A multi-process assembly layout optimization method and device

Abstract

The application provides a multi-process assembly layout optimization method and device, and the method comprises the following steps: determining the total number of processes of an assembly process and the observation number of each process; measuring the time of each process according to the observation number of each process to obtain the operation time of each process; using Flexsim simulation software to establish a multi-process production line simulation model, setting basic parameters and operation parameters, running the model and exporting the processing rate and idle rate of each process obtained by simulation, and finding the busiest process, wherein the operation parameters comprise the operation time of each process; analyzing the busiest process to obtain a new process; using the Flexsim simulation software to simulate the new process again until the simulation effect reaches a preset optimization result. The application considers the operation time and adopts the Flexsim simulation software to optimize the assembly layout among the multi-processes, so that the assembly layout among the multi-processes is more reasonable.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of production process optimization, in particular to a multi-process assembly layout optimization method and device. BACKGROUND

[0002] In the current production and processing industry, the production of many devices requires the assembly of multiple components. For example, the assembly of an electric vehicle includes front fork cover and front wheel assembly, head cover assembly, front wall assembly, panel assembly, seat barrel assembly, flat fork guard and rear wheel assembly, vehicle body assembly, and final assembly. The first six processes need to be sent to the final assembly for final assembly. Whether the layout of each process in the assembly workshop is reasonable will affect material transportation efficiency, worker work intensity, and worker production efficiency, thereby affecting the production efficiency of the electric vehicle.

[0003] In the prior art, the assembly layout of multiple processes is performed by technical personnel with rich industry experience, which cannot guarantee the rationality of the assembly layout between multiple processes. Therefore, there is an urgent need for a multi-process assembly layout optimization method. SUMMARY

[0004] To solve the above problems of the prior art, the present application provides a multi-process assembly layout optimization method and device to ensure that the assembly layout between multiple processes is more reasonable.

[0005] To achieve the above purpose, the technical solution adopted by the present application is as follows:

[0006] In a first aspect, the present application provides a multi-process assembly layout optimization method, comprising:

[0007] determining the total number of processes of the assembly process and the observation number of each process;

[0008] measuring the time of each process according to the observation number of each process to obtain the working time of each process;

[0009] using Flexsim simulation software to establish a multi-process production line simulation model, setting basic parameters and working parameters, running the model and exporting the processing rate and idle rate of each process obtained by simulation to find the busiest process, the working parameters including the working time of each process;

[0010] analyzing the busiest process to obtain a new process;

[0011] using Flexsim simulation software to simulate the new process again until the simulation effect reaches the preset optimization result.

[0012] The application has the beneficial effect that: the operation time of each process is obtained by process timing, and is input as an important parameter of a multi-process production line simulation model, process operation simulation is performed by Flexsim simulation software, the busiest process in all processes is obtained, and optimization is performed until the final effect reaches the preset optimization effect, thereby, the operation time is considered, the Flexsim simulation software is used for assembly layout optimization between multi-processes, and the assembly layout between multi-processes is more reasonable.

[0013] Optionally, the determining the total number of processes of the assembly process and the observation number of each process comprises:

[0014] determining the total number K of processes of the assembly process and the trial observation number m;

[0015] performing process timing according to the trial observation number m of each process, to obtain trial observation time X of each process under the trial observation number m;

[0016] substituting the trial observation number m and the trial observation time X into a calculation formula of the actual observation number:

[0017]

[0018] wherein, n j is the actual observation number of the jth process; m is the trial observation number; X i is the ith trial observation time of the jth process; is the average of all trial observation times of the jth process; R is the difference between the maximum X max and the minimum X min of all trial observation times of the jth process; d2 is a preset coefficient corresponding to the trial observation number m;

[0019] taking the maximum of the actual observation numbers of all processes as the observation number n of each process.

[0020] According to the above description, process timing is performed based on a trial observation number, and then the appropriate actual observation number of each process is calculated according to the result of the process timing, so that the observation number is reasonable and accurate.

[0021] Optionally, the process timing according to the observation number of each process to obtain the operation time of each process comprises:

[0022] performing process timing according to the observation number n of each process to obtain n actual observation times Y of each process;

[0023] for each process, calculating the average value and a standard deviation σ, judge whether each actual observation time Y is within , if yes, keep the actual observation time Y, otherwise, eliminate the actual observation time Y;

[0024] For each process, obtain the number of eliminated actual observation times Y, and re-perform process timing a times, and again perform verification until finally n actual observation times Y' are obtained;

[0025] For each process, the average of n actual observation times Y' is taken as the operation time thereof.

