CN109992919B - Beer filling production line equipment model selection system based on particle swarm algorithm - Google Patents
Beer filling production line equipment model selection system based on particle swarm algorithm Download PDFInfo
- Publication number
- CN109992919B CN109992919B CN201910288888.6A CN201910288888A CN109992919B CN 109992919 B CN109992919 B CN 109992919B CN 201910288888 A CN201910288888 A CN 201910288888A CN 109992919 B CN109992919 B CN 109992919B
- Authority
- CN
- China
- Prior art keywords
- equipment
- production line
- beer filling
- capacity
- model selection
- 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.)
- Active
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 87
- 239000002245 particle Substances 0.000 title claims abstract description 50
- 235000013405 beer Nutrition 0.000 title claims abstract description 47
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 27
- 238000004806 packaging method and process Methods 0.000 claims abstract description 26
- 238000012545 processing Methods 0.000 claims abstract description 10
- 238000000034 method Methods 0.000 claims description 28
- 238000009826 distribution Methods 0.000 claims description 2
- 238000002360 preparation method Methods 0.000 abstract description 2
- 238000013461 design Methods 0.000 description 6
- 238000005457 optimization Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000004140 cleaning Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012407 engineering method Methods 0.000 description 2
- 238000002372 labelling Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 238000012800 visualization Methods 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012856 packing Methods 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 238000005406 washing Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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
-
- 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
- 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
- G06F2111/04—Constraint-based CAD
-
- 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)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- Artificial Intelligence (AREA)
- Economics (AREA)
- Geometry (AREA)
- Human Resources & Organizations (AREA)
- Computer Hardware Design (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Life Sciences & Earth Sciences (AREA)
- Manufacturing & Machinery (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- General Factory Administration (AREA)
Abstract
The invention discloses a particle swarm algorithm-based beer filling production line equipment model selection system, which relates to the technical field of beer filling production lines and is characterized in that: at least comprises the following steps: the database module is used for storing beer filling and packaging equipment data of different manufacturers, wherein the beer filling and packaging equipment data comprises equipment capacity, equipment price and equipment floor area; and the data processing module is used for processing the beer filling and packaging equipment data based on the particle swarm algorithm so as to obtain a model selection scheme. According to the invention, the equipment required by the beer filling production line at the initial stage of factory building is subjected to model selection through the particle swarm intelligent algorithm, the optimal equipment is selected from the database according to the customer requirements, a brand-new model selection scheme is rapidly provided for customers, the existing bottleneck problem is solved, the balance rate of the whole packaging production line is improved, the idle time of a work station is reduced, and therefore, the model selection preparation is made for building the brand-new packaging production line, and the efficiency of the whole production line is improved.
Description
Technical Field
The invention relates to the technical field of beer filling production lines, in particular to a particle swarm algorithm-based beer filling production line equipment model selection system.
Background
As is well known, the process of beer bottling line is as follows: firstly, purchasing empty bottles from a glass factory or recycling old bottles, and after beer bottles enter the factory, putting the empty bottles in a warehouse or directly conveying the empty bottles to a beer filling production line; then removing the bottle cap by a bottle cap removing machine, cleaning by a bottle cleaning machine, and inspecting by a bottle inspecting device after cleaning; then the beer bottles are filled by a filling machine, the bottle caps conveyed by a bottle cap conveyor and the filled beer bottles are capped by a bottle capping machine, and then finished product inspection is carried out; labeling by a labeling machine, and then boxing by a boxing machine; and finally, stacking by a stacking robot, and warehousing the packaged finished products, or directly delivering the finished products according to actual requirements.
Line balance problems have been studied since 1954 to date for more than 60 years, and there are two main types of line solution designs: one is to modify the existing production line and the other is to set up a completely new production line. More of the first category is currently studied and less of the second category. Compared with the old planning and design concept, the new design concept is different from the old planning and design concept, 80% of problems are dragged to the production stage to be solved, and the new design concept enables 80% of problems to be thoroughly solved in the early planning and design stage, so that a great number of problems of projects in the production stage are avoided. Packaging line balancing essentially optimizes all processes and equipment on the line so that the workload and the operating time on each process or workstation are balanced. The method aims at the aspects of quality, efficiency, cost and the like, reduces the waste of working hours by eliminating bottleneck working procedures, and finally realizes 'one-flow' production. In general, when the balance rate of a packaging production line is 70-85%, the control of an assembly production line is basically scientific; while when the line balance rate is higher than 85%, the production process is performed in a "one stream" mode.
