Submarine cable production workshop optimization layout method based on genetic algorithm

Abstract

The invention discloses a submarine cable production workshop optimization layout method based on a genetic algorithm, and the method comprises the following steps: creating an initial population in a submarine cable production workshop, and randomly placing equipment at a certain position in the workshop according to a layout scheme; calculating the fitness of each individual in the initial population, establishing an objective function according to an optimization objective, and calculating a function value to evaluate an optimization scheme; increasing the probability that the individuals with high fitness are selected, randomly selecting two parent individuals to carry out crossover operation, and carrying out variation on the individuals of the population according to a set variation rate; running a corresponding program in the Matlab, iterating for multiple times, calculating to obtain the fitness of each generation, outputting an optimal filial generation, and obtaining an optimal layout sketch; according to the optimal layout sketch obtained through output, a layout optimization model is established in Plant simulation, and analogue simulation is carried out; and inserting the layout optimization model into a table for statistical analysis, and optimizing the problem of long time and long distance when the AGV goes from the tray goods taking area to the frame-twisting unloading area.

Description

Technical field:

[0001] The invention relates to a method for optimizing the layout of a submarine cable production workshop based on a genetic algorithm. Background technology:

[0002] The submarine cable production workshop production line is an important part of the enterprise's production. Simulating and optimizing the production line can greatly help the enterprise curb waste, ensure equipment utilization, optimize the balance of the production line, and carry out production activities in limited workshop space.

[0003] Workshop optimization layout planning is an important part of the work in submarine cable production workshops and has attracted the attention of major manufacturing companies. With the acceleration of the intelligentization process of manufacturing, companies are paying more and more attention to workshop layout planning, equipment automation and the rational allocation of human and machine resources. By optimizing the production line structure, upgrading production equipment and planning the workstation layout, they aim to achieve seamless collaboration between man and machine, thereby improving production efficiency and ensuring a balanced and stable production process.

[0004] At present, most of the commonly used optimization layout methods are based on traditional industrial engineering methods, mainly including hierarchical analysis method, recommended warehouse simulation model for analysis and optimization and simulated annealing algorithm, which performs multi-objective optimization inside the production workshop to obtain the corresponding layout optimization plan; however, the existing optimization layout methods are not fully applicable to complex production and logistics problems, especially for the submarine cable production industry. The complex production relations lead to low overall efficiency of the optimized layout, and the production cost of the submarine cable production workshop is high, which cannot meet the needs of modern manufacturing industry to reduce costs and increase efficiency. Summary of the invention:

[0005] The embodiment of the present invention provides a method for optimizing the layout of a submarine cable production workshop based on a genetic algorithm. The method is reasonably designed, based on a genetic algorithm, makes assumptions about the optimization problem, configures relevant genetic operations and sets an objective function, and can perform iterative optimization in the process of optimizing the layout to obtain the optimal layout solution. The layout solution is applied to the simulation model of PlantSimulation, and compared with the equipment layout, simulation time and equipment working information before optimization, thereby reducing the material handling time, reducing the blocking rate of the material temporary storage area, and improving the problem of long time and long distance for AGV to unload. Therefore, the method is fully suitable for the actual application of complex production relations in the submarine cable production industry, reducing the production cost of submarine cable production workshops, meeting the needs of modern manufacturing industry to reduce costs and increase efficiency, and solving the problems existing in the prior art.

[0006] The technical solution adopted by the present invention to solve the above technical problems is:

[0007] A method for optimizing the layout of a submarine cable production workshop based on a genetic algorithm, the method comprising the following steps:

[0008] S1, creating an initial population in a submarine cable production workshop, wherein each individual of the initial population represents a layout scheme, and randomly placing the equipment at a certain position in the workshop according to the layout scheme;

[0009] S2, calculate the fitness of each individual in the initial population, establish the objective function according to the optimization goal, and calculate the function value to evaluate the optimization plan;

[0010] S3, increases the probability of individuals with high fitness being selected, randomly selects two parent individuals for crossover operation, and mutates the individuals in the population according to the set mutation rate;

[0011] S4, run the corresponding program in Matlab, iterate multiple times and calculate the fitness of each generation, output the optimal offspring, and obtain the optimal layout diagram;

[0012] S5, establishing a layout optimization model in Plant Simulation according to the outputted optimal layout diagram and performing simulation;

[0013] S6, insert the layout optimization model into the table for statistical analysis, and optimize the problem of long time and distance for AGV to go from the pallet pickup area to the frame unloading area.

[0014] In the submarine cable production workshop, the order of workstations in each production line is fixed and cannot be disrupted during optimization. According to the production characteristics of the submarine cable production workshop, the submarine cable starts to be frame-stranded and formed from the frame-stranding machine with the least wire intake. After the current frame-stranding machine is completed, it moves to the next frame-stranding machine with more wire intake to continue processing until the complete conductor is produced from the frame-stranding machine with the largest wire intake. Therefore, the order of the frame-stranding machines in each production line is from the frame-stranding machine with the least wire intake to the frame-stranding machine with the largest wire intake.