[0026] According to the above description, for actual process timing, it is judged whether the obtained actual observation time is reasonable, so that when the data is unreasonable, it is timely eliminated and re-timed, so as to ensure that all measured actual observation times are within a reasonable range, so as to ensure that the final operation time data is more accurate.

[0027] Optionally, the basic parameters include time unit, distance unit and volume unit, and the operation parameters further include the type and quantity of generator transportation and the running time of the model.

[0028] Optionally, the analysis of the most busy process to obtain a new process comprises:

[0029] The ECRS and 5W1H are applied to the most busy process to obtain a new process.

[0030] In a second aspect, the present application provides a multi-process assembly layout optimization device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the following when executing the computer program:

[0031] Determine the total number of processes of the assembly process and the observation number of each process;

[0032] According to the observation number of each process, perform process timing to obtain the operation time of each process;

[0033] Use Flexsim simulation software to establish a multi-process production line simulation model, set basic parameters and operation parameters, run the model and export the processing rate and idle rate of each process obtained by simulation, find the most busy process, and the operation parameters include the operation time of each process;

[0034] Analyze the most busy process to obtain a new process;

[0035] Use Flexsim simulation software to simulate the new process again until the simulation effect reaches the preset optimization result.

[0036] Optionally, the determining the total number of processes of the assembly process and the observation number of each process comprises:

[0037] determining the total number of processes K of the assembly process and the trial observation number m;

[0038] timing the processes according to the trial observation number m of each process to obtain trial observation time X of each process under the trial observation number m;

[0039] substituting the trial observation number m and the trial observation time X into the calculation formula of the actual observation number:

[0040]

[0041] wherein, n j is the actual observation number of the jth process; m is the trial observation number; X i is the ith trial observation time of the jth process; is the average of all trial observation times of the jth process; R is the difference between the maximum X max and the minimum X min of all trial observation times of the jth process; d2 is a preset coefficient corresponding to the trial observation number m;

[0042] taking the maximum of the actual observation numbers of all processes as the observation number n of each process.

[0043] Optionally, the timing the processes according to the observation number of each process to obtain the operation time of each process comprises:

[0044] timing the processes according to the observation number n of each process to obtain n actual observation times Y of each process;

[0045] for each process, calculating the average and the standard deviation σ of the n actual observation times Y, judging whether each actual observation time Y is within , if yes, retaining the actual observation time Y, otherwise, rejecting the actual observation time Y;

[0046] for each process, obtaining the number of rejected actual observation times a of the actual observation times Y, and re-timing the processes a times, and again verifying until finally obtaining n actual observation times Y';

[0047] for each process, taking the average of the n actual observation times Y' as the operation time thereof.

[0048] Optionally, the basic parameters comprise time unit, distance unit and volume unit, and the operation parameters further comprise the type and quantity of generator transportation and the running time of the model.

[0049] Optionally, the analysis of the most busy process obtains a new process, which includes:

[0050] The ECRS and 5W1H are applied to the most busy process to obtain a new process.

[0051] The technical effects of the multi-process assembly layout optimization device provided by the second aspect are referred to the related description of the multi-process assembly layout optimization method provided by the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 The main flowchart of a multi-process assembly layout optimization method of an embodiment of the present application is shown in the figure.

[0053] Figure 2 The simulation model of a multi-process production line before optimization is shown in the figure.

[0054] Figure 3 The setting interface of the operation parameters is shown in the figure.

[0055] Figure 4 The simulation model of a multi-process production line before optimization is shown in the figure.

[0056] Figure 5 The structure diagram of a multi-process assembly layout optimization device of an embodiment of the present application is shown in the figure.