For half a century, researchers have proposed various techniques and methods to address the packaging line balance rate problem. The method mainly comprises an industrial engineering method, a simulation method, an accurate solving method and a heuristic algorithm. The industrial engineering method is often dependent on the experience of a manager and lacks the support of data; the simulation method has a visual effect, but consumes a great deal of time at the initial modeling stage; the calculation precision of the accurate solution method is high, but when the model is complex, a calculation result can be obtained only by taking a long time; with the development of heuristic algorithm, namely intelligent algorithm, the problem of production line balance is solved to a certain extent. The packaging industry is also developing towards flexibility, informatization and intellectualization at present, but at present, a method for solving the quantity of equipment required by a beer filling production line through an intelligent algorithm is not reported.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a particle swarm algorithm-based beer filling production line equipment model selection system, which is used for carrying out model selection on equipment required by the beer filling production line at the initial stage of factory building through a particle swarm intelligent algorithm, selecting the optimal equipment from a database according to the requirements of customers, quickly providing a brand-new model selection scheme for the customers, solving the existing bottleneck problem, improving the balance rate of the whole packaging production line, reducing the idle time of a work station, and thus making model selection preparation for building the brand-new packaging production line and improving the efficiency of the whole production line.
The technical scheme adopted by the invention for solving the technical problems in the prior art is as follows:
a beer filling production line equipment model selection system based on particle swarm optimization at least comprises:
the database module is used for storing beer filling and packaging equipment data of different manufacturers, wherein the beer filling and packaging equipment data comprises equipment capacity, equipment price and equipment floor area;
the data processing module is used for processing the beer filling and packaging equipment data based on a particle swarm algorithm so as to obtain a model selection scheme; the method comprises the following specific steps:
s1, importing beer filling and packaging equipment data;
s2, initializing parameters; the parameter comprises a maximum iteration number MaxIt; the maximum value ω max and the minimum value ω min of the inertia weight; adjustment coefficients c1, c2;
s3, inputting customer requirements, wherein the customer requirements comprise beer filling production line capacity, total equipment cost and total equipment floor area;
s4, initializing the population xSize, wherein the number of the initializing population xSize ranges from 20 to 40;
s5, calculating a target value: firstly, according to the target productivity of a beer packaging production line required by a client, obtaining the quantity of equipment required by a corresponding scheme from randomly selected schemes through the productivity data of different equipment, rounding up, and placing a plurality of equipment in a workstation when a certain procedure requires the plurality of equipment; calculating the actual capacity of the equipment under different schemes according to the number of the equipment, and taking the lowest capacity as the capacity of the production line under the scheme after obtaining the capacity of the equipment; calculating to obtain the balance rate and the smoothness coefficient of the production line under the corresponding scheme; calculating the total price and the total occupied area of the equipment according to the quantity of the equipment obtained in the previous step; finally, optimizing the multiple targets of the highest balance rate of the production line, the lowest total price of the equipment and the condition that the total occupied area of the equipment meets the requirements of customers;
s6, under the constraint conditions of meeting the capacity of a client and the total occupied area of equipment, calculating by taking the highest balance rate of a production line and the lowest total cost of the production line as targets to obtain a non-inferior solution set, judging whether the non-inferior solution set contains a non-inferior solution, if so, carrying out the next step, otherwise, outputting a production scheme which does not meet the requirements of the client in a current database module;
s7, updating the inertia weight omega by adopting a linear descending inertia weight;
wherein: omega start Is the initial inertial weight; omega end The inertial weight when the iteration reaches the maximum number; iter is the current iteration number; maxIt is the maximum iteration number;
s8, updating the new speed and the new position of the particles;
the velocity and position update formulas are:
V k+1 ＝ωV k +c 1 r 1 (P k id -X k )+c 2 r 2 (P k gd -X k )
X k+1 ＝X k +V k+1
wherein: r1 and r2 are distributed in [0,1]A random number in between; k is the current number of iterations; p k id Optimal particle locations for the individual; p k gd Is the global optimal particle position; c1 and c2 are constants, and V is the particle velocity; x is the particle position;
s9, recalculating the target value according to the new particle position;
s10, updating the optimal position of the particle;
s11, updating a non-inferior solution set;
s12, calculating a corresponding position of the non-inferior solution set particles in the database, a corresponding equipment quantity selection scheme, a corresponding production line balance rate and smoothness coefficient, equipment total cost and equipment total floor area under the current iteration condition, and drawing;
s13, judging whether a maximum iteration number termination condition is met, and if so, outputting a result; otherwise, returning to S7 and continuing to calculate.
