CN113609759A - Reconfigurable flexible assembly line layout method and device - Google Patents

Reconfigurable flexible assembly line layout method and device Download PDF

Info

Publication number
CN113609759A
CN113609759A CN202110813304.XA CN202110813304A CN113609759A CN 113609759 A CN113609759 A CN 113609759A CN 202110813304 A CN202110813304 A CN 202110813304A CN 113609759 A CN113609759 A CN 113609759A
Authority
CN
China
Prior art keywords
assembly
reconfigurable flexible
flexible assembly
tool
reconfigurable
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
Application number
CN202110813304.XA
Other languages
Chinese (zh)
Other versions
CN113609759B (en
Inventor
曾龙
史丰源
王硕
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen International Graduate School of Tsinghua University
Original Assignee
Shenzhen International Graduate School of Tsinghua University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Shenzhen International Graduate School of Tsinghua University filed Critical Shenzhen International Graduate School of Tsinghua University
Priority to CN202110813304.XA priority Critical patent/CN113609759B/en
Publication of CN113609759A publication Critical patent/CN113609759A/en
Application granted granted Critical
Publication of CN113609759B publication Critical patent/CN113609759B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling
    • 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

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Economics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Strategic Management (AREA)
  • Evolutionary Computation (AREA)
  • Biophysics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Health & Medical Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Software Systems (AREA)
  • Evolutionary Biology (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Geometry (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Computer Hardware Design (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Development Economics (AREA)
  • Data Mining & Analysis (AREA)
  • Educational Administration (AREA)
  • Molecular Biology (AREA)
  • Game Theory and Decision Science (AREA)
  • Computational Linguistics (AREA)
  • Medical Informatics (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Genetics & Genomics (AREA)
  • Physiology (AREA)

Abstract

The invention discloses a reconfigurable flexible assembly line layout method and a reconfigurable flexible assembly line layout device, wherein the method comprises the following steps: s1, designing a reconfigurable flexible assembly line which takes the reconfigurable flexible assembly unit as a core component and can be rapidly reconfigured; s2, providing corresponding problem hypothesis and a mathematical model of multiple optimization targets according to the reconfigurable flexible assembly line; and S3, solving the mathematical model by using a pre-optimization operator and a double-population genetic algorithm to obtain a layout of the reconfigurable flexible assembly line. The invention can reconstruct the assembling tool, the assembling clamp and the like of each assembling unit according to different assembling processes of products so as to realize the rapid reconstruction of the assembling environment.

