp(t+1)=Xp(t)-A*D
A=2 α * r1-α
C=2*r2
Dα=| C1*Xa-X|,Dβ=| C1*Xβ-X|,Dγ=| C1*Xγ-X|
X1=Xα-A1*Dα,X2=Xβ-A2*Dβ,X3=Xγ-A3*Dγ
Wherein, t is the number of iterations, A, C, A1、C1It is coefficient of concordance vector, XpIndicate the position vector of prey, X is grey wolf
Position vector, D indicates that the distance between current wolf and food, X (t) indicate position vector of the t for wolf, Dα、Dβ、DγTable respectively
At a distance from showing α, β, γ wolf position between wolf pack position, Xa、Xβ、Xγ, indicate α, β, γ wolf position vector,
X1、X2、X3The revised position vector of α, β, γ wolf is respectively indicated, X (t+1) indicates the position vector of next-generation wolf pack, and α is repeatedly
0, r is linearly decreased to from 2 during generation1、r2It is the random number between [0,1].
Since grey wolf algorithm is the iteration optimization algorithms based on real number field, we use lov rule (largest order
Value rule) name placement by grey wolf in real number region be mapped as the cargo in order sequence, thus the processing to cargo
Sequence is iterated optimization, until finding lesser Maximal Makespan Cmax(π)。
C, the local search based on the field insert: to optimum individual (the Maximal Makespan C in the present agemax(π) i.e.
The smallest individual of fitness value) insert field search operation is carried out, it is replaced if due to current individual, otherwise not
Become.
D, it iterates and exports optimal population and find Maximal Makespan Cmax(π) minimum corresponding cargo sequence.
The beneficial effects of the present invention are: 1, order models are established according to home-delivery center's cargo process time；2, being directed to should
Model proposes mixing grey wolf algorithm, solves to the problem；Specific processing scheme is provided for the cargo processing of clothing factory,
To realize the reasonable utilization of clothing factory's resource, the maximization of its productivity effect is realized.
Detailed description of the invention
Fig. 1 is overall flow figure of the invention；
Fig. 2 is the Insert operation diagram for mixing grey wolf algorithm.
Specific embodiment
The present invention will be further explained below with reference to the attached drawings and specific examples.
Embodiment 1: clothing factory of Guangdong Province is connected to the order of 10 ready-made clothes, it is now desired to be machined by 4 sewing
It completes.But the working ability of each sewing machine is different, thus the time of each sewing machining different garment is not also identical, specifically
Constraint is shown in Table one:
The sewing machining Cargo restraint of table one
N indicates that cargo number, m indicate sewing machine number in table.
According to the pipeline model that preceding solution is established, the problem is solved using mixing grey wolf algorithm, such as
Shown in Fig. 1-2, the wolf where optimal solution, excellent solution, suboptimal solution in wolf pack (is respectively labeled as α, β, γ by initialization population first
Wolf), and chosen and then instructed the update of population, finally using the local search operation based on the field insert, to its into
Row iteration optimization.Initial population number is 100, mixes grey wolf algorithm iteration 20 times and obtains volume Maximal Makespan Cmax(π) is shown in Table
Two:
Maximal Makespan C under the different the number of iterations of table twomax(π)
From table it can be found that mixing grey wolf algorithm can preferably solve the problems, such as clothing factory to the processing arrangement of cargo,
And optimal processing sequence is provided in a relatively short period of time.
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 (3)
1. a kind of Optimization Scheduling applied to clothing factory's line flow procedure, it is characterised in that: ordered with clothing factory's production
Cargo process is scheduling model, Maximal Makespan C in listmax(π) is optimization aim, is calculated using mixing grey wolf optimization
Method carries out corresponding optimization to the model to minimize its Maximal Makespan Cmax(π), detailed process is as follows:
Maximal Makespan are as follows:
Cmax(π)=C (jn,m)
In formula, k=1,2 ..., m indicate that the sewing machine number of clothing factory, i=1,2 ..., n indicate the cargo number in order, ti,kTable
Show process time of the cargo i on sewing machine k, c (ji, k) and indicate cargo jiIn the completion of processing time on sewing machine k, π=
j1,j2,…,jnIndicate the sequence of cargo.
2. the Optimization Scheduling according to claim 1 applied to clothing factory's line flow procedure, it is characterised in that:
The mixing grey wolf optimization algorithm optimizes that specific step is as follows to scheduling:
A, the initialization of population: initial population, a kind of processing sequence of the corresponding cargo of each of group individual, kind are generated at random
The scale of group is NP；
B, the more new stage: the wolf where optimal solution, excellent solution, suboptimal solution in wolf pack is respectively labeled as α, β, γ wolf, remaining individual
Labeled as w wolf, during hunting, wolf pack approaches, guidance side under the guidance of α, β, γ wolf to food position, that is, globally optimal solution
Journey is as follows:
D=| C*Xp(t)-X(t)|
Xp(t+1)=Xp(t)-A*D
A=2 α * r1-α
C=2*r2
Dα=| C1*Xa-X|,Dβ=| C1*Xβ-X|,Dγ=| C1*Xγ-X|
X1=Xα-A1*Dα,X2=Xβ-A2*Dβ,X3=Xγ-A3*Dγ
Wherein, t is the number of iterations, A, C, A1、C1It is coefficient of concordance vector, XpIndicate the position vector of prey, X is the position of grey wolf
Vector is set, D indicates that the distance between current wolf and food, X (t) indicate position vector of the t for wolf, Dα、Dβ、DγRespectively indicate α,
β, γ wolf position between wolf pack position at a distance from, Xa、Xβ、Xγ, indicate α, β, γ wolf position vector, X1、X2、X3
The revised position vector of α, β, γ wolf is respectively indicated, X (t+1) indicates the position vector of next-generation wolf pack, and α is in an iterative process
0, r is linearly decreased to from 21、r2It is the random number between [0,1]；
D, the local search based on the field insert: to the optimum individual, that is, Maximal Makespan C in the present agemax(π) it is small individual into
Row insert field search operation, is replaced if due to current individual, otherwise constant；
E, it iterates and exports optimal population and find Maximal Makespan Cmax(π) minimum corresponding cargo sequence.
3. the Optimization Scheduling according to claim 2 applied to clothing factory's line flow procedure, it is characterised in that:
The cargo in order is mapped as using name placement of the lov rule by grey wolf in real number region in the mixing grey wolf optimization algorithm
Sequence, so that optimization is iterated to the Machining Sequencing of cargo, until finding lesser Maximal Makespan Cmax(π)。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810843276.4A CN109102112A (en) | 2018-07-27 | 2018-07-27 | A kind of Optimization Scheduling using clothing factory's line flow procedure |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810843276.4A CN109102112A (en) | 2018-07-27 | 2018-07-27 | A kind of Optimization Scheduling using clothing factory's line flow procedure |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109102112A true CN109102112A (en) | 2018-12-28 |
Family
ID=64847809
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810843276.4A Pending CN109102112A (en) | 2018-07-27 | 2018-07-27 | A kind of Optimization Scheduling using clothing factory's line flow procedure |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109102112A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109669423A (en) * | 2019-01-07 | 2019-04-23 | 福州大学 | The method that part processes optimal scheduling scheme is obtained based on multiple target grey wolf algorithm is improved |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090234695A1 (en) * | 2007-10-16 | 2009-09-17 | Kapadi Mangesh D | System and method for harvesting scheduling, planting scheduling and capacity expansion |
| CN104504453A (en) * | 2014-11-20 | 2015-04-08 | 昆明理工大学 | Optimal scheduling method for multi-objective optimization military transportation process |
| CN105159242A (en) * | 2015-07-28 | 2015-12-16 | 昆明理工大学 | Optimization scheduling method of textile fabrics dyeing process |
| CN106374534A (en) * | 2016-11-17 | 2017-02-01 | 云南电网有限责任公司玉溪供电局 | Multi-target grey wolf optimization algorithm-based large scale household energy management method |
| CN106845725A (en) * | 2017-02-13 | 2017-06-13 | 广东工业大学 | A kind of engineering parameter optimization method and system |
-
2018