[0057] LEGEND OF THE DRAWINGS

[0058] 1: a multi-process assembly layout optimization device;

[0059] 2: a processor;

[0060] 3: a memory. DETAILED DESCRIPTION

[0061] In order to better understand the above technical solutions, the exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a clearer, more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0062] Embodiment one

[0063] In the embodiment, the assembly workshop of the electric vehicle is optimized, wherein the processes include work units 1 to 7, which are front fork cover and front wheel assembly part, head cover assembly part, front wall assembly part and panel assembly part, seat barrel assembly part, flat fork guard and rear wheel assembly part, vehicle body assembly part and general assembly part, as shown in Figure 2 The assembly layout before optimization is shown, which has low material transportation efficiency, high worker operation intensity and low worker production efficiency, thereby affecting the production efficiency of the electric vehicle. Therefore, the assembly workshop of the electric vehicle is optimized by process timing combined with Flexsim simulation in the embodiment, and the optimized assembly workshop is obtained as shown in Figure 4 The material transportation efficiency is improved, the worker operation intensity is reduced and the worker production efficiency is improved.

[0064] Please refer to Figures 1 to 4 A multi-process assembly layout optimization method, comprising the steps of:

[0065] S1, determining the total number of processes of the assembly process and the observation number of each process;

[0066] The determination of the total number of processes of the assembly process and the observation number of each process comprises:

[0067] S11, determining the total number of processes K of the assembly process and the trial observation number m;

[0068] Therefore, in the embodiment, the total number of processes K is 7 and the trial observation number m is 10.

[0069] S12, timing the process according to the trial observation number m of each process to obtain the trial observation time X of each process under the trial observation number m;

[0070] For the first process, when j = 1, the trial observation times obtained by 10 trial observations are 6, 7, 5, 6, 7, 8, 7, 7, 6 and 7, and the time unit is s.

[0071] S13, substituting the trial observation number m and the trial observation time X into the calculation formula of the actual observation number:

[0072]

[0073] Wherein, n j is the actual observation number of the jth process; m is the trial observation number; X i is the ith trial observation time of the jth process; is the average of all trial observation times of the jth process; R is the difference between the maximum X max and the minimum X min of all trial observation times of the jth process; d2 is a preset coefficient corresponding to the trial observation number m;

[0074] Wherein, according to the observation time of the test, we can get R=X max -X min =8-5=3, according to the observation times m of the test, we can get the d2 value coefficient of 3.078 from the d2 value coefficient table, then n1=[(40*3) / (3.078*6.6)] 2 =34.893, since the observation times is an integer, we get n1=35.

[0075] Similarly, n2=39, n3=11, n4=3, n5=8, n6=17, n7=12.

[0076] S14, take the maximum value of all the actual observation times as the observation times n of each process.

[0077] Thus, take the maximum value of all the actual observation times, i.e. 39 times, as the observation times n of each process.

[0078] S2, according to the observation times of each process, we can get the operation time of each process;

[0079] Wherein, according to the observation times of each process, we can get the operation time of each process, including:

[0080] S21, according to the observation times n of each process, we can get n actual observation times Y of each process;

[0081] Thus, this embodiment needs to measure the operation time of seven processes for 39 times to get 39 actual observation times Y.

[0082] S22, for each process, calculate the average value of n actual observation times Y and the standard deviation σ, judge whether each actual observation time Y is within , if yes, keep the actual observation time Y, otherwise, eliminate the actual observation time Y;

[0083] Wherein, suppose that there are 2 actual observation times not within , then eliminate the 2 actual observation times and keep 37 actual observation times.

[0084] S23, for each process, get the elimination number a of actual observation times Y, and re-measure the operation time a times, and again perform the verification until finally get n actual observation times Y';

[0085] Wherein, the elimination number a is 2, then we need to measure 2 times, and again calculate the average value and the standard deviation and whether within the recalculated The 39 actual observation times Y' are within the range of the 39 theoretical observation times Y.

[0086] S24, for each process, the average of the n actual observation times Y' is taken as the operation time thereof.

[0087] In this embodiment, the operation time is obtained by averaging the 39 actual observation times Y'.

[0088] It should be noted that the process timing in this embodiment is used as an example. In actual electric vehicle assembly process, the timing of each process and the final operation time will be different according to different assembly process and worker proficiency, etc. The actual production conditions are used as the reference.