[0015] The frame stranding machines cannot overlap with each other, and the frame stranding machines cannot overlap with the temporary storage area for the coiled cables, so as to avoid producing unrealistic optimization schemes; during the optimization process, the frame stranding machines can only be iteratively optimized within the specified area and cannot exceed the area; the production lines cannot cross each other, so as to facilitate the transportation of the submarine cable conductors moving within the production line in subsequent production.

[0016] The objective function includes:

[0017] Minimizing the distance from the two temporary storage areas to each frame stranding machine can reflect the shortest time for the AGV to reach the frame stranding machine after picking up the goods from the temporary storage area, which is conducive to reducing logistics costs and time waste. The formula is:

[0018] D ij =min((x i -x j ) 2 +(y i -y j) 2 )

[0019] Maximizing the space utilization of the layout solution can avoid the situation where the frame stranding machines are placed too densely, resulting in low utilization of the workshop area. The formula is:

[0020] S=(x imax -x imin )*(y imax -y imin ) / (n*s)

[0021] Minimize the distance between adjacent stranding machines in the production line to facilitate the completion of a series of stranding processes for the submarine cable conductor in the production line. The formula is:

[0022] d i =(x i -x i+1 ) 2 +(y i -y i+1 ) 2

[0023] The above multiple objective functions are converted into a single objective function, and different weights are assigned to each objective function, namely α, β, and γ; the weight value α of the distance from the minimum buffer area to the frame stranding machine is set to 0.4, so that D ij As the denominator, the maximum space utilization weight value β is set to 0.3, and the space utilization is multiplied by it; the minimum adjacent frame stranding machine distance weight value γ is set to 0.3, so that d i As the denominator, we get the weighted formula:

[0024]

[0025] This shortens the handling time of the copper wire reels and maximizes space utilization.

[0026] The layout optimization model includes a reel pickup area, a transportation scheduling area, a frame twisting unloading area and a system control area; the reel pickup area includes two wire drawing reel production lines, and the copper wire flows directly from the material source to the copper wire reel temporary storage area through the wire drawing station processing, so that the AGV can directly load the copper wire reel from the temporary storage area; the transportation scheduling area includes the AGV track in the workshop and the AGV performing transportation work on the track, as well as the trolley source for generating AGVs and the material termination unit for recycling AGVs; the frame twisting unloading area includes three frame twisting machine production lines, and the copper wire reels transported by the AGV from the reel temporary storage area will be unloaded in the material area corresponding to each frame twisting machine to supply the startup requirements of the frame twisting machine; the system control area is used to stack information flow modules and event controllers, so as to facilitate the observation of method calls during simulation operation.

[0027] The stranding unloading area uses the buffer zone as a template for the stranding machine. According to the initial layout of the stranding machine equipment in the workshop, the 15 stranding machines of the three production lines are arranged in the model in sequence. The capacity of each buffer zone is set according to the number of copper wire drums actually required for the start-up of each stranding machine, and then each stranding machine is connected to the material termination unit of the current production line in sequence.

[0028] The handling and scheduling area uses a one-way track as the AGV's operating path, and the AGV can only travel on the track when transporting materials. At the same time, a sensor is set at the point on the track closest to the unloading and picking up areas. The sensor is used to detect the parking position of the AGV in a non-full load state when there are materials in the picking up area, and to detect the parking position of the AGC in a loaded state if the number of copper wire reels in the unloading area does not meet the startup requirements.

[0029] The system control area is called when the simulation is reset through the event controller, and its function is to close the material outlet of the frame winch in the unloading area, open the material inlet, and close the material inlet for the trolley recovery each time the simulation is reset; it is called when the simulation starts through the event controller, and its function is to assign the 15 frame winch objects in the frame winch unloading area to the table data table at the beginning of the simulation, and assign the 2 temporary storage area objects in the tray pickup area to the table temporary storage table, so that the objects can be directly read from the table when calling later;

[0030] When the vehicle source starts to output the AGV, the system control area turns on the automatic routing function of each AGV. The AGV with the automatic routing function can automatically find the shortest path to the destination when determining the destination;

[0031] The system control area controls a global variable named Final_Time so that the value of the global variable is equal to the total simulation time, and the simulation time can be displayed intuitively in the window; the system control area can assign uniformly distributed initial power values ​​to the five AGVs.

[0032] After the layout optimization model is established, the simulation is started using the event manager until the transport task is completed and the AGV is recovered.