Description

Reconfigurable flexible assembly line layout method and device
Technical Field
The invention relates to the field of automatic assembly, in particular to a reconfigurable flexible assembly line layout method and a reconfigurable flexible assembly line layout device.
Background
The existing rigid or most automatic production equipment is generally a special assembly line and is suitable for products with large yield and stable demand. Under the market environment that products are increasingly personalized and the iteration speed is increased, the production mode is gradually changed into various types and small batches, and in the face of the assembly requirements of different types of products, the special assembly line has the defects of high modification difficulty, high cost, long time and the like. In order to adapt to various-small-batch production modes, an assembly line can be automatically and rapidly reconfigured according to different product assembly processes, and the flexibility of the assembly line is improved.
The reconfigurable flexible assembly line is characterized in that the characteristics of a flexible manufacturing system FMS and a reconfigurable manufacturing system RMS are used for reference, the aim is to obtain a special assembly line suitable for the product through rapid reconfiguration according to different assembly processes of the assembled product, and the reconfigurable flexible assembly line has the advantages of flexibility, flexibility and low cost. Patent CN211672695U provides a flexible shoemaking production line of modularization restructurable, and every station all can accomplish the participation that does not need the people automatically, can also form unified production line with independent production cell simultaneously, improves the reusability of production facility and improves work efficiency greatly. Patent CN109894929A provides a modular reconfigurable flexible production method and system, mainly comprising: the method comprises four steps of order scheduling, route planning, reconstruction and processing, and can reconstruct the whole production system in real time according to order information to obtain a new configuration so as to meet the production requirement of flexible customization. Patent CN105252180A discloses a reconfigurable automatic flexible welding production platform and an operation method thereof, which can redesign the production capacity according to the high flexibility requirement of automatic welding production of various small assembly parts, and realize the coordination of production task and production capacity.
It follows that reconfigurable flexible manufacturing techniques have gained widespread interest and application in the manufacturing field, but there has been less research and patent in the product assembly field. The team has designed a reconfigurable flexible assembly unit (patent CN110238621B), and the most obvious feature of the reconfigurable flexible assembly unit is that each unit is completely equivalent from the perspective of physical carrier or functional implementation, so that the reconfiguration of the assembly environment can be completed quickly by reconfiguring the assembly tool, the assembly fixture and the like of each assembly unit according to different assembly processes of the product. In order to achieve the reconstruction of the assembly environment quickly, the important technical problems of how to calculate the number of the assembly units, reasonably distribute the assembly procedures and arrange the assembly units according to the assembly process of a given product and achieve the maximum equipment balance rate need to be solved.
Disclosure of Invention
The invention aims to provide a reconfigurable flexible assembly line layout method and a reconfigurable flexible assembly line layout device, which can be used for reconfiguring an assembly tool, an assembly fixture and the like of each assembly unit according to different assembly processes of products so as to realize rapid reconfiguration of an assembly environment.
In order to realize the purpose, the invention adopts the following technical scheme:
a reconfigurable flexible assembly line layout method is characterized by comprising the following steps:
s1, designing a reconfigurable flexible assembly line with the reconfigurable flexible assembly unit as a core component;
s2, providing corresponding problem hypothesis and a mathematical model of multiple optimization targets according to the reconfigurable flexible assembly line;
and S3, solving the mathematical model by using a pre-optimization operator and a double-population genetic algorithm to obtain a layout of the reconfigurable flexible assembly line.
Further:
in step S1, the reconfigurable flexible assembly unit includes a robot, an assembly tool and a tool rack, an assembly fixture and a fixture base, and a tray; the robot is located at the center of the workbench, and the assembling tool, the tool rack, the assembling fixture, the fixture base and the tray are annularly distributed on the workbench.
Further:
the robot is used for completing assembly actions on the reconfigurable flexible assembly unit;
in the assembly tool and the tool rack, the assembly tool is stored on the tool rack, and the assembly tool can be configured at the tail end of the robot and used for assembling parts;
in the assembly fixture and the fixture base, the assembly fixture is arranged on the fixture base and used for fixing and positioning the part;
the assembly tool and the assembly fixture are matched with assembly tasks to be completed on the reconfigurable flexible assembly unit; the tray is used to hold parts needed for assembly conveyed by the peripheral material handling system.
Further:
the work process of the reconfigurable flexible assembly line comprises the following steps: before assembly, firstly determining the number of the reconfigurable flexible assembly units, determining the required assembly tools and the required assembly fixtures according to the assembly process of a product, then respectively conveying the required assembly tools and the required assembly fixtures to the reconfigurable flexible assembly units from an assembly tool library and an assembly fixture library by an AGV trolley, respectively configuring the reconfigurable flexible assembly units on a tool rack and a fixture base, and placing parts in trays on a workbench by a material conveying system; when assembling is started, the robot takes out the parts from the inlet tray, places the parts in a clamp for clamping and assembling, and then carries out the next assembling process; and after all assembly procedures on the reconfigurable flexible assembly units are completed, moving the semi-finished product to an outlet tray, and moving the semi-finished product to an inlet tray of the next reconfigurable flexible assembly unit by a transfer robot to sequentially complete the whole assembly process.
Further:
the problem assumption in step S2 includes:
s2-1, all the procedures are minimum operation elements and can not be re-separated;
s2-2, each assembly process can be distributed to any workstation, but the same assembly process cannot be distributed to a plurality of different workstations;
s2-3, the priority relation among the processes is known and fixed and is determined by a priority relation graph, so that the assembly processes distributed to the workstations are strictly in accordance with the priority constraint relation;
s2-4, the working procedure time is fixed and invariable and is determined according to the actual situation;
s2-5, neglecting the loading and unloading time and the robot starting time;
s2-6, neglecting the clamp replacing time and the transferring time of the transferring robot.
Further:
the optimization targets of the mathematical model in the step S3 include a tact CT, a tool change time RT, and a table cost ST;
min w1*CT+w2*RT+w3*ST
wherein, w1、w2、w3Respectively, the weights of the respective considerations.
Further:
the mathematical model of the tact CT in step S2 is:
Figure BDA0003169257640000041
the constraint conditions are:
Figure BDA0003169257640000042
Figure BDA0003169257640000043
Figure BDA0003169257640000044
Figure BDA0003169257640000045
wherein, CT is the production beat; i, j is the number of the assembly process, i, j is 1,2, …, n; k is the workstation serial number, k is 1,2, …, m; n is the total number of assembly processes; m is the total number of the workstations, namely the total number of the robots; t is tiIs the process i time; pre (i) is a direct pre-process set for task i; decision variable xikIs composed of
Figure BDA0003169257640000046
The mathematical model of the tool change time RT is:
Figure BDA0003169257640000047
the constraint conditions are:
Figure BDA0003169257640000048
Figure BDA0003169257640000049
wherein RT is the assembly tool change time; RTI is the time required to change a tool; the decision variable Tool (i, j) is
Figure BDA0003169257640000051
The decision variable P (i, j) is
Figure BDA0003169257640000052
The mathematical model of the table cost ST is:
ST＝STI·(n+S)
wherein ST is the total cost of the reconfigurable flexible assembly unit; STI is the cost of one of the reconfigurable flexible assembly cells; and S is the increased number of the reconfigurable flexible assembly units.
Further:
in step S3, the pre-optimization operator "splits" the assembly process with a longer assembly time, so as to reduce the assembly time of the process, thereby calculating the number of the reconfigurable flexible assembly units that can be additionally added in one workstation.
Further:
the main implementation process of the double population genetic algorithm in the step S3 includes:
s3-1, coding: the chromosome is coded by adopting an operation element sequential coding mode, operation elements are arranged in a line according to the priority relation of the operation elements, and each operation element corresponds to a gene position to form an individual;
s3-2, decoding: on the premise of meeting the process priority relationship, sequentially distributing the processes to the workstations;
s3-3, population initialization: initializing a population by adopting a random method;
s3-4, fitness function: namely a multi-objective optimization function;
s3-5, selection, crossover and mutation operators: through the comparison of fitness functions, excellent individuals are selected, and the advantages and disadvantages are realized.
The invention also provides a reconfigurable flexible assembly line layout device, and the reconfigurable flexible assembly line layout is obtained by using the method.
The invention has the following beneficial effects:
1. the reconfigurable flexible assembly line layout device provided by the invention can be applied to actual production and is suitable for assembly in a small-batch multi-variety production mode;
2. the layout method of the reconfigurable flexible assembly line can make reasonable assumptions according to the characteristics of the assembly line and provide a mathematical model, thereby laying a foundation for further research in the field;
3. the reconfigurable flexible assembly line layout method provided by the invention adopts a preposed optimization operator and an improved double-population genetic algorithm to solve a mathematical model, and further obtains a more optimized feasible solution.
Drawings
FIG. 1 is a schematic view of a reconfigurable flexible mounting unit of an embodiment of the present invention;
FIG. 2 is a chart of priorities obtained from an actual assembly process in accordance with an embodiment of the present invention;
FIG. 3 is a priority relationship diagram after splitting a critical process in an embodiment of the present invention;
FIG. 4 is a process map according to an embodiment of the present invention;
FIG. 5 is a logical layout diagram of a workstation according to an embodiment of the present invention;
FIG. 6 is a physical layout diagram of a workstation according to an embodiment of the present invention.
Detailed Description