[0089] S3, a multi-process production line simulation model is established using Flexsim simulation software, basic parameters and operation parameters are set, the model is run and the processing rate and idle rate of each process obtained by simulation are exported, the busiest process is found, and the operation parameters include the operation time of each process;

[0090] The basic parameters include time unit, distance unit and volume unit, and the operation parameters further include the types and quantities of the generators transported and the running time of the model. Figure 3

[0091] Specifically, after the layout is established, the parameters of each operation unit are set. The forklift needs to distribute different types of materials from the warehouse to operation units 1-6, and finally all the materials are distributed to operation unit 7. Therefore, in order to avoid too many temporary entity type values, when distributing from the warehouse to operation units 1-6, each operation unit does not need to distinguish the material types, only needs to distinguish the operation units to which the materials in the warehouse are distributed, and the number of material types distributed to each operation unit is set.

[0092] In this embodiment, it is assumed that the busiest process of the simulation is operation unit 2.

[0093] S4, the busiest process is analyzed to obtain a new process;

[0094] The analysis of the busiest process to obtain a new process includes:

[0095] The ECRS and 5W1H are applied to the busiest process to analyze and obtain a new process.

[0096] The ECRS and 5W1H are applied to operation unit 2, the busiest process, to analyze and obtain a new process.

[0097] ​​Among them, ECRS is the four principles of program analysis in industrial engineering, which is used to optimize the production process to reduce unnecessary process waste and achieve higher production efficiency. They are respectively Eliminate, Combine, Rearrange and Simplify.

[0098] Among them, 5W1H is to ask questions from the six aspects of reason Why, object What, place Where, time When, personnel Who and method How for the selected project, process or operation.

[0099] S5, the new process is simulated again by using Flexsim simulation software until the simulation effect reaches the preset optimization result.

[0100] As shown in Figure 4 The final optimized workshop layout simulation model is obtained, in which the spatial layout of operation units 1 to 7 also changes, and four new logistics warehouse processes are added, forming operation units 8 to 11, which are respectively tire warehouse, electrical device warehouse, black part warehouse and baking paint warehouse.

[0101] Therefore, only in terms of the driving distance of the logistics forklift, the driving distance of the forklift for distributing a batch of materials in the workshop layout simulation model before optimization is 724.23m, and the forklift for distributing a batch of materials after optimization needs to drive 549.98m, that is, the driving distance of the forklift for distributing a batch of materials after optimization is reduced by 174.25m compared with that before optimization, and the transportation efficiency after optimization is improved, and the rest is the same.

[0102] Example two

[0103] Please refer to Figure 5 A multi-process assembly layout optimization device 1, comprising a memory 3, a processor 2 and a computer program stored on the memory 3 and executable on the processor 2, the processor 2 executes the computer program to realize the steps in the above-mentioned embodiment one or two.

[0104] Because the device described in the above-mentioned embodiment of the application is the device used to implement the method of the above-mentioned embodiment of the application, the specific structure and modification of the device can be understood by those skilled in the art based on the method described in the above-mentioned embodiment of the application, so it is not repeated here. Any device used in the method of the above-mentioned embodiment of the application belongs to the scope of protection of the present application.

[0105] Those skilled in the art will appreciate that embodiments of the present application can be devised for a method, an apparatus or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code thereon.

[0106] The present application is described in reference to the flowchart and / or block diagram illustrations of the method, apparatus (device) and computer program product according to embodiments of the present application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions of the flowchart and / or block diagrams.

[0107] It should be noted that the description of the application is not limited to the embodiments described above. It will be apparent to those skilled in the art that various modifications and variations can be made to the specific embodiments without departing from the scope or spirit of the application. One of the skills in the art will understand that in the claims the reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application can be implemented by means of both hardware and software, and any combination thereof. In a claim reciting a means, the term "comprising" is to be interpreted as including the elements or steps recited in the claim, not as excluding the presence of other elements or steps. The word "first", "second", "third", etc. does not imply any order but is used for distinguishing between two or more elements, steps or items. The word "step" can not imply a step of a method but a function or operation.