[0033] The present invention adopts the above method, by creating an initial population in a submarine cable production workshop, randomly placing the equipment at a certain position in the workshop according to the layout plan; by establishing an objective function according to the optimization goal, calculating the function value to evaluate the optimization plan; by running the corresponding program in Matlab, iterating multiple times and calculating the fitness of each generation, outputting the optimal offspring, and obtaining the optimal layout diagram; by establishing a layout optimization model in Plant Simulation according to the output optimal layout diagram and performing simulation simulation; by inserting the layout optimization model into a table for statistical analysis, the problem of long time and distance for AGV to go from the finished product pickup area to the frame twist unloading area is optimized, which has the advantages of high efficiency, accuracy, economy and practicality. Description of the drawings:

[0034] Figure 1 It is a schematic diagram of the process of the present invention.

[0035] Figure 2 Schematic diagram of the working principle of the genetic algorithm of the present invention.

[0036] Figure 3 It is the optimization parameter definition table of the present invention.

[0037] Figure 4 This is a simplified diagram of the optimal layout of the present invention.

[0038] Figure 5 It is a structural schematic diagram of the layout optimization model of the present invention.

[0039] Figure 6 This is the AGV operation statistics table after optimization of the present invention. Specific implementation method:

[0040] In order to clearly illustrate the technical features of the present solution, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0041] like Figure 1-6 As shown in the figure, a method for optimizing the layout of a submarine cable production workshop based on a genetic algorithm comprises the following steps:

[0042] S1, creating an initial population in a submarine cable production workshop, wherein each individual of the initial population represents a layout scheme, and randomly placing the equipment at a certain position in the workshop according to the layout scheme;

[0043] S2, calculate the fitness of each individual in the initial population, establish the objective function according to the optimization goal, and calculate the function value to evaluate the optimization plan;

[0044] S3, increases the probability of individuals with high fitness being selected, randomly selects two parent individuals for crossover operation, and mutates the individuals in the population according to the set mutation rate;

[0045] S4, run the corresponding program in Matlab, iterate multiple times and calculate the fitness of each generation, output the optimal offspring, and obtain the optimal layout diagram;

[0046] S5, establishing a layout optimization model in Plant Simulation according to the outputted optimal layout diagram and performing simulation;

[0047] S6, insert the layout optimization model into the table for statistical analysis, and optimize the problem of long time and distance for AGV to go from the pallet pickup area to the frame unloading area.

[0048] In the submarine cable production workshop, the order of workstations in each production line is fixed and cannot be disrupted during optimization. According to the production characteristics of the submarine cable production workshop, the submarine cable starts to be frame-stranded and formed from the frame-stranding machine with the least wire intake. After the current frame-stranding machine is completed, it moves to the next frame-stranding machine with more wire intake to continue processing until the complete conductor is produced from the frame-stranding machine with the largest wire intake. Therefore, the order of the frame-stranding machines in each production line is from the frame-stranding machine with the least wire intake to the frame-stranding machine with the largest wire intake.

[0049] The frame stranding machines cannot overlap with each other, and the frame stranding machines cannot overlap with the temporary storage area for the coiled cables, so as to avoid producing unrealistic optimization schemes; during the optimization process, the frame stranding machines can only be iteratively optimized within the specified area and cannot exceed the area; the production lines cannot cross each other, so as to facilitate the transportation of the submarine cable conductors moving within the production line in subsequent production.

[0050] The objective function includes:

[0051] Minimizing the distance from the two temporary storage areas to each frame stranding machine can reflect the shortest time for the AGV to reach the frame stranding machine after picking up the goods from the temporary storage area, which is conducive to reducing logistics costs and time waste. The formula is:

[0052] D ij =min((x i -x j ) 2 +(y i -y j ) 2 )

[0053] Maximizing the space utilization of the layout solution can avoid the situation where the frame stranding machines are placed too densely, resulting in low utilization of the workshop area. The formula is:

[0054] S=(x imax -x imin )*(y imax -y imin ) / (n*s)

[0055] Minimize the distance between adjacent stranding machines in the production line to facilitate the completion of a series of stranding processes for the submarine cable conductor in the production line. The formula is:

[0056] d i =(x i -x i+1 ) 2 +(y i -y i+1 ) 2

[0057] The above multiple objective functions are converted into a single objective function, and different weights are assigned to each objective function, namely α, β, and γ; the weight value α of the distance from the minimum buffer area to the frame stranding machine is set to 0.4, so that D ij As the denominator, the maximum space utilization weight value β is set to 0.3, and the space utilization is multiplied by it; the minimum adjacent frame stranding machine distance weight value γ is set to 0.3, so that d i As the denominator, we get the weighted formula:

[0058]

[0059] This shortens the handling time of the copper wire reels and maximizes space utilization.

[0060] The layout optimization model includes a reel pickup area, a transportation scheduling area, a frame twisting unloading area and a system control area; the reel pickup area includes two wire drawing reel production lines, and the copper wire flows directly from the material source to the copper wire reel temporary storage area through the wire drawing station processing, so that the AGV can directly load the copper wire reel from the temporary storage area; the transportation scheduling area includes the AGV track in the workshop and the AGV performing transportation work on the track, as well as the trolley source for generating AGVs and the material termination unit for recycling AGVs; the frame twisting unloading area includes three frame twisting machine production lines, and the copper wire reels transported by the AGV from the reel temporary storage area will be unloaded in the material area corresponding to each frame twisting machine to supply the startup requirements of the frame twisting machine; the system control area is used to stack information flow modules and event controllers, so as to facilitate the observation of method calls during simulation operation.

[0061] The stranding unloading area uses the buffer zone as a template for the stranding machine. According to the initial layout of the stranding machine equipment in the workshop, the 15 stranding machines of the three production lines are arranged in the model in sequence. The capacity of each buffer zone is set according to the number of copper wire drums actually required for the start-up of each stranding machine, and then each stranding machine is connected to the material termination unit of the current production line in sequence.

[0062] The handling and scheduling area uses a one-way track as the AGV's operating path, and the AGV can only travel on the track when transporting materials. At the same time, a sensor is set at the point on the track closest to the unloading and picking up areas. The sensor is used to detect the parking position of the AGV in a non-full load state when there are materials in the picking up area, and to detect the parking position of the AGC in a loaded state if the number of copper wire reels in the unloading area does not meet the startup requirements.

[0063] The system control area is called when the simulation is reset through the event controller, and its function is to close the material outlet of the frame winch in the unloading area, open the material inlet, and close the material inlet for the trolley recovery each time the simulation is reset; it is called when the simulation starts through the event controller, and its function is to assign the 15 frame winch objects in the frame winch unloading area to the table data table at the beginning of the simulation, and assign the 2 temporary storage area objects in the tray pickup area to the table temporary storage table, so that the objects can be directly read from the table when calling later;

[0064] When the vehicle source starts to output the AGV, the system control area turns on the automatic routing function of each AGV. The AGV with the automatic routing function can automatically find the shortest path to the destination when determining the destination;

[0065] The system control area controls a global variable named Final_Time so that the value of the global variable is equal to the total simulation time, and the simulation time can be displayed intuitively in the window; the system control area can assign evenly distributed initial power values ​​to the five AGVs.

[0066] After the layout optimization model is established, the simulation is started using the event manager until the transport task is completed and the AGV is recovered.

[0067] The working principle of the genetic algorithm-based submarine cable production workshop optimization layout method in the embodiment of the present invention is: based on the genetic algorithm, assumptions are made on the optimization problem, relevant genetic operations are configured and the objective function is set, it can iteratively search for the best in the optimization layout process, obtain the optimal layout plan, and apply the layout plan to the simulation model of PlantSimulation, and compare it with the equipment layout, simulation time, and equipment working information before optimization, thereby reducing material handling time, reducing the blocking rate of the material temporary storage area, and improving the problem of long time and distance for AGV to unload. It is fully suitable for the actual application of complex production relations in the submarine cable production industry, reducing the production cost of submarine cable production workshops, and meeting the needs of modern manufacturing industry to reduce costs and increase efficiency.

[0068] With the acceleration of the intelligentization process of manufacturing industry, enterprises are paying more and more attention to the layout planning of workshops, equipment automation and the reasonable allocation of human and machine resources. By optimizing the production line structure, upgrading production equipment and planning the layout of workstations, it is aimed to achieve seamless collaboration between man and machine, thereby improving production efficiency and ensuring the balance and stability of the production process. For enterprises engaged in mass production, their product types are relatively single, the processing equipment is relatively simple, and most of them are based on continuous process operations. Although the processing difficulty of some processes is not high, in the actual production process, unreasonable process allocation will lead to too much or too little work at some workstations, which will affect the overall production progress. In addition, some workstations may time out or be idle during operation, resulting in poor balance between workstations, which ultimately restricts the overall production efficiency of the workshop. Therefore, how to achieve smooth and smooth operation of the production line and improve production efficiency through reasonable cost investment within a limited space has always been a problem that enterprises have been working hard to solve. Not only do enterprises need to have advanced production concepts and technical means, but they also need to continue to explore and innovate to adapt to the increasingly fierce market competition environment.

[0069] The production tasks completed in the submarine cable production workshop mainly include drawing copper wire into coils and then transporting them to the stranding production line to participate in the stranding of conductors. The stranding work starts from the stranding machine with the least wire input. After the processing is completed, it is sent to the next stranding machine and stranded together with the copper wire transported, so that the conductor becomes thicker. The processing process is completed one by one from the stranding machine with the least wire input to the stranding machine with the most wire input. After the stranding is completed on the last stranding machine in the production line, which is the stranding machine with the most wire input, the conductor is output.