Further: c1= c2=2.
Further: the data processing module is a processor with Matlab.
Further: the production balance rate shows the balance degree of the working process among all the workstations on the production line, and the production is flat
The balance rate is represented by P:
wherein: n is the number of workstations; cn (c) i Is the total energy of the ith workstation device.
Further: the smoothness coefficient represents an index of the discrete condition of the capacity distribution of each workstation on the production line, and is flat
The coefficient of lubricity is expressed as SI:
wherein: n is the number of workstations; cn is the minimum capacity required by the client; cn (c) i Is the total energy of the ith workstation device. The invention has the advantages and positive effects that:
by adopting the technical scheme, the scheme of quickly selecting the equipment through the particle group intelligent algorithm not only meets the capacity requirement of a client, but also meets multiple targets such as the constraint of the total maximum occupied area of the equipment and the like, and the total investment sum of the machine equipment is also within the budget of the client. In particular, the smoothness factor of the production line is very low; the balance rate is up to more than 90%, the production efficiency is very high, and the requirement of one-flow production is met.
Description of the drawings:
FIG. 1 is a flow chart of a preferred embodiment of the present invention;
FIG. 2 is a case line diagram of a beer filling line;
fig. 3 is a visualization scheme diagram of a preferred embodiment of the present invention.
Detailed Description
For a further understanding of the invention, its nature and utility, reference should be made to the following examples, taken in conjunction with the accompanying drawings, in which:
as shown in FIG. 1, the invention discloses a beer filling line equipment model selection system based on a particle swarm algorithm.
The basic terms:
(1) A workstation: an automated apparatus for carrying out a plurality of successive processes, where N is commonly used to denote the number of stations, and one station is capable of carrying out a plurality of successive processes, and a process with the same apparatus is considered to be one station.
(2) Production balance rate: the balance degree of the working process among all the workstations on the production line is represented, and the larger the production balance rate is, the better the production balance rate is. Commonly denoted by P:
wherein: n is the number of workstations;cn i is the total energy of the ith workstation device.
(3) Smoothness coefficient: the smoothness coefficient is smaller and better. Commonly used SI indicates:
wherein: n is the number of workstations; cn is the minimum capacity required by the client; cn (c) i Is the total energy of the ith workstation device.
(4) Non-inferior solution: if there is not another feasible solution, so that the targets in one solution are all inferior to the solution, the solution is called a non-inferior solution of the multi-objective optimization problem.
(5) Non-inferior solution set: a set of all non-inferior solutions.
Beer filling production line equipment model selection system based on particle swarm algorithm includes:
the database module is used for storing beer filling and packaging equipment data of different manufacturers, wherein the beer filling and packaging equipment data comprises equipment capacity, equipment price and equipment floor area;
the data processing module is used for processing the beer filling and packaging equipment data based on a particle swarm algorithm so as to obtain a model selection scheme; the method comprises the following specific steps:
s1, importing database data: and (3) mainly performing constraint solution on the capacity, the cost, the occupied area and the like required by a client as multiple targets, and importing the built database model into Matlab software.
S2, initializing parameters: including the maximum number of iterations MaxIt; the maximum value ω max and the minimum value ω min of the inertia weight; the coefficients c1, c2 are adjusted.
And S3, inputting customer requirements, and solving by taking the beer filling production line capacity, the total equipment cost, the total equipment floor area and the like required by customers as multiple targets.
