CN107392497A - A kind of Job-Shop system and dispatching method based on improved adaptive GA-IAGA - Google Patents
A kind of Job-Shop system and dispatching method based on improved adaptive GA-IAGA Download PDFInfo
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Abstract
The invention discloses a kind of Intelligent Workshop Scheduling System and dispatching method based on improved adaptive GA-IAGA, genetic algorithm crossover operator is adapted to using substantially improving, Job-Shop system operation cost minimum is realized, solves the problems, such as that traditional workshop scheduling time and cost price are high.Partial mapped crossover method is have selected in terms of crossover operation crossover operation is carried out to the gene of individual, both solved the problems, such as, there may be illegal solution, to improve the arithmetic speed of algorithm again, and then reduce the time cost of Job-Shop." Intelligent Workshop Scheduling System " optimal solution strategy is solved using based on FCFP rules improved adaptive GA-IAGA, realizes the selection optimizing decision scheme in one group of non-domination solution of acquisition, solves the problems, such as the problem of multiple optimization solutions in Job-Shop be present.The present invention solves the problems, such as that optimal solution convergence rate is slow and globally optimal solution hunting zone is small, improves the operational efficiency of Intelligent Workshop Scheduling System and shortens the response time.
Description
Technical field
It is especially a kind of to be based on improved adaptive GA-IAGA skill the present invention relates to a kind of film Job-Shop system platform and method
The Job-Shop system platform and method that art and advanced processing rule are combined, belong to and improve intelligent Computation Technology and Workshop
Administrative skill field.
Background technology
With the development of advanced computing technique and going deep into for " intelligence manufacture " theory, China, which proposes, to be helped to promote tradition
Manufacturing industry transition and upgrade and the strategic plan of development, this can promote the leapfrog development of workshop management technology.Made for tradition
For moulding enterprise, if wanting to be produced into the overall competitive strength of original enterprise by improving production efficiency and reduction, then
It is very necessary and very crucial means that production scheduling, which is optimized,.Production scheduling is not only computer integrated manufacturing system
The ring of key one in system, its Production＆Operations Management also to enterprise play the role of very important.To efficient production scheduling
Method and optimized algorithm carry out research and application and are so that enterprise finally wins the market the science guarantee of competition.Production scheduling is asked
Topic is studied, and is in order to reduction production cost more fully using existing resource and by a larger margin, so as to greatly
Strengthen the core competitiveness of enterprise.
Most operation plans in Job-Shop field belong to typical NP-hard problems, and Flow Shop
Scheduling problem (Flow shop scheduling problem, FSP) and job-shop scheduling problem (Job shop
Scheduling problem, JSP) it is relatively common in actual production, while many scholars also compare and are keen to such correlation
The research of problem.Hybrid flow shop scheduling problem is also known as flexible Flow Shop Scheduling, it be one kind combine FSP and
The scheduling problem of parallel machine distribution (Parallel machine scheduling), this has been considerably improved the difficulty of problem solving
Degree, so as to turn into more complicated NP-hard problems.It follows that in actual production process, the manual mode of knowhow is relied on
Scheduling would become hard to try to achieve good scheduling scheme.For now, HFSP (Hybrid Flow shop scheduling
Problem) closer to the produce reality of most flow industry enterprises.Therefore, in-depth study is carried out to HFSP both with important
Theory significance but also with reality engineering application value.
But up to the present, many traditional manufacture enterprises in China are still used using knowhow as leading manual mode
Job-Shop, this kind of method schedule speed is slow, the efficiency of management is low, increase operating cost and time, data can not be carried out it is real-time
The accuracy of data is dispatched in prediction and can not ensureing, and due to the disperseing of data management, be difficult to it is regular by data acquisition
Information, future behaviour is carried out effectively to predict and provide reference.This reduces the high efficiency of Job-Shop management and predictive work(
Energy.
In the Job-Shop system management platform application under based on improved adaptive GA-IAGA environment, some management processes are superfluous
It is remaining and invalid, it is necessary to using appropriate means handled and improved, it is necessary to according to the type of Job-Shop data, scale,
The information such as channel are obtained, back traceability management is carried out to workpiece, part data information.Data processing common in the art is calculated
Method has：Dijkstra's algorithm, genetic algorithm, ant group algorithm, simulated annealing and tabu search algorithm etc., existing workshop are adjusted
There are the following problems for degree system：
Although the 1, the Job-Shop model quantity of research has some scales, because dynamic factor deposits in actual production
And can not with theory idealized state be consistent completely.If the research to problem biases toward determination, static demand
The research of problem, then this will make carried algorithm be confined to Utopian theoretic, and greatly reduce the practical valency of algorithm
Value.