[0070] Problems such as the lack of tight connection in the production process, the intersection of material transportation routes, and unreasonable transportation distances will affect the production capacity of the workshop, which in turn affects the logistics cost, reduces transportation efficiency, and slows down the production rhythm of the enterprise. This application takes the optimal layout plan as the research direction, and the purpose is to enable the workshop to shorten the logistics handling time and reduce the logistics transportation cost under the new layout plan, improve the connection between the front and rear processes, and enable the frame twisting machine production line to meet the startup requirements as soon as possible and start the frame twisting work. The time saved can also enable the company to formulate working hours that are more in line with the development of the times and more in line with its own interests.

[0071] The design goal of this application is to meet the minimum simulation time required for the start-up of each frame stranding machine. The AGV can meet the condition by transporting the corresponding number of disks required for the start-up of each frame stranding machine to the material area of ​​each frame stranding machine. Formulating a workshop layout optimization plan has a significant role in improving the utilization rate of the workshop floor space, improving the production and handling process, reducing handling costs, and improving handling efficiency.

[0072] In the overall scheme, the optimization layout method includes the following steps: creating an initial population in a submarine cable production workshop, each individual of the initial population represents a layout scheme, and randomly placing the equipment at a certain position in the workshop according to the layout scheme; calculating the fitness of each individual in the initial population, establishing an objective function according to the optimization goal, and calculating the function value to evaluate the optimization scheme; increasing the probability of individuals with large fitness being selected, randomly selecting two parent individuals for crossover operation, and mutating the individuals in the population according to the set mutation rate; running the corresponding program in Matlab, iterating multiple times and calculating the fitness of each generation, outputting the optimal offspring, and obtaining the optimal layout diagram; establishing a layout optimization model in Plant Simulation according to the output optimal layout diagram and performing simulation; inserting the layout optimization model into a table for statistical analysis, and optimizing the problem of long time and distance for AGV to go from the finished product pickup area to the frame unloading area.

[0073] Genetic algorithms find the optimal solution to a problem by simulating the natural evolution process. Unlike other algorithms, the optimization strategy of genetic algorithms is survival of the fittest, which simulates the evolutionary principle of organisms in nature. For the workshops of enterprises, in order to achieve the effect of reducing production costs and logistics costs and creating more profits through production, the layout planning also requires the optimal result, which is extremely consistent with the characteristics of genetic algorithms in terms of purpose.

[0074] Furthermore, the steps of using genetic algorithms to solve problems include encoding initial conditions, setting initial populations, determining fitness functions, designing genetic operations, etc. First, each possible solution is encoded as a "chromosome", and several chromosome individuals constitute a group, which is a set of all possible solutions. After the algorithm starts running, a batch of chromosomes will be randomly generated as the initial solution, and then each individual in the batch will be evaluated according to the predetermined objective function, and a fitness will be obtained. Based on the fitness value, the appropriate chromosome will be selected as the parent to reproduce the next generation. After that, the chromosomes will reorganize the selected chromosomes through genetic operations such as crossover and mutation, and continue to generate new chromosomes, thus continuing to evolve towards the optimal offspring. In the iteration of the algorithm, the crossover operation simulates the gene recombination in the evolution of organisms in nature, so that the offspring can inherit the excellent traits of the parent. The mutation operation simulates gene mutation, introduces new individuals into the algorithm model, can effectively avoid local optimal solutions, and is suitable for studying global problems.

[0075] In comparison, the workshop layout problem is a global problem. Genetic algorithms can perform global optimization in the area to be optimized, provide global optimization for the problem research, and do not have high requirements on the continuity of the problem, and have strong versatility. The iterative mechanism of genetic algorithms can quickly select excellent individuals and use them as a basis for the next optimization search. It is very efficient in solving problems. At the same time, the time required for a single iterative optimization is relatively short. While implementing multi-generation optimization, it can improve work efficiency and ensure the economic benefits of the company's operations.

[0076] Generally speaking, the order of workstations in each production line in a submarine cable production workshop is fixed and cannot be disrupted during optimization. According to the production characteristics of the submarine cable production workshop, the submarine cable starts to be frame-stranded and formed from the frame-stranding machine with the least wire intake. After the current frame-stranding machine is completed, it moves to the next frame-stranding machine with more wire intake to continue processing until the complete conductor is produced from the frame-stranding machine with the largest wire intake. Therefore, the order of the frame-stranding machines in each production line is from the frame-stranding machine with the least wire intake to the frame-stranding machine with the largest wire intake.

[0077] Furthermore, the 15 frame-twisting machines installed in the submarine cable production workshop cannot overlap with each other, and the frame-twisting machines cannot overlap with the temporary storage area for finished coils, so as to avoid producing unrealistic optimization plans; during the optimization process, the frame-twisting machines can only be iteratively optimized within the specified area and cannot exceed the area; there cannot be any intersection between production lines, so as to facilitate the transportation of submarine cable conductors moving within the production line in subsequent production.