The invention is further described in the following by means of specific embodiments in conjunction with the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
It should be noted that the directions or positional relationships indicated by the left, right, upper, lower, top, bottom, etc. in the present embodiment are based on the directions or positional relationships shown in the drawings, and are only for convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific direction, be constructed in a specific direction and operate, and thus, should not be construed as limiting the present invention.
The embodiment of the invention provides a layout method of a reconfigurable flexible assembly line, which mainly comprises the following three parts:
firstly, designing a reconfigurable flexible assembly line with rapid reconfiguration capability
The main content of the subsection is to design a reconfigurable flexible assembly line with rapid reconfiguration capability, the core component of the reconfigurable flexible assembly line is a reconfigurable flexible assembly unit, and the physical carrier is also called a workbench. The physical diagram of the reconfigurable flexible assembly unit is shown in the attached figure 1 and mainly comprises the following four parts:
(1) the robot 101: the reconfigurable flexible assembly unit is mainly responsible for completing assembly actions on the reconfigurable flexible assembly unit.
(2) Replaceable assembly tool and tool rack 102: an assembly tool is arranged at the end of the robot 101 for assembling the parts. When the assembled parts are different, the assembling can be realized by replacing different assembling tools. The assembly tools on the unit are temporarily stored on a tool rack.
(3) Replaceable assembly jig and jig base 103: the assembly fixture is arranged on the fixture base and used for fixing and positioning the part. When different kinds of parts are assembled, different assembling jigs can be replaced.
(4) The tray 104: for holding parts required for assembly, which are transferred to the tray 104 by the peripheral material transport system.
Based on the quick replacement device, different types of assembly tools can be installed at the tail end of the robot 101, and different types of assembly fixtures can be configured on the fixture base, so that various assembly actions can be completed. The assembly tools and fixtures are matched to the assembly tasks that need to be completed on the assembly unit, while the pallet 104 is responsible for the loading of the parts.
The assembly line includes, in addition to the assembly unit, an assembly tool magazine, an assembly jig magazine, a transfer robot, and an AGV. The assembly tool library is used for storing all kinds of assembly tools, the assembly fixture library is used for storing all kinds of assembly fixtures, and the transfer robot is used for transferring semi-finished products among all the assembly units. AGV (automated Guided vehicle) is an unmanned transport vehicle, also called an automated Guided vehicle, which is used to retrieve and place assembly tools and assembly jigs from and into tool and jig libraries, respectively.
The process for a reconfigurable flexible assembly line is described as follows:
the main working process of the reconfigurable flexible assembly line comprises the following steps: the reconfigurable assembly unit takes a 6-freedom assembly robot as a center, and the workbench is in an annular layout. Before assembly, determining the number of assembly units, and determining required assembly tools and assembly clamps according to the assembly process of a product; then the AGV trolley respectively conveys the required assembling tools and assembling fixtures from the assembling tool library and the assembling fixture library to each assembling unit, the assembling tools and the assembling fixtures are respectively arranged on the tool rack and the fixture base, and the parts are placed in the trays on the workbench through the material conveying system until the assembling preparation work is finished. At the start of assembling, the assembling robot takes out the parts from the inlet tray, places them in the jig, clamps them, and assembles them, followed by the next assembling process. And after all the assembly procedures on the assembly units are completed, the semi-finished products are moved to an outlet tray and moved to an inlet tray of the next assembly unit by a transfer robot, and the whole assembly process is completed in sequence.
Secondly, according to the characteristics of the assembly line, the corresponding assembly line problem hypothesis and the mathematical model of multiple optimization targets are provided
The main content of the subsection is to provide a mathematical model which accords with the characteristics of a second type robot assembly line on the basis of a balance model of the assembly line according to a reconfigurable flexible assembly line. The optimization targets of the mathematical model comprise three parts of production cycle CT, tool replacement time RT and workbench cost ST. Considering that factors influencing the actual production efficiency of an assembly line are not only production tact, but also other factors, such as the fact that a plurality of assembly processes can be distributed in the same workstation, tool replacement time caused by different assembly tools required for assembly is not negligible, and the problem of cost of assembly units (workstations) caused by the existence of more than one assembly unit in one workstation.
The problem assumptions and mathematical models for a reconfigurable flexible assembly line are specified below:
the assembly line balance problem is to solve the distribution problem of each operation element on the assembly workstation, needs to realize better balance requirement, meets certain constraint condition, and realizes the high-efficiency and low-cost operation of the assembly operation. In view of the aforementioned characteristics of the assembly unit, for a more appropriate description of the research problem, the following assumptions are made:
(1) all procedures are the smallest operational element and are not separable;
(2) each assembly process can be assigned to any workstation, but the same assembly process cannot be assigned to a plurality of different workstations;
(3) the priority relationship between the processes is known and fixed and is determined by a priority relationship diagram, so that the assembly processes distributed to the workstations also strictly conform to the priority constraint relationship;
(4) the working procedure time is fixed and invariable and is determined according to the actual situation;
(5) the material loading and unloading time and the starting time of the robot are ignored;
(6) the clamp replacement time and the transfer time of the transfer robot are ignored.
The typical second type of robotic assembly line balancing problem is to minimize production tact, i.e., minimize the maximum assembly time for all stations, given the number of stations, to make the assembly times between stations not very different, and to improve assembly line balance. The optimization method is improved on the basis of the balance problem of the second type of robot, and a multi-objective optimization problem comprising production cycle CT, tool replacement time RT and workbench cost ST is considered.
min w1*CT+w2*RT+w3*ST
Wherein, w1、w2、w3Respectively, the weights of the respective considerations.
Mathematical model of CT:
Figure BDA0003169257640000091
the constraint conditions are:
Figure BDA0003169257640000092
Figure BDA0003169257640000093
Figure BDA0003169257640000094
Figure BDA0003169257640000095
wherein, CT is the production beat; i, j is the number of the assembly process, i, j is 1,2, …, n; k is the workstation serial number, k is 1,2, …, m; n is the total number of assembly processes; m is the total number of the workstations, namely the total number of the robots; t is tiIs the process i time; pre (i) is a direct pre-process set for task i;decision variable xikIs composed of
Figure BDA0003169257640000096
Mathematical model of RT:
Figure BDA0003169257640000097
the constraint conditions are:
Figure BDA0003169257640000098
Figure BDA0003169257640000101
wherein RT is the assembly tool change time; RTI is the time required to change a tool; the decision variable Tool (i, j) is
Figure BDA0003169257640000102
The decision variable P (i, j) is
Figure BDA0003169257640000103
The mathematical model of ST is:
ST＝STI·(n+S)
wherein ST is the total cost of the assembly unit; STI is the cost of one assembly unit; s is the increased number of assembly units.
Thirdly, solving the model by using a preposed optimization operator and a double-population genetic algorithm to obtain the layout of the assembly line
And solving the mathematical model by using a pre-optimization operator and a double-population genetic algorithm. The priority relation graph of the assembly processes of the product determines the priority among the product processes, and the process i with longer assembly time can be divided into two same processes i 'and i', so that the assembly time is halved, and the assembly efficiency is improved. The population genetic algorithm is a random optimization search algorithm which is carried out by simulating and comparing the evolution process of organisms as a classical heuristic method.
The following specific description about the solution using the pre-optimization operator and the double population genetic algorithm is as follows:
due to the particularity of the reconfigurable flexible assembly line, a plurality of assembly units can be arranged in one workstation, the number of the additionally increased assembly units is not generated randomly but calculated by a preposed optimization operator, the operator can help the assembly process with larger assembly time to be split, and the assembly units with the same number as the split process are added in the workstation, so that the assembly time of the process is reduced.
An example test of mathematical modeling and solving is carried out by selecting a typical hydraulic valve product of a certain company, and a priority relation graph obtained according to an actual assembly process is shown in figure 2. The whole assembly process comprises 12 processes in total, the numbers next to the process serial numbers represent the process time, and the broken line boxes represent the processes with larger process time.
And then, pre-optimization is carried out, namely, the key process with larger process time is split, namely, the process 2 and the process 9 in the figure, the process time of the key process is respectively 24 and 20, and the priority relationship graph after splitting is shown as the figure 3. As shown in the priority relationship diagram after the splitting, the numbers beside the broken line boxes represent new assembly time, the key processes 2 and 9 become the processes 2 ', 2' and 9 ', 9', respectively, the process time becomes half of the original process time, namely the process time of the processes 2 ', 2' is 12, and the process time of the processes 9 ', 9' is 10.
Then solving using a double population genetic algorithm. The common population algorithm is simple and easy to implement, the searching speed is high, but the searching space is limited, the problem is easily trapped in the local optimal predicament, and the dual-population genetic algorithm can better solve the problem.
The main implementation process of the algorithm comprises the following steps:
(1) and (3) encoding: the chromosome is coded by adopting a sequential coding mode of the operation elements, the operation elements are arranged in a line according to the priority relationship, and each operation element corresponds to a gene position to form an individual.
(2) And (3) decoding: and on the premise of meeting the process priority relation, sequentially distributing the processes to the workstations.
(3) Population initialization: a random approach is used to initialize the population.
(4) Fitness function: i.e. a multi-objective optimization function.
(5) Selection, crossover and mutation operators: through the comparison of fitness functions, excellent individuals are selected, and the advantages and disadvantages are realized.
Compared with the prior art, the embodiment has the following advantages:
1. the existing assembly line is generally a non-fully automatic assembly line or an assembly line with a low degree of flexibility, and the embodiment provides a reconfigurable flexible assembly line.
2. The existing mathematical model is not in accordance with the characteristics of the assembly line and has limited practical application, and the embodiment provides a problem hypothesis and a mathematical model which are in accordance with the characteristics of a reconfigurable flexible assembly line.
3. The feasible solution obtained by the existing model solving method is not optimal enough and can still be optimized continuously, and the mathematical model is solved through a preposed optimization operator and an improved double-population genetic algorithm.
Finally, we tested the above method by way of example, and the results are shown below.
A dual-population genetic algorithm is written by using matlab language, a priority relation graph is input, the number of assembly workstations is set to be 5, the types of tools required by each process are set, and an assembly tool table required by each process is shown in table 1.
TABLE 1
Kind of assembly tool Sequence number of process
1 1、4、8
2 3、5、9、10、11
3 2、6、7
4 12
The process distribution diagram obtained by solving the double population genetic algorithm is shown in the attached figure 4. The abscissa represents the serial number of the assembly station and the ordinate represents the total time of the station, each small rectangle in the figure represents a process, of the two numbers in the small rectangle, the left number represents the serial number of the process and the right number represents the assembly time of the process. Taking the first workstation as an example, indicating that the process 8 and the process 10 are allocated to the workstation 1, the total time of the workstations 1-5 can be calculated as: 20. 15, 27, 29, 27, so the tact CT is calculated to be 29; similarly, the number of assembly tool changes is 4, so RT — RTI 4; the number of work stations is 5, the number of required main work tables is 5, the number of splitting processes is two, the number of required sub work tables is 2, and therefore the total number of required assembly units is 7, and ST is STI × 7.
Two indexes for measuring the balance degree of the assembly line are given: a smoothing coefficient SI and a balance ratio P. Assembly line balance rate P: the method is an important index of assembly line balance and time utilization efficiency in the whole assembly process, and the higher the balance rate is, the higher the time utilization efficiency is.
Figure BDA0003169257640000121
The smoothing coefficient SI is used for measuring the distribution of the time of each work station relative to the production beat on the assembly line and reflecting the discrete condition of the time of each work station. The smaller the smoothing factor, the smaller the deviation of all station times with respect to tact, and the better the assembly line balance.
Figure BDA0003169257640000122
Where CT is the production tempo, and is generally taken as CT max (T)i)；TiThe time of the work station is the sum of all the time of the working procedures distributed to the work station; m is the number of work stations
As a result of the above test, the smoothing coefficient SI obtained was 3.376, and the balance ratio was 81.4%, which is a good balance effect in the assembly line.
The splitting process (process 2 and process 9) from the process allocation map plus the pre-optimization results in a workstation logical layout, also called an assembly line logical layout, as shown in fig. 5. In fig. 5, the numbers outside the frames represent the serial numbers of the workstations, the numbers inside the frames represent the serial numbers of the processes, there are five workstations in total, the solid line frame is the main workstation, the dashed line frame represents the auxiliary workstation, and the main workstation and the auxiliary workstation complete the same assembly process, so that the processes distributed to the same workstation as the splitting process can obtain the advantage of halving the assembly time, reduce the assembly time of the workstations, and therefore, the production tact CT in the optimization target can be reduced, and a better result can be obtained. Note also that this is a logical layout, representing the assembly logical order of each workstation, and is not a true physical layout.
To obtain a physical entity layout diagram required in real actual production, consideration needs to be given to the direct post-process of the process in each workstation on the priority relationship diagram, and consideration needs to be given to factors such as floor space and semi-finished product transfer efficiency, and here, we use the above logic layout diagram as an example to derive the entity layout diagram, as shown in fig. 6.
By way of example of the workstation 1, the semifinished product of the workstation 1 is sent to the workstation 2 and to the workstation 5, since the workstation 1 is equipped with the operations 8 and 10, while the operation directly following the operation 8 is the operation 9, which is carried out in the workstation 2, and the operation directly following the operation 10 is the operation 11, which is carried out in the workstation 5.
The background of the present invention may contain background information related to the problem or environment of the present invention and does not necessarily describe the prior art. Accordingly, the inclusion in the background section is not an admission of prior art by the applicant.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several equivalent substitutions or obvious modifications can be made without departing from the spirit of the invention, and all the properties or uses are considered to be within the scope of the invention.