[0108] In addition, it should be pointed out that in the description of the present specification, the description of the terms "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples" and the like means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any suitable manner in any one or more embodiments or examples. In addition, the skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0109] Although the preferred embodiments of the present application have been described, those skilled in the art can make further changes and modifications to the embodiments after learning the basic inventive concept. Therefore, the claims should be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0110] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A method for optimizing the layout of multi-process assembly, characterized in that, Including the following steps: Determine the total number of assembly process steps and the number of observations for each step; The operation time of each process is obtained by measuring the number of observations for each process. A multi-process production line simulation model was established using Flexsim simulation software. Basic parameters and operation parameters were set, the model was run, and the processing rate and idle rate of each process obtained from the simulation were exported. The busiest process was identified. The operation parameters include the operation time of each process. New processes are derived by analyzing the busiest processes; The new process was simulated again using Flexsim simulation software until the simulation effect reached the preset optimization result. The total number of assembly process steps and the number of observations for each step include: Determine the total number of assembly processes K and the number of trial observations m; The process time is measured according to the number of trial observations m for each process, and the trial observation time X obtained for each process under the number of trial observations m is obtained; Substitute the number of trial observations m and the trial observation time X into the formula for calculating the actual number of observations: Where, n j X represents the actual number of observations for the j-th process; m represents the number of trial observations; X i The time for the i-th trial observation of the j-th process; Let X be the average of all trial observation times for the j-th process; R is the maximum value of all trial observation times for the j-th process. max With minimum value X min The difference between them; d2 is the preset coefficient corresponding to the number of trial observations m; Take the maximum value of the actual number of observations obtained in all processes as the number of observations n for each process; The process time measurement based on the number of observations for each process, to obtain the operation time of each process, includes: The process time is measured according to the number of observations n for each process, and the n actual observation times Y for each process are obtained. For each process step, calculate the average of its n actual observed times Y. And the standard deviation σ, to determine whether each actual observation time Y is within If the actual observation time Y is within ±3σ, then retain the actual observation time Y; otherwise, discard the actual observation time Y. For each process, obtain the number a of the actual observation times Y to be eliminated, and re-perform the process time a times, and verify it again, until the final n actual observation times Y' are obtained; For each process, the average of the n actual observation times Y' is taken as its operation time.

2. The multi-process assembly layout optimization method according to claim 1, characterized in that, The basic parameters include time units, distance units, and volume units. The operational parameters also include the type and quantity of generator transport and the model's running time.

3. A multi-process assembly layout optimization method according to claim 1 or 2, characterized in that, The analysis of the busiest processes to obtain new processes includes: New processes were obtained by applying ECRS and 5W1H to the busiest processes.

4. A multi-process assembly layout optimization device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it performs the following: Determine the total number of assembly process steps and the number of observations for each step; The operation time of each process is obtained by measuring the number of observations for each process. A multi-process production line simulation model was established using Flexsim simulation software. Basic parameters and operation parameters were set, the model was run, and the processing rate and idle rate of each process obtained from the simulation were exported. The busiest process was identified. The operation parameters include the operation time of each process. New processes are derived by analyzing the busiest processes; The new process was simulated again using Flexsim simulation software until the simulation effect reached the preset optimization result. The total number of assembly process steps and the number of observations for each step include: Determine the total number of assembly processes K and the number of trial observations m; The process time is measured according to the number of trial observations m for each process, and the trial observation time X obtained for each process under the number of trial observations m is obtained; Substitute the number of trial observations m and the trial observation time X into the formula for calculating the actual number of observations: Where, n j X represents the actual number of observations for the j-th process; m represents the number of trial observations; X i The time for the i-th trial observation of the j-th process; Let X be the average of all trial observation times for the j-th process; R is the maximum value of all trial observation times for the j-th process. max With minimum value X min The difference between them; d2 is the preset coefficient corresponding to the number of trial observations m; Take the maximum value of the actual number of observations obtained in all processes as the number of observations n for each process; The process time measurement based on the number of observations for each process, to obtain the operation time of each process, includes: The process time is measured according to the number of observations n for each process, and the n actual observation times Y for each process are obtained. For each process step, calculate the average of its n actual observed times Y. And the standard deviation σ, to determine whether each actual observation time Y is within If the actual observation time Y is within ±3σ, then retain the actual observation time Y; otherwise, discard the actual observation time Y. For each process, obtain the number a of the actual observation times Y to be eliminated, and re-perform the process time a times, and verify it again, until the final n actual observation times Y' are obtained; For each process, the average of the n actual observation times Y' is taken as its operation time.

5. The multi-process assembly layout optimization device according to claim 4, characterized in that, The basic parameters include time units, distance units, and volume units. The operational parameters also include the type and quantity of generator transport and the model's running time.

6. A multi-process assembly layout optimization device according to claim 4 or 5, characterized in that, The analysis of the busiest processes to obtain new processes includes: New processes were obtained by applying ECRS and 5W1H to the busiest processes.

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