[0078] In this application, the establishment and application of the objective function is a core innovation. According to the above-mentioned environmental conditions, the layout of the frame stranding machine in the workshop is iteratively optimized to obtain the optimal solution.

[0079] Specifically, the objective function includes:

[0080] Minimizing the distance from the two temporary storage areas to each frame stranding machine can reflect the shortest time for the AGV to reach the frame stranding machine after picking up the goods from the temporary storage area, which is conducive to reducing logistics costs and time waste. The formula is:

[0081] D ij =min((x i -x j ) 2 +(y i -y j ) 2 )

[0082] Maximizing the space utilization of the layout solution can avoid the situation where the frame stranding machines are placed too densely, resulting in low utilization of the workshop area. The formula is:

[0083] S=(x imax -x imin )*(yimax -y imin ) / (n*s)

[0084] Minimize the distance between adjacent stranding machines in the production line to facilitate the completion of a series of stranding processes for the submarine cable conductor in the production line. The formula is:

[0085] d i =(x i -x i+1 ) 2 +(y i -y i+1 ) 2

[0086] The above multiple objective functions are converted into a single objective function, and different weights are assigned to each objective function, namely α, β, and γ; the weight value α of the distance from the minimum buffer area to the frame stranding machine is set to 0.4, so that D ij As the denominator, the maximum space utilization weight value β is set to 0.3, and the space utilization is multiplied by it; the minimum adjacent frame stranding machine distance weight value γ is set to 0.3, so that d i As the denominator, we get the weighted formula:

[0087]

[0088] This shortens the handling time of the copper wire reels and maximizes space utilization.

[0089] Furthermore, it can be seen from the output layout diagram that the position of the frame stranding machine has changed compared to the original layout. In order to verify the feasibility of the optimization result, the layout model is re-established in Plant Simulation according to the optimal layout diagram and a simulation is performed.

[0090] For the layout optimization model, it mainly includes the reel pickup area, the transportation scheduling area, the frame twisting unloading area and the system control area; the reel pickup area contains two wire drawing reel production lines, and the copper wire flows directly from the material source through the wire drawing station processing to the copper wire reel temporary storage area, so that the AGV can directly load the copper wire reel from the temporary storage area; the transportation scheduling area includes the AGV track in the workshop and the AGV performing transportation work on the track, as well as the trolley source for generating AGV and the material termination unit for recycling AGV; the frame twisting unloading area contains three frame twisting machine production lines, and the copper wire reels transported by the AGV from the reel temporary storage area will be unloaded in the material area corresponding to each frame twisting machine to supply the startup requirements of the frame twisting machine; the system control area is used to stack information flow modules and event controllers, so as to facilitate the observation of method calls during simulation operation.

[0091] According to different areas, the frame stranding unloading area uses the buffer zone as the template of the frame stranding machine. According to the initial layout of the frame stranding machine equipment in the workshop, the 15 frame stranding machines of the three production lines are arranged in the model in sequence. The capacity of each buffer zone is set according to the number of copper wire drums actually required for the start-up of each frame stranding machine, and then each frame stranding machine is connected to the material termination unit of the current production line in sequence;

[0092] The handling and scheduling area uses a one-way track as the AGV's operating path, and the AGV can only travel on the track when transporting materials. At the same time, a sensor is set at the point on the track closest to the unloading and picking up areas. The sensor is used to detect the parking position of the AGV in a non-full load state when there are materials in the picking up area, and to detect the parking position of the AGC in a loaded state if the number of copper wire reels in the unloading area does not meet the startup requirements.

[0093] For the system control area, the system control area can be called when the simulation is reset through the event controller. Its function is to close the material outlet of the frame winch in the unloading area, open the material entrance, and close the material entrance for the trolley recovery each time the simulation is reset; it is called when the simulation starts through the event controller. Its function is to assign the 15 frame winch machine objects in the frame winch unloading area to the table data table at the beginning of the simulation, and assign the 2 temporary storage area objects in the tray picking area to the table temporary storage table, so that the objects can be directly read from the table during subsequent calls; when the trolley source starts to output AGV, the system control area turns on the automatic routing function of each AGV, and the AGV with automatic routing function can automatically find the shortest path to the destination when determining the destination; the system control area controls the global variable named Final_Time so that the value of the global variable is equal to the total simulation time, and the simulation time can be intuitively displayed in the window; the system control area can assign evenly distributed initial power values ​​to 5 AGVs.