- 2018-07-27 CN CN201810843276.4A patent/CN109102112A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090234695A1 (en) * | 2007-10-16 | 2009-09-17 | Kapadi Mangesh D | System and method for harvesting scheduling, planting scheduling and capacity expansion |
| CN104504453A (en) * | 2014-11-20 | 2015-04-08 | 昆明理工大学 | Optimal scheduling method for multi-objective optimization military transportation process |
| CN105159242A (en) * | 2015-07-28 | 2015-12-16 | 昆明理工大学 | Optimization scheduling method of textile fabrics dyeing process |
| CN106374534A (en) * | 2016-11-17 | 2017-02-01 | 云南电网有限责任公司玉溪供电局 | Multi-target grey wolf optimization algorithm-based large scale household energy management method |
| CN106845725A (en) * | 2017-02-13 | 2017-06-13 | 广东工业大学 | A kind of engineering parameter optimization method and system |
Non-Patent Citations (1)
| Title |
|---|
| 吕新桥 等: "基于灰狼优化算法的置换流水线车间调度", 《武汉理工大学学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109669423A (en) * | 2019-01-07 | 2019-04-23 | 福州大学 | The method that part processes optimal scheduling scheme is obtained based on multiple target grey wolf algorithm is improved |
| CN109669423B (en) * | 2019-01-07 | 2021-08-31 | 福州大学 | Method for obtaining optimal scheduling scheme of part machining based on improved multi-target wolf algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113159383A (en) | Manufacturing resource reconfiguration scheduling method and system for multi-machine cooperation processing workshop | |
| CN103679388B (en) | Production scheduling method and production scheduling system | |
| CN108170530A (en) | A kind of Hadoop Load Balancing Task Scheduling methods based on mixing meta-heuristic algorithm | |
| CN106651139A (en) | Asymmetric multi-workshop integrated dispatching method with consideration of same-kind-of-equipment process | |
| CN108508853A (en) | Based on the method for improving extension moving bottleneck algorithm solution product integrated dispatch problem | |
| Mathirajan et al. | Heuristic algorithms for scheduling heat-treatment furnaces of steel casting industries | |
| CN106327053B (en) | Construction method of weaving process recommendation model based on multi-mode set | |
| CN109102112A (en) | A kind of Optimization Scheduling using clothing factory's line flow procedure | |
| CN105117801B (en) | Tire building based on intelligent algorithm-vulcanization production energy consumption real-time optimization method | |
| CN107844104A (en) | Consider the modeling method of the flexible job shop energy-saving distribution of cycle power strategy | |
| CN106611381A (en) | Algorithm for analyzing influence of material purchase to production scheduling of manufacturing shop based on cloud manufacturing | |
| CN106610655A (en) | Improved particle swarm optimization algorithm for solving job-shop scheduling problem | |
| CN117707083A (en) | Scheduling method, terminal equipment and storage medium for distributed assembly line shop | |
| Schorn et al. | Learning Priority Indices for Energy-Aware Scheduling of Jobs on Batch Processing Machines | |
| CN110059908A (en) | New workpiece weight method for optimizing scheduling based on self-adapted genetic algorithm | |
| Liu et al. | An improved nondominated sorting genetic algorithm-ii for multi-objective flexible job-shop scheduling problem considering worker assignments | |
| CN115293436A (en) | Flexible production scheduling method based on particle swarm optimization model | |
| CN110826909B (en) | Workflow execution method based on rule set | |
| CN108898319A (en) | A kind of Optimization Scheduling of spinning workshop middle ring ingot spinning process production process | |
| CN107730075A (en) | Aeronautical product development process control method based on maturity | |
| CN115496322A (en) | Distributed flow shop scheduling method and device | |
| CN101794417A (en) | Work flow dispatching and business flow modeling method based on sequence number | |
| Chen et al. | Research on scheduling problem of discrete manufacturing workshop for major equipment in complicated environment | |
| Liu et al. | A Multi-Object Genetic Algorithm for the Assembly Line Balance Optimization in Garment Flexible Job Shop Scheduling. | |
| Xu et al. | Flexible Job-Shop Scheduling Based on Improved Firefly Algorithm |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181228 |