S4, initializing the populations xSize, wherein the larger the number of the initialized populations is, the stronger the particle swarm searching capability is, but the corresponding calculation time is increased, generally, 20-40 initialized populations are better, and if the data in the database is more, the number of the initialized populations is correspondingly increased.
S5, calculating a target value: firstly, according to the target productivity of the beer packaging production line required by a client, obtaining the quantity of equipment required by the corresponding scheme from the randomly selected scheme through the productivity data of different equipment, and rounding up. Assuming that a process requires multiple pieces of equipment, the pieces of equipment are placed in a single workstation. And calculating the actual capacity of the equipment under different schemes according to the number of the equipment, and taking the lowest capacity as the capacity of the production line under the scheme after obtaining the capacity of the equipment. And calculating to obtain the balance rate and the smoothness coefficient of the production line under the corresponding scheme. And calculating the total price and the total floor area of the equipment according to the number of the equipment obtained in the previous step. And finally, optimizing the multiple targets by taking the highest balance rate of the production line, the lowest total price of the equipment and the condition that the total occupied area of the equipment meets the requirements of customers.
And S6, under the constraint conditions of meeting the capacity of the client and the total occupied area of the equipment, calculating by taking the highest balance rate of the production line and the lowest total cost of the production line as targets to obtain a non-inferior solution set. Is it determined whether there are non-inferior solutions in the set? If so, the next step is carried out, otherwise, the production scheme which meets the customer requirement is output in the current database under the condition of the customer requirement.
S7, updating the inertia weight: the inertia weight embodies the capability of the particle to inherit the previous speed, a larger inertia weight is beneficial to global search, a smaller inertia weight is beneficial to local search, and in order to better balance the global search and the local search capability of the algorithm, the linear decreasing inertia weight is adopted. The equation for solving ω is as follows:
wherein: omega start Is the initial inertial weight; omega end The inertial weight when the iteration reaches the maximum number; iter is the current iterationGeneration times; maxIt is the maximum number of iterations.
And calculating the weighted inertia weight under the current iteration condition according to an inertia weight updating formula.
And S8, updating the new speed and the new position of the particles.
The velocity and position update formulas are:
V k+1 ＝ωV k +c 1 r 1 (P k id -X k )+c 2 r 2 (P k gd -X k ) (4)
X k+1 ＝X k +V k+1 (5)
wherein: r1 and r2 are distributed in [0,1]A random number in between; k is the current number of iterations; p k id Optimal particle locations for the individual; p k gd Is the global optimal particle position; c1 and c2 are constants, typically c1 equals c2 equals 2; v is the particle velocity; x is the particle position.
And S9, recalculating the target value according to the new particle position in the same way as the S5.
And S10, updating the optimal position of the particle.
And S11, updating the non-inferior solution set.
S12, calculating the corresponding position of the non-inferior solution set particles in the database and the corresponding equipment quantity selection scheme (table) under the current iteration condition, corresponding production line balance rate (%) and smoothness coefficient, equipment total cost (element) and equipment total floor area (square meter), and drawing.
S13, determine whether the maximum iteration count termination condition is satisfied? If yes, outputting a result; otherwise, returning to S7 and continuing to calculate.
The equipment required by the process flow shown in figure 2 is rapidly selected according to the process requirements of customers.
Establishing a database:
the method comprises the steps of establishing a database of equipment required by a beer filling production line by collecting beer filling and packaging equipment data of different manufacturers, wherein the main data of the equipment is the corresponding equipment capacity, and the capacity is the highest capacity corresponding to the equipment; the price of the equipment; the floor area of the equipment is occupied. The partial data is limited to theoretical analysis and research, more accurate data needs to be collected for solving in practical application, and the partial data of the database is shown in table 1.
TABLE 1 beer filling line equipment database parameters
The obvious characteristic of the multi-objective optimization problem is that each objective is optimized to simultaneously reach the optimal value under the comprehensive condition. Suppose the target capacity of customer demand is 10000 bottles/hour; the total sum of the pre-investment equipment is 20-50 ten thousand; the total floor space of the equipment is 40 square meters. Under the requirement, the particle swarm intelligent algorithm is used for rapidly selecting the type from the database.