[0094] On the other hand, the following tables are also set up in the system control area: a data table and a temporary storage table; the data table is used to store the names of the 15 frame twisting machine objects required by the Init access table, namely buffer zones 1 to 15, which is convenient for controlling loading and unloading and setting AGV routes; the temporary storage table is used to store the names of the two temporary storage area objects required by the Init access table, namely temporary storage 1 and temporary storage 2, which is convenient for calling.

[0095] After setting up the system control area, all non-material and material flow modules required for the entire simulation process are gathered in this area, making the operation process of the simulation construction clear at a glance and not easy to confuse.

[0096] After the simulation, it was learned from the Final_Time that the time required to complete all handling tasks under the optimal layout was 2:40:37. Compared with 3:03:05 before optimization, the handling time was reduced by 22:28. From the simulation results, it can be seen that the time required to meet the conditions for starting the frame stranding machine production line was reduced.

[0097] The optimized simulation model was inserted into a table for statistical analysis. The proportion of blocking time in the temporary storage area was significantly reduced, indicating that the AGV can reach the temporary storage area more timely to take away the copper wire reel compared with before optimization; the AGV order occupancy rate was about 54%, which was slightly lower than before optimization; the time when the order was empty was about 38%, which was higher than before optimization. The total walking distance of the AGV after optimization was 26,711 meters, which was 1,689 meters shorter than before optimization. Combined with the shortened overall simulation time of the optimized model, it shows that the optimization has solved the problem of the long time and distance of the AGV from the pickup area to the unloading area.

[0098] It is particularly noted that the optimization layout method of this application reduces the necessary logistics scheduling time. The location and sequence of the production line stations are optimized, which reduces the time required for each station to start up and improves the balance rate of the production line; it can produce real effects. Through the deep combination of production line simulation technology and algorithms, the simulation results are analyzed and deficiencies are found and improved, making the research closer to actual production.

[0099] Furthermore, this application integrates existing research on algorithms, production line simulation technology and optimization methods, and it can be understood that compared with traditional industrial engineering methods, simulation and optimization methods based on computer technology are aimed at more complex production and logistics problems, and are more in line with the needs of modern manufacturing industry to reduce costs and increase efficiency.

[0100] To sum up, the genetic algorithm-based submarine cable production workshop optimization layout method in the embodiment of the present invention is based on the genetic algorithm, makes assumptions on the optimization problem, configures relevant genetic operations and sets the objective function, and can iteratively search for the best in the optimization layout process to obtain the optimal layout plan, and apply the layout plan to the simulation model of PlantSimulation, and compare it with the equipment layout, simulation time, and equipment working information before optimization, thereby reducing the material handling time, reducing the blocking rate of the material temporary storage area, and improving the problem of long time and distance for AGV to unload. Therefore, it is fully suitable for the actual application of complex production relations in the submarine cable production industry, reducing the production cost of submarine cable production workshops, and meeting the needs of modern manufacturing industry to reduce costs and increase efficiency.

[0101] The above specific implementation manner cannot be used as a limitation on the protection scope of the present invention. For those skilled in the art, any substitution, improvement or change made to the implementation manner of the present invention falls within the protection scope of the present invention.

[0102] The matters not described in detail in the present invention are all known technologies to those skilled in the art.

Claims

1. A method for optimizing the layout of a submarine cable production workshop based on a genetic algorithm, characterized in that: The layout optimization method comprises the following steps: S1, creating an initial population in a submarine cable production workshop, wherein each individual of the initial population represents a layout scheme, and randomly placing the equipment at a certain position in the workshop according to the layout scheme; S2, calculate the fitness of each individual in the initial population, establish the objective function according to the optimization goal, and calculate the function value to evaluate the optimization plan; S3, increases the probability of individuals with high fitness being selected, randomly selects two parent individuals for crossover operation, and mutates the individuals in the population according to the set mutation rate; S4, run the corresponding program in Matlab, iterate multiple times and calculate the fitness of each generation, output the optimal offspring, and obtain the optimal layout diagram; S5, establishing a layout optimization model in Plant Simulation according to the outputted optimal layout diagram and performing simulation; S6, insert the layout optimization model into the table for statistical analysis, and optimize the problem of long time and distance for AGV to go from the pallet pickup area to the frame unloading area.

2. The method for optimizing layout of submarine cable production workshop based on genetic algorithm according to claim 1, characterized in that: In the submarine cable production workshop, the order of workstations in each production line is fixed and cannot be disrupted during optimization. According to the production characteristics of the submarine cable production workshop, the submarine cable starts to be frame-stranded and formed from the frame-stranding machine with the least wire intake. After the current frame-stranding machine is completed, it moves to the next frame-stranding machine with more wire intake to continue processing until the complete conductor is produced from the frame-stranding machine with the largest wire intake. Therefore, the order of the frame-stranding machines in each production line is from the frame-stranding machine with the least wire intake to the frame-stranding machine with the largest wire intake.