Claims (10)

1. A reconfigurable flexible assembly line layout method is characterized by comprising the following steps:
s1, designing a reconfigurable flexible assembly line with the reconfigurable flexible assembly unit as a core component;
s2, providing corresponding problem hypothesis and a mathematical model of multiple optimization targets according to the reconfigurable flexible assembly line;
and S3, solving the mathematical model by using a pre-optimization operator and a double-population genetic algorithm to obtain a layout of the reconfigurable flexible assembly line.
2. A reconfigurable flexible assembly line layout method according to claim 1, wherein in step S1, the reconfigurable flexible assembly cell includes a robot (101), an assembly tool and tool holder (102), an assembly fixture and fixture base (103), a pallet (104); the robot (101) is located at the center of the workbench, and the assembling tool and tool rack (102), the assembling fixture and fixture base (103) and the tray (104) are annularly arranged on the workbench.
3. A reconfigurable flexible assembly line layout method according to claim 2, characterized in that the robot (101) is used to perform assembly actions on the reconfigurable flexible assembly cell;
the assembly tool and the tool rack (102) are used for storing the assembly tool, and the assembly tool can be configured at the tail end of the robot (101) and used for assembling parts;
in the assembly fixture and fixture base (103), the assembly fixture is arranged on the fixture base and used for fixing and positioning the part;
the assembly tool and the assembly fixture are matched with assembly tasks to be completed on the reconfigurable flexible assembly unit;
the tray (104) is used to hold parts needed for assembly conveyed by the peripheral material handling system.
4. A reconfigurable flexible assembly line layout method according to claim 3, wherein the work process of the reconfigurable flexible assembly line comprises the following steps: before assembly, determining the number of the reconfigurable flexible assembly units, determining the required assembly tools and the required assembly fixtures according to the assembly process of a product, then respectively conveying the required assembly tools and the required assembly fixtures from an assembly tool library and an assembly fixture library to the reconfigurable flexible assembly units by an AGV trolley, respectively configuring the reconfigurable flexible assembly units on the tool rack and the fixture base, and placing parts in the trays (104) on a workbench by a material conveying system; when assembling is started, the robot (101) takes out the parts from the inlet tray, places the parts in a clamp for clamping and assembling, and then carries out the next assembling process; and after all assembly procedures on the reconfigurable flexible assembly units are completed, moving the semi-finished product to an outlet tray, and moving the semi-finished product to an inlet tray of the next reconfigurable flexible assembly unit by a transfer robot to sequentially complete the whole assembly process.
5. The reconfigurable flexible assembly line layout method of claim 1, wherein the problem assumption in step S2 includes:
s2-1, all the procedures are minimum operation elements and can not be re-separated;
s2-2, each assembly process can be distributed to any workstation, but the same assembly process cannot be distributed to a plurality of different workstations;
s2-3, the priority relation among the processes is known and fixed and is determined by a priority relation graph, so that the assembly processes distributed to the workstations are strictly in accordance with the priority constraint relation;
s2-4, the working procedure time is fixed and invariable and is determined according to the actual situation;
s2-5, neglecting the loading and unloading time and the robot starting time;
s2-6, neglecting the clamp replacing time and the transferring time of the transferring robot.
6. The reconfigurable flexible assembly line layout method according to claim 1, wherein the optimization goals of the mathematical model in step S3 include tact CT, tool change time RT, bench cost ST;
min w1*CT+w2*RT+w3*ST
wherein, w1、w2、w3Respectively, the weights of the respective considerations.
7. The reconfigurable flexible assembly line layout method according to claim 6, wherein the mathematical model of the tact CT in step S2 is:
Figure FDA0003169257630000021
the constraint conditions are:
Figure FDA0003169257630000031
Figure FDA0003169257630000032
Figure FDA0003169257630000033
Figure FDA0003169257630000034
wherein, CT is the production beat; i, j is the number of the assembly process, i, j is 1,2, …, n; k is the workstation serial number, k is 1,2, …, m; n is the total number of assembly processes; m is the total number of the workstations, namely the total number of the robots; t is tiIs the process i time; pre (i) is a direct pre-process set for task i; decision variable xikIs composed of
Figure FDA0003169257630000035
The mathematical model of the tool change time RT is:
Figure FDA0003169257630000036
the constraint conditions are:
Figure FDA0003169257630000037
Figure FDA0003169257630000038
wherein RT is the assembly tool change time; RTI is the time required to change a tool; the decision variable Tool (i, j) is
Figure FDA0003169257630000039
The decision variable P (i, j) is
Figure FDA00031692576300000310
The mathematical model of the table cost ST is:
ST＝STI·(n+S)
wherein ST is the total cost of the reconfigurable flexible assembly unit; STI is the cost of one of the reconfigurable flexible assembly cells; and S is the increased number of the reconfigurable flexible assembly units.
8. The method as claimed in claim 1, wherein the pre-optimization operator in step S3 calculates the number of reconfigurable flexible assembly cells that can be added in a workstation by "splitting" the assembly process with a longer assembly time, and adding assembly cells with the same number as the splitting process in the workstation to reduce the assembly time of the process.
9. The reconfigurable flexible assembly line layout method according to claim 1, wherein the main implementation process of the double population genetic algorithm in the step S3 includes:
s3-1, coding: the chromosome is coded by adopting an operation element sequential coding mode, operation elements are arranged in a line according to the priority relation of the operation elements, and each operation element corresponds to a gene position to form an individual;
s3-2, decoding: on the premise of meeting the process priority relationship, sequentially distributing the processes to the workstations;
s3-3, population initialization: initializing a population by adopting a random method;
s3-4, fitness function: namely a multi-objective optimization function;
s3-5, selection, crossover and mutation operators: through the comparison of fitness functions, excellent individuals are selected, and the advantages and disadvantages are realized.
10. Reconfigurable flexible assembly line layout apparatus, characterized in that a reconfigurable flexible assembly line layout is obtained using the method of any of claims 1-9.
CN202110813304.XA 2021-07-19 2021-07-19 Reconfigurable flexible assembly line layout method and device Active CN113609759B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110813304.XA CN113609759B (en) 2021-07-19 2021-07-19 Reconfigurable flexible assembly line layout method and device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110813304.XA CN113609759B (en) 2021-07-19 2021-07-19 Reconfigurable flexible assembly line layout method and device