2nd, when problem scale is smaller, current computational methods can be in multiple not ipsilateral Solve problems；But work as workshop
After scheduling problem scale becomes big, respective change also occurs for the state space of solution, ultimately results in existing methods solution efficiency
There is the phenomenon for declining and hanging up.
3rd, the current evaluation criterion disunity for solving Job-Shop problem algorithm, this may be in evaluation algorithms performance
During occur objectivity missing phenomenon.
The content of the invention
To solve above mentioned problem existing for prior art, the present invention will design a kind of workshop based on improved adaptive GA-IAGA and adjust
Degree system and dispatching method, to meet dynamic production requirement, increase the utility function of scheduling；Suitable for larger problem
Dispatching requirement；Generation disclosure satisfy that the unified evaluation criterion system of different scheduling system evaluations.
To achieve these goals, technical scheme is as follows：
A kind of Intelligent Workshop Scheduling System based on improved adaptive GA-IAGA, including subscriber information management module, facility information
Management module, Product Information Management module, sequence information management module, machining information management module and Job-Shop management mould
Block；Described subscriber information management module helps user profile and renewal user profile for inquiry；Described equipment information management
Module is used for inquiry apparatus information and renewal facility information；Described Product Information Management module is used to browse product information and more
New product information；Described sequence information management module is used for preview sequence information and renewal sequence information；Described processing letter
Breath management module is used to inquire about machining information and renewal machining information；Described Job-Shop management module be based on FAM rule and
FCFP rules are scheduled management and setup parameter.
A kind of intelligent workshop dispatching method based on improved adaptive GA-IAGA, comprises the following steps：
Step 1：To current optimal solution generation initializaing variable λi 0, i.e., initial schedule scheme, described current optimal solution are mark
Known standard solution in GA-like Arithmetic；λi 0Represent the Variables Sequence in static production planning and sequencing initially.
Step 2：According to the change of desired value, it then follows FAM rules generate new scheduling scheme, calculate variable of future generation
λi (t+1)；T is iterations, and initial value assigns 0；
λi (t+1)=u λi (t)(1-λi (t)), i=1,2 ..., n
In formula：U represents conversion coefficient, and positioned at section (0,1), n represents the number of variable, corresponding Job-Shop protocol questions
The number of middle solution；Described FAM rules are the earliest available machines used rule of parallel machine.
Step 3:By variable λi (t+1)Be converted to position vectorConversion formula is as follows：
In formula, mi、niTo change constant, value is between 0.5-1.0.
Step 4：Judge whether to meet end condition, i.e., whether converge to current optimal solution；If meeting, optimal is exported
Body simultaneously terminates iteration；Otherwise 5 are gone to step；
Step 5：Calculate the fitness function value of each individual；
Step 6：Under FCFP rules, to present bit by the way of roulette wheel selection and elite retention strategy are combined
Put vector and carry out selection operation；Described FCFP rules are first processing is regular first.
Step 7：Produce random number r1∈ [0,1], and judge r1＜ PcWhether meet, step 8 is performed if meeting, otherwise
Perform step 9；PcCrossover probability is represented, span is 0.2~1.0.
Step 8：Crossover operation is carried out according to selected PMX interior extrapolation methods, generates new individual；
Step 9：Produce a random number r2∈ [0,1], and judge r2＜ PmWhether meet, step 11 performed if meeting,
Otherwise step 10 is performed；PmMutation probability is represented, value is 0.01~0.2.
Step 10：Mutation operation is carried out, generates new individual；
Step 11：Population of new generation is produced, P (t)=P (t+1) is made, iterations t=t+1, goes to step 4.