3. The method for optimizing layout of submarine cable production workshop based on genetic algorithm according to claim 2, characterized in that: The frame stranding machines cannot overlap with each other, and the frame stranding machines cannot overlap with the temporary storage area for the coiled cables, so as to avoid producing unrealistic optimization schemes; during the optimization process, the frame stranding machines can only be iteratively optimized within the specified area and cannot exceed the area; the production lines cannot cross each other, so as to facilitate the transportation of the submarine cable conductors moving within the production line in subsequent production.

4. The method for optimizing layout of submarine cable production workshop based on genetic algorithm according to claim 1, characterized in that: The objective function includes: Minimizing the distance from the two temporary storage areas to each frame stranding machine can reflect the shortest time for the AGV to reach the frame stranding machine after picking up the goods from the temporary storage area, which is conducive to reducing logistics costs and time waste. The formula is: D ij =min((x i -x j ) 2 +(y i -y j ) 2 ) Maximizing the space utilization of the layout solution can avoid the situation where the frame stranding machines are placed too densely, resulting in low utilization of the workshop area. The formula is: S=(x imax -x imin )*(y imax -y imin ) / (n*s) Minimize the distance between adjacent stranding machines in the production line to facilitate the completion of a series of stranding processes for the submarine cable conductor in the production line. The formula is: d i =(x i -x i+1 ) 2 +(y i -y i+1 ) 2 The above multiple objective functions are converted into a single objective function, and different weights are assigned to each objective function, namely α, β, and γ; the weight value α of the distance from the minimum buffer area to the frame stranding machine is set to 0.4, so that D ij As the denominator, the maximum space utilization weight value β is set to 0.3, and the space utilization is multiplied by it; the minimum adjacent frame stranding machine distance weight value γ is set to 0.3, so that d i As the denominator, we get the weighted formula: This shortens the handling time of the copper wire reels and maximizes space utilization.

5. The method for optimizing layout of submarine cable production workshop based on genetic algorithm according to claim 1, characterized in that: The layout optimization model includes a reel pickup area, a transportation scheduling area, a frame twisting unloading area and a system control area; the reel pickup area includes two wire drawing reel production lines, and the copper wire flows directly from the material source to the copper wire reel temporary storage area through the wire drawing station processing, so that the AGV can directly load the copper wire reel from the temporary storage area; the transportation scheduling area includes the AGV track in the workshop and the AGV performing transportation work on the track, as well as the trolley source for generating AGVs and the material termination unit for recycling AGVs; the frame twisting unloading area includes three frame twisting machine production lines, and the copper wire reels transported by the AGV from the reel temporary storage area will be unloaded in the material area corresponding to each frame twisting machine to supply the startup requirements of the frame twisting machine; the system control area is used to stack information flow modules and event controllers, so as to facilitate the observation of method calls during simulation operation.

6. The method for optimizing layout of submarine cable production workshop based on genetic algorithm according to claim 5, characterized in that: The stranding unloading area uses the buffer zone as a template for the stranding machine. According to the initial layout of the stranding machine equipment in the workshop, the 15 stranding machines of the three production lines are arranged in the model in sequence. The capacity of each buffer zone is set according to the number of copper wire drums actually required for the start-up of each stranding machine, and then each stranding machine is connected to the material termination unit of the current production line in sequence. The handling and scheduling area uses a one-way track as the AGV's operating path, and the AGV can only travel on the track when transporting materials. At the same time, a sensor is set at the point on the track closest to the unloading and picking up areas. The sensor is used to detect the parking position of the AGV in a non-full load state when there are materials in the picking up area, and to detect the parking position of the AGC in a loaded state if the number of copper wire reels in the unloading area does not meet the startup requirements.

7. The method for optimizing layout of submarine cable production workshops based on genetic algorithm according to claim 5, characterized in that: The system control area is called when the simulation is reset through the event controller, and its function is to close the material outlet of the frame winch in the unloading area, open the material inlet, and close the material inlet for the trolley recovery each time the simulation is reset; it is called when the simulation starts through the event controller, and its function is to assign the 15 frame winch objects in the frame winch unloading area to the table data table at the beginning of the simulation, and assign the 2 temporary storage area objects in the tray pickup area to the table temporary storage table, so that the objects can be directly read from the table when calling later; When the vehicle source starts to output the AGV, the system control area turns on the automatic routing function of each AGV. The AGV with the automatic routing function can automatically find the shortest path to the destination when determining the destination; The system control area controls a global variable named Final_Time so that the value of the global variable is equal to the total simulation time, and the simulation time can be displayed intuitively in the window; the system control area can assign uniformly distributed initial power values ​​to the five AGVs.

8. The method for optimizing layout of submarine cable production workshops based on genetic algorithm according to claim 5, characterized in that: After the layout optimization model is established, the simulation is started using the event manager until the transport task is completed and the AGV is recovered.

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