Respectively initializing 40 populations and 100 populations to ensure the reliability of results; the number of iterations was 2000, 5000, 8000, respectively. The detailed solution data results are shown in table 2.
TABLE 2 particle swarm optimization results
As shown in table 2, the same color in the non-inferior solution position column indicates that the solution results are the same. As can be seen from table 2, when the population number is 40 and the number of iterations is 2000, 5000, 8000, respectively, six solutions are completely the same; when the population number is 100 and the iteration times are 2000, 5000 and 8000 respectively, nine solving schemes are completely the same. And when the population number is respectively 40 and 100 and the iteration number is 8000, the results of eight schemes are completely the same. In conclusion, as the population number increases, the more the iteration times are, the closer the non-inferior solution sets are, the more reliable the solution result is, and the above data indicate that the particle cluster intelligent algorithm solution is feasible.
When the population is 100 and the number of iterations is 8000, a visualization scheme obtained by using a particle swarm intelligent algorithm is shown in FIG. 3 (a-non-inferior solution is distributed in a target space; b-smoothness coefficients under different schemes; c-equipment production energy under different schemes; d-equipment quantity under different schemes; e-lowest production capacity of a production line; and f-the number of iterations).
From fig. 3 (a), when the total cost of the production line equipment is constant, the corresponding solution can make the corresponding production balance rate in all solution sets the highest. Similarly, when the production balance rate is constant, the corresponding solution scheme can minimize the total cost of the corresponding production line equipment in all solution sets. These non-inferior solutions are within the constraints of line capacity and total floor space required by the customer. The final selection scheme is determined by referring to the solution results of fig. 3 (b), fig. 3 (d), and fig. 3 (e).
Since the total cost of the solution results in the non-inferior solutions is within the customer budget, the final solution is selected according to the line balance rate and the line smoothness coefficient. The smoothness coefficient of the equipment in the fourth scheme and the smoothness coefficient of the equipment in the eighth scheme are lower, and the balance rate of the production line is respectively 98.4% and 100%, so the eighth scheme is better. But the production line capacity of the solution four is 11040 bottles/hour and is closer to the requirement of the customer, so the solution four is the final optimal solution. The result of the selection scheme is as follows: 1 bottle washing machine with the production capacity of 12000 bottles/hour; 1 filling machine with the production capacity of 12000 bottles/hour; 4 capping machines with the production capacity of 3000 bottles/hour; 1 labeler with the production capacity of 12000 bottles/hour; 4 packing machines with the production capacity of 2760 bottles/hour; the balance rate of the production line is 98.4 percent; the productivity is 11040 bottles/hour; the total cost of the production line equipment is 270200 yuan; the total floor area of the production line equipment is 27.994 square meters, and the requirements of customers are met.
The embodiments of the present invention have been described in detail, but the description is only for the preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (5)
1. The utility model provides a beer filling line equipment lectotype system based on particle swarm algorithm which characterized in that: at least comprises the following steps:
the database module is used for storing beer filling and packaging equipment data of different manufacturers, wherein the beer filling and packaging equipment data comprises equipment capacity, equipment price and equipment floor area;
the data processing module is used for processing the beer filling and packaging equipment data based on a particle swarm algorithm so as to obtain a model selection scheme; the method comprises the following specific steps:
s1, importing beer filling and packaging equipment data;
s2, initializing parameters; the parameter comprises a maximum iteration number MaxIt; the maximum value ω max and the minimum value ω min of the inertia weight; adjustment coefficients c1, c2;
s3, inputting customer requirements, wherein the customer requirements comprise beer filling production line capacity, total equipment cost and total equipment floor area;
s4, initializing the population xSize, wherein the number of the initializing population xSize ranges from 20 to 40;