Some formula occurred in above-mentioned steps are done as described below：In step 2Formula body
Show the iteration optimization thought of genetic algorithm, continuous iteration so that progressively convergence optimal solution, and finally obtain optimal solution.Step
In rapid 3The position vector of formula represents the population of each grey iterative generation, and its purpose is to convenient
Subsequently population (i.e. disaggregation) is carried out the specific genetic manipulation such as selecting.R in step 7 and step 91＜ PcAnd r2＜ PmIt is to use
In the Rule of judgment for judging whether execution crossover operation and mutation operation, and crossover operation and mutation operation are non-in genetic algorithm
The step of Chang Guanjian, material impact often is played to the effect of genetic algorithm Solve problems.
In the description of above-mentioned steps, the thought of hereditary calculation Optimized Iterative is embodied a concentrated reflection of, by primary standard disaggregation
Continue to optimize iteration, progressively convergence optimal solution, finally tries to achieve optimal solution.And Revised genetic algorithum is the set intelligence workshop
The core component of scheduling system, if not using Revised genetic algorithum, then the set system is only just a set of common
Management system.In the Job-Shop management module of the set Intelligent Workshop Scheduling System, the scheduling principle on its backstage is in other words
It is that dispatching method is carried out using Revised genetic algorithum, it is exactly to be carried out according to above-mentioned steps that it, which specifically performs flow,.Therefore
Above-mentioned step and flow is the core component of the set Intelligent Workshop Scheduling System.
Compared with prior art, the invention has the advantages that：
1st, the present invention solves the problems, such as that optimal solution convergence rate is slow and globally optimal solution hunting zone is small, reduces intelligence
The run time and cost of Job-Shop system, improve the operational efficiency of the system and shorten the response time.
2nd, due to the present invention using substantially improve adapt to genetic algorithm crossover operator, realize Job-Shop system operation into
This minimum, solve the problems, such as that traditional workshop scheduling time and cost price are high.
3rd, partial mapped crossover method (PMX) is have selected in terms of crossover operation crossover operation is carried out to the gene of individual, both
Can solve the problems, such as there may be illegal solution, the arithmetic speed of and can raising algorithm, so reduce time of Job-Shop into
This.
4th, because the present invention is using based on FCFP rules improved adaptive GA-IAGA solution " Intelligent Workshop Scheduling System " optimal solution
Strategy, the selection optimizing decision scheme in one group of non-domination solution of acquisition is realized, solved the problems, such as in Job-Shop in the presence of more
The problem of individual optimization solution.
5th, the present invention is on the basis to HFSP founding mathematical models, it is proposed that one kind is by genetic algorithm with first adding first
Work rule (First Come First Process, FCFP) and earliest available machines used (First Available Machine,
FAM) the innovatory algorithm that is combined of strategy, and for low, easy local convergence of traditional genetic algorithm solution efficiency that may be present etc.
Deficiency, basic operation and relevant parameter to genetic algorithm have also carried out being correspondingly improved design.Reach fast convergence rate, can be most
Smallization run time and operating cost, data progress processing in real time can be predicted, degree of accuracy height and height can be provided the user
The effect of efficiency.HFSP is solved with emulation experiment, achieves good experimental result, is calculated so as to strongly suggest
The validity and reliability of method.
Brief description of the drawings
Fig. 1 is the system composition figure of the present invention.
Fig. 2 is flow chart of the method for the present invention.
Embodiment
The present invention is further described through below in conjunction with the accompanying drawings.
Technical scheme is based on following Rulemaking：
First, based under the earliest available machines used of parallel machine (First Available Machine, FAM) allocation strategy first
First process the scheduling scheme of regular (First Come First Process, FCFP)
The present invention use based on FAM strategy and FCFP rule scheduling scheme in, processing of the workpiece in each procedure
Order keeps constant, i.e., no FCFP rules, but then uses earliest available machines used for the parallel machine on same procedure
(FAM) allocation strategy.Chromosome is [54123] as known to above-mentioned assumed condition, i.e. the processing sequence of workpiece is：5-->4-->
1-->2-->3, so the order that workpiece is processed on 2 procedures is all：5-->4-->1-->2-->3.Sequence to workpiece is adopted
Carried out with FCFP rules, FAM allocation strategy is then taken in the parallel machine distribution on same procedure；To avoid the production illegally solved
Raw, crossover operator uses partial mapped crossover method (PMX)；Accelerate convergence rate while in order to be effectively retained excellent individual,
Selection strategy is using the elite retention strategy for retaining 10% optimal parent individuality.
2nd, the scheduling scheme of strategy is randomly assigned based on the lower parallel machine of FCFP rules
In this scheduling scheme, in addition to the first procedure, processing sequence of the workpiece on each procedure is advised according to FCFP
Then it is ranked up, but for the parallel machine distribution on same procedure then using the strategy being randomly assigned.By above-mentioned hypothesis bar
Chromosome known to part is [54123], i.e. workpiece is in the processing sequence of the first procedure：5-->4-->1-->2-->3.In this base
Sequence on plinth to workpiece is carried out using FCFP rules, and FAM distribution plan is then taken in the parallel machine distribution on same procedure
Slightly；To avoid the generation illegally solved, crossover operator uses PMX methods；Accelerate to receive while in order to be effectively retained excellent individual
Speed is held back, selection strategy is using the elite retention strategy for retaining 10% optimal parent individuality.
Embodiments of the invention are as follows：
A kind of Job-Shop system based on improved adaptive GA-IAGA, including enterprise's static state initial scheme and dynamic weight dispatching party
Case management, the selection of dynamic weight scheduling scheme and elite screening strategy solve optimal solution convergence rate and searched slowly with globally optimal solution
The problem of rope scope is small；The workshop scheduling management information system for implementing algorithm is as shown in Figure 1.In subscriber information management
The essential information of user can be updated and be set as needed different authorities in module.Equipment information management module bag
The information of all devices in process, such as the parameter such as device numbering, device name, unit type, equipment state have been included,
Except facility information can be checked in the module, moreover it is possible to which it is updated.Managed in Product Information Management module such as product
The essential information of all over products such as numbering, name of product, product type, stockpile number, inventory area, the module, which possesses, browses production
The function of product information and upgrading products information.All sequence informations of the said firm, such as order are listed in order management module
Numbering, name of product, quantity on order, orderer, lower single time, time of delivery etc., it can be protected in real time by the management to order
Demonstrate,prove its validity and accuracy.Machining information mainly include processing numbering, processed product numbering, process title, process number,
Process equipment title, process time etc., the machining information progress additions and deletions of different product can be changed in machining information management module
Look into operation.The parameter of algorithm can be set in Job-Shop management module, for example, population scale, maximum iteration,
Crossover probability, mutation probability etc., by introducing the computing of innovatory algorithm, obtain optimal production scheduling scheme, specific steps ginseng
See Fig. 2.
The method of the present invention can also use embedded chip, the software module of computing device, or the combination of the two
Implement.Software module can be placed in random access memory (RAM), internal memory, read-only storage (ROM), electrically programmable ROM, electrically erasable
Except any other form of well known in programming ROM, register, hard disk, moveable magnetic disc, CD-ROM or technical field
In storage medium.
Above-described embodiment, the purpose of the present invention, technical scheme and beneficial effect are carried out further
Describe in detail, should be understood that the embodiment that the foregoing is only the present invention, the guarantor being not intended to limit the present invention
Scope is protected, within the spirit and principles of the invention, any modification, equivalent substitution and improvements done etc., should be included in this
The protection domain of invention.
Claims (2)
- A kind of 1. Intelligent Workshop Scheduling System based on improved adaptive GA-IAGA, it is characterised in that：Including subscriber information management module, Equipment information management module, Product Information Management module, sequence information management module, machining information management module and workshop are adjusted Spend management module；Described subscriber information management module helps user profile and renewal user profile for inquiry；Described equipment Information management module is used for inquiry apparatus information and renewal facility information；Described Product Information Management module is used to browse product Information and upgrading products information；Described sequence information management module is used for preview sequence information and renewal sequence information；It is described Machining information management module be used for inquire about machining information and renewal machining information；Described Job-Shop management module is based on FAM rules and FCFP rules are scheduled management and setup parameter.
- A kind of 2. intelligent workshop dispatching method based on improved adaptive GA-IAGA, it is characterised in that：Comprise the following steps：Step 1：Initializaing variable is generated to current optimal solutionThat is initial schedule scheme, described current optimal solution are lost for standard Known standard solution in propagation algorithm；Represent the Variables Sequence in static production planning and sequencing initially；Step 2：According to the change of desired value, it then follows FAM rules generate new scheduling scheme, calculate variable of future generationT is Iterations, initial value assign 0；<mrow> <msubsup> <mi>&lambda;</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <mi>u</mi> <mo>&CenterDot;</mo> <msubsup> <mi>&lambda;</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <msubsup> <mi>&lambda;</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msubsup> <mo>)</mo> </mrow> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>n</mi> </mrow>In formula：U represents conversion coefficient, and positioned at section (0,1), n represents the number of variable, is solved in corresponding Job-Shop protocol questions Number；Described FAM rules are the earliest available machines used rule of parallel machine；Step 3:By variableBe converted to position vectorConversion formula is as follows：<mrow> <msubsup> <mi>x</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msub> <mi>m</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>n</mi> <mi>i</mi> </msub> <mo>&CenterDot;</mo> <msubsup> <mi>&lambda;</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>n</mi> </mrow>In formula, mi、niTo change constant, value is between 0.5-1.0；Step 4：Judge whether to meet end condition, i.e., whether converge to current optimal solution；If meeting, output optimum individual is simultaneously Terminate iteration；Otherwise 5 are gone to step；Step 5：Calculate the fitness function value of each individual；Step 6：Under FCFP rules, by the way of roulette wheel selection and elite retention strategy are combined to current location to Amount carries out selection operation；Described FCFP rules are first processing is regular first；Step 7：Produce random number r1∈ [0,1], and judge r1＜ PcWhether meet, step 8 is performed if meeting, is otherwise performed Step 9；PcCrossover probability is represented, span is 0.2~1.0；Step 8：Crossover operation is carried out according to selected PMX interior extrapolation methods, generates new individual；Step 9：Produce a random number r2∈ [0,1], and judge r2＜ PmWhether meet, step 11 is performed if meeting, otherwise Perform step 10；PmMutation probability is represented, value is 0.01~0.2；Step 10：Mutation operation is carried out, generates new individual；Step 11：Population of new generation is produced, P (t)=P (t+1) is made, iterations t=t+1, goes to step 4.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108959783A (en) * | 2018-07-09 | 2018-12-07 | 广东工业大学 | A kind of layout simulation optimization method and device in intelligence workshop |
| CN109543921A (en) * | 2018-12-11 | 2019-03-29 | 合肥工业大学 | The production scheduled production method of oil pipes Flow Shop based on improved adaptive GA-IAGA |
| CN109685259A (en) * | 2018-12-12 | 2019-04-26 | 聊城大学 | Hybrid flow based on multi-modal characteristic perception dispatches evolution optimization method |
| CN111144710A (en) * | 2019-12-06 | 2020-05-12 | 重庆大学 | Construction and dynamic scheduling method of sustainable hybrid flow shop |
| CN111369036A (en) * | 2020-02-18 | 2020-07-03 | 吉林师范大学 | Comprehensive scheduling method based on Dijkstra algorithm |
| CN112966887A (en) * | 2019-12-13 | 2021-06-15 | 多点(深圳)数字科技有限公司 | Method, apparatus, electronic device, and medium for generating allocation information |
| CN113240176A (en) * | 2021-05-12 | 2021-08-10 | 西北工业大学 | Intelligent scheduling method for unit type assembly workshop based on limited personnel instant station |
| CN113326917A (en) * | 2021-04-29 | 2021-08-31 | 开放智能机器（上海）有限公司 | Method and system for automatically optimizing operator based on genetic algorithm |
| CN113515879A (en) * | 2021-09-14 | 2021-10-19 | 南京华清智能科技有限公司 | Method for solving distributed multi-factory production scheduling by improved particle swarm algorithm |
| CN113960964A (en) * | 2021-09-22 | 2022-01-21 | 哈尔滨工业大学 | Flexible flow shop production scheduling system based on simulation optimization |
| CN114444239A (en) * | 2022-01-27 | 2022-05-06 | 湘南学院 | Operation workshop movement track path guiding optimization method based on hybrid genetic algorithm |
| CN113960964B (en) * | 2021-09-22 | 2024-06-28 | 哈尔滨工业大学 | Flexible flow shop production scheduling system based on simulation optimization |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102663574A (en) * | 2012-03-23 | 2012-09-12 | 合肥工业大学 | Multi-RGV dynamic scheduling method based on genetic algorithm |
| CN103592920A (en) * | 2013-11-19 | 2014-02-19 | 天津工业大学 | Hybrid flow shop scheduling method with finite buffers |
| CN106681334A (en) * | 2017-03-13 | 2017-05-17 | 东莞市迪文数字技术有限公司 | Automatic-guided-vehicle dispatching control method based on genetic algorithm |
-
2017
- 2017-08-08 CN CN201710672208.1A patent/CN107392497A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102663574A (en) * | 2012-03-23 | 2012-09-12 | 合肥工业大学 | Multi-RGV dynamic scheduling method based on genetic algorithm |
| CN103592920A (en) * | 2013-11-19 | 2014-02-19 | 天津工业大学 | Hybrid flow shop scheduling method with finite buffers |
| CN106681334A (en) * | 2017-03-13 | 2017-05-17 | 东莞市迪文数字技术有限公司 | Automatic-guided-vehicle dispatching control method based on genetic algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 苗峰 等: ""多阶段可替换分组并行机调度的串行遗传算法"", 《现代制造工程》 * |
| 陈振同: ""基于改进遗传算法的车间调度问题研究与应用"", 《中国优秀硕士学位论文全文数据库（工程科技II辑）》 * |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108959783B (en) * | 2018-07-09 | 2019-05-24 | 广东工业大学 | A kind of layout simulation optimization method and device in intelligence workshop |
| CN108959783A (en) * | 2018-07-09 | 2018-12-07 | 广东工业大学 | A kind of layout simulation optimization method and device in intelligence workshop |
| CN109543921B (en) * | 2018-12-11 | 2021-04-06 | 合肥工业大学 | Production scheduling method of special petroleum pipe flow shop based on improved genetic algorithm |
| CN109543921A (en) * | 2018-12-11 | 2019-03-29 | 合肥工业大学 | The production scheduled production method of oil pipes Flow Shop based on improved adaptive GA-IAGA |
| CN109685259A (en) * | 2018-12-12 | 2019-04-26 | 聊城大学 | Hybrid flow based on multi-modal characteristic perception dispatches evolution optimization method |
| CN111144710A (en) * | 2019-12-06 | 2020-05-12 | 重庆大学 | Construction and dynamic scheduling method of sustainable hybrid flow shop |
| CN111144710B (en) * | 2019-12-06 | 2023-04-07 | 重庆大学 | Construction and dynamic scheduling method of sustainable hybrid flow shop |
| CN112966887A (en) * | 2019-12-13 | 2021-06-15 | 多点(深圳)数字科技有限公司 | Method, apparatus, electronic device, and medium for generating allocation information |
| CN112966887B (en) * | 2019-12-13 | 2024-05-28 | 多点(深圳)数字科技有限公司 | Method, device, electronic equipment and medium for generating distribution information |
| CN111369036B (en) * | 2020-02-18 | 2020-11-24 | 吉林师范大学 | Comprehensive scheduling method based on Dijkstra algorithm |
| CN111369036A (en) * | 2020-02-18 | 2020-07-03 | 吉林师范大学 | Comprehensive scheduling method based on Dijkstra algorithm |
| CN113326917A (en) * | 2021-04-29 | 2021-08-31 | 开放智能机器（上海）有限公司 | Method and system for automatically optimizing operator based on genetic algorithm |
| CN113240176A (en) * | 2021-05-12 | 2021-08-10 | 西北工业大学 | Intelligent scheduling method for unit type assembly workshop based on limited personnel instant station |
| CN113240176B (en) * | 2021-05-12 | 2023-06-20 | 西北工业大学 | Unit type assembly workshop intelligent scheduling method based on limited personnel instant station |
| CN113515879A (en) * | 2021-09-14 | 2021-10-19 | 南京华清智能科技有限公司 | Method for solving distributed multi-factory production scheduling by improved particle swarm algorithm |
| CN113960964A (en) * | 2021-09-22 | 2022-01-21 | 哈尔滨工业大学 | Flexible flow shop production scheduling system based on simulation optimization |
| CN113960964B (en) * | 2021-09-22 | 2024-06-28 | 哈尔滨工业大学 | Flexible flow shop production scheduling system based on simulation optimization |
| CN114444239A (en) * | 2022-01-27 | 2022-05-06 | 湘南学院 | Operation workshop movement track path guiding optimization method based on hybrid genetic algorithm |
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