Publications (2)

Publication Number Publication Date
CN113609759A true CN113609759A (en) 2021-11-05
CN113609759B CN113609759B (en) 2023-07-25

Family

ID=78337904

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110813304.XA Active CN113609759B (en) 2021-07-19 2021-07-19 Reconfigurable flexible assembly line layout method and device

Country Status (1)

Country Link
CN (1) CN113609759B (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107316107A (en) * 2017-06-15 2017-11-03 南京理工大学 A kind of tricot machine assembly line balancing method towards multiple-objection optimization
CN110238621A (en) * 2019-05-30 2019-09-17 清华大学深圳研究生院 Reconfigurable flexible assembly system
CN111353604A (en) * 2018-12-24 2020-06-30 南京理工大学 Flexible assembly multi-objective dynamic optimization method
WO2021036658A1 (en) * 2019-08-29 2021-03-04 华中科技大学 Multi-objective optimization method and system for master production schedule of casting parallel workshops

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107316107A (en) * 2017-06-15 2017-11-03 南京理工大学 A kind of tricot machine assembly line balancing method towards multiple-objection optimization
CN111353604A (en) * 2018-12-24 2020-06-30 南京理工大学 Flexible assembly multi-objective dynamic optimization method
CN110238621A (en) * 2019-05-30 2019-09-17 清华大学深圳研究生院 Reconfigurable flexible assembly system
WO2021036658A1 (en) * 2019-08-29 2021-03-04 华中科技大学 Multi-objective optimization method and system for master production schedule of casting parallel workshops

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
梅攀: "基于动态分批的柔性流水车间能效优化调度方法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》, no. 01, pages 140 - 31 *
谢翔: "基于双种群遗传算法的装配线平衡优化研究——以W企业电子天平秤装配线为例", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》, no. 04, pages 022 - 375 *

Also Published As

Publication number Publication date
CN113609759B (en) 2023-07-25

Similar Documents

Publication Publication Date Title
CN109657354B (en) Mixed flow assembly workshop rapid reconstruction method and system based on digital twinning
Yoshitake et al. New automated guided vehicle system using real-time holonic scheduling for warehouse picking
Nee A framework for an object/rule-based automated fixture design system
Heilala et al. Modular reconfigurable flexible final assembly systems
CN110058573B (en) Huff and puff flexible intelligent assembly logistics path planning platform
CN110084405B (en) Throughput flexible intelligent assembly logistics path planning method
Xin et al. A time-space network model for collision-free routing of planar motions in a multirobot station
Kumar et al. Simulation modelling and analysis of part and tool flow control decisions in a flexible manufacturing system
Moussa et al. Master assembly network for alternative assembly sequences
Zhou et al. Multi-robot multi-station cooperative spot welding task allocation based on stepwise optimization: An industrial case study
Dolgui et al. Enhanced mixed integer programming model for a transfer line design problem
Yifei et al. An estimate and simulation approach to determining the automated guided vehicle fleet size in FMS
KR20130115162A (en) Device for setting processing chamber assignment and recording medium
Pattanaik et al. Tri-objective optimisation of mixed model reconfigurable assembly system for modular products
Yang et al. Assembly transport optimization for a reconfigurable flow shop based on a discrete event simulation.
CN113609759A (en) Reconfigurable flexible assembly line layout method and device
Heemskerk et al. A concept for computer-aided process planning of flexible assembly
Németh et al. Layout configuration, maintenance planning and simulation of AGV based robotic assembly systems
Jawahar et al. Optimal random storage allocation for an AS/RS in an FMS
Vaher et al. THE MOBILITY OF ROBOTISED WORK CELLS IN MANUFACTURING.
Khan et al. A knowledge based methodology for planning and designing of a flexible manufacturing system (FMS)
Cedeno-Campos et al. Highly flexible self-reconfigurable systems for rapid layout formation to offer manufacturing services
CN116457139A (en) Assembly sequence generation
Dove Design principles for highly adaptable business systems, with tangible manufacturing examples
Weber et al. Job Placement for Cooperative 3D Printing

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