s5, calculating a target value: firstly, according to the target productivity of a beer packaging production line required by a client, obtaining the quantity of equipment required by a corresponding scheme from randomly selected schemes through the productivity data of different equipment, rounding up, and placing a plurality of equipment in a workstation when a certain procedure requires the plurality of equipment; calculating the actual capacity of the equipment under different schemes according to the number of the equipment, and taking the lowest capacity as the capacity of the production line under the scheme after obtaining the capacity of the equipment; calculating to obtain the balance rate and the smoothness coefficient of the production line under the corresponding scheme; calculating the total price and the total occupied area of the equipment according to the quantity of the equipment obtained in the previous step; finally, optimizing the multiple targets of the highest balance rate of the production line, the lowest total price of the equipment and the condition that the total occupied area of the equipment meets the requirements of customers;
s6, under the constraint conditions of meeting the capacity of a client and the total occupied area of equipment, calculating by taking the highest balance rate of a production line and the lowest total cost of the production line as targets to obtain a non-inferior solution set, judging whether the non-inferior solution set contains a non-inferior solution, if so, carrying out the next step, otherwise, outputting a production scheme which does not meet the requirements of the client in a current database module;
s7, updating the inertia weight omega by adopting a linear descending inertia weight;
wherein: omega start Is the initial inertial weight; omega end The inertial weight when the iteration reaches the maximum number; iter is the current iteration number; maxIt is the maximum iteration number;
s8, updating the new speed and the new position of the particles;
the velocity and position update formulas are:
V k+1 ＝ωV k +c 1 r 1 (P k id -X k )+c 2 r 2 (P k gd -X k )
X k+1 ＝X k +V k+1
wherein: r1 and r2 are distributed in [0,1]A random number in between; k is the current iteration number; p is k id Optimal particle locations for the individual; p k gd Is the global optimal particle position; c1 and c2 are constants, and V is the particle velocity; x is the particle position;
s9, recalculating the target value according to the new particle position;
s10, updating the optimal position of the particles;
s11, updating a non-inferior solution set;
s12, calculating a corresponding position of the non-inferior solution set particles in the database, a corresponding equipment quantity selection scheme, a corresponding production line balance rate and smoothness coefficient, equipment total cost and equipment total floor area under the current iteration condition, and drawing;
s13, judging whether a maximum iteration time termination condition is met, and if so, outputting a result; otherwise, returning to S7 and continuing to calculate.
2. The particle swarm algorithm-based beer filling line equipment model selection system according to claim 1, wherein: c1= c2=2.
3. The particle swarm algorithm-based beer filling line equipment model selection system of claim 2, wherein: the data processing module is a processor with Matlab.
4. The particle swarm algorithm-based beer filling line equipment model selection system of claim 2, wherein: the production line balance rate represents the balance degree of the working process among all the workstations on the production line, and is represented by P:
wherein: n is the number of workstations; cn (c) i The total energy of the ith workstation equipment.
5. The particle swarm algorithm-based beer filling line equipment model selection system of claim 2, wherein: the smoothness coefficient represents an index of the discrete condition of the capacity distribution of each workstation on the production line, and is represented by SI:
wherein: n is the number of workstations; cn is the minimum capacity required by the client; cn (c) i Is the total energy of the ith workstation device.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910288888.6A CN109992919B (en) | 2019-04-11 | 2019-04-11 | Beer filling production line equipment model selection system based on particle swarm algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910288888.6A CN109992919B (en) | 2019-04-11 | 2019-04-11 | Beer filling production line equipment model selection system based on particle swarm algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109992919A CN109992919A (en) | 2019-07-09 |
| CN109992919B true CN109992919B (en) | 2023-04-07 |
Family
ID=67133247
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910288888.6A Active CN109992919B (en) | 2019-04-11 | 2019-04-11 | Beer filling production line equipment model selection system based on particle swarm algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109992919B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111266846B (en) * | 2020-01-14 | 2021-11-26 | 昆山市富川机电科技有限公司 | Stranding machine assembling system and method equipped with intelligent logistics |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103942612A (en) * | 2014-04-08 | 2014-07-23 | 河海大学 | Cascade reservoir optimal operation method based on adaptive particle swarm optimization algorithm |
| 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 |
| CN108764449A (en) * | 2018-05-18 | 2018-11-06 | 九江学院 | A method of improving PSO Algorithm white body assemble welding line balance problem |
| CN109086900A (en) * | 2018-08-31 | 2018-12-25 | 贵州电网有限责任公司都匀供电局 | Power Material guarantee and deployment platform based on multi-objective particle |
-
2019
- 2019-04-11 CN CN201910288888.6A patent/CN109992919B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103942612A (en) * | 2014-04-08 | 2014-07-23 | 河海大学 | Cascade reservoir optimal operation method based on adaptive particle swarm optimization algorithm |
| 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 |
| CN108764449A (en) * | 2018-05-18 | 2018-11-06 | 九江学院 | A method of improving PSO Algorithm white body assemble welding line balance problem |
| CN109086900A (en) * | 2018-08-31 | 2018-12-25 | 贵州电网有限责任公司都匀供电局 | Power Material guarantee and deployment platform based on multi-objective particle |
Non-Patent Citations (6)
| Title |
|---|
| Interactive particle swarm optimization;J. Madar 等;《IEEE》;20060123;全文 * |
| 啤酒企业包装生产调度优化研究;周佳莹;《中国优秀硕士学位论文全文数据库信息科技辑》;20150915;全文 * |
| 基于多目标规划的Z公司啤酒配方优化研究;张宇锋;《中国优秀硕士学位论文全文数据库工程科技Ⅰ辑》;20140515;全文 * |
| 基于生产物流系统分析的啤酒灌装生产线优化设计;曹菲 等;《包装工程》;20070930;第28卷(第9期);全文 * |
| 基于蜂群算法的等压灌装阀阀口流道结构优化;李全来;《包装工程》;20171231;第38卷(第23期);全文 * |
| 灌装输送线数字化设计平台研究与开发;陶熠;《中国优秀硕士学位论文全文数据库信息科技辑》;20100815;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109992919A (en) | 2019-07-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110111048B (en) | Order task scheduling method in warehousing system | |
| CN109886478B (en) | Goods space optimization method for finished wine automatic stereoscopic warehouse | |
| CN110069880B (en) | Multi-target equipment layout and production schedule collaborative optimization method based on simulation | |
| CN110084545B (en) | Integrated scheduling method of multi-lane automatic stereoscopic warehouse based on mixed integer programming model | |
| WO2016169286A1 (en) | Workshop layout method for discrete manufacturing system | |
| CN108550007A (en) | A kind of slotting optimization method and system of pharmacy corporation automatic stereowarehouse | |
| KR102042318B1 (en) | Smart Factory Layout Design Method and System | |
| CN104636871B (en) | A kind of control method of the single phase multi-product batch processing based on data | |
| CN115099543B (en) | Path planning method, device and equipment for battery replacement and storage medium | |
| CN109992919B (en) | Beer filling production line equipment model selection system based on particle swarm algorithm | |
| CN110363402B (en) | Factory personnel scheduling method based on grouping strategy | |
| CN111857081A (en) | Chip packaging test production line performance control method based on Q-learning reinforcement learning | |
| CN111882363A (en) | Sales prediction method, system and terminal | |
| CN115965154A (en) | Knowledge graph-based digital twin machining process scheduling method | |
| Yang | Evaluation of the Joint Impact of the Storage Assignment and Order Batching in Mobile‐Pod Warehouse Systems | |
| Hani et al. | Simulation based optimization of a train maintenance facility | |
| CN110147596B (en) | Aviation product production capacity assessment method | |
| CN110928261A (en) | Distributed estimation scheduling method and system for distributed heterogeneous flow shop | |
| CN102737141A (en) | Processing support device, method and computer readable storage medium, and semiconductor fabrication support device and method | |
| CN113112121A (en) | Workshop layout scheduling optimization method based on multi-objective non-dominated sorting | |
| CN117371918A (en) | Goods space distribution two-stage optimization method and system based on improved order association rule | |
| CN107831764A (en) | A kind of sorting vehicle dispatching method suitable for matrix form warehouse | |
| CN113344407A (en) | Industrial Internet green energy management system | |
| CN115062936A (en) | Automobile assembly line dynamic periodic material distribution scheduling method considering regional responsibility system | |
| CN116011723A (en) | Intelligent dispatching method and application of coking and coking mixed flow shop based on Harris eagle 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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |