PFEP data maintenance and production line planning optimization method based on logistics simulation

Abstract

The invention discloses a PFEP data maintenance and production line planning optimization method based on logistics simulation, and belongs to the technical field of production line logistics, and the method comprises the steps: making a PFEP form and a layout planning scheme of a new production line, and carrying out the collection and filling based on the existing production line data; importing the two-dimensional layout diagram of the new production line and the three-dimensional model of the material equipment into simulation software, and constructing an equal-proportion simulation model consistent with the planning scheme; performing parameter setting on the simulation model by taking the PFEP data and the business process as simulation input, and debugging, running and outputting a simulation result; the initial PFEP data are perfected in combination with the simulation result, whether the simulation result meets the new production line planning target requirement or not is judged, if yes, an optimal production line scheme is output, and if not, an optimization coefficient is added to the perfected PFEP data to serve as simulation model secondary input for loop iteration optimization simulation till the optimal production line scheme is output. The method has the advantages that automatic discrimination and improvement of the PFEP data and loop iteration optimization of the production line planning scheme are realized.

Description

Technical Field

[0001] This invention relates to the field of production line logistics technology, specifically to a method for maintaining PFEP data and optimizing production line planning based on logistics simulation. Background Technology

[0002] Product flow is like the circulatory system in the manufacturing process, ensuring that materials can flow through each process on time, in the right amount, and at the right point. It is a key factor in ensuring stable and continuous production on the workshop production line.

[0003] PFEP (PlanForEveryPart) is a standardized document that centrally manages the basic attributes of materials (length, width, height, weight, etc.) and logistics attributes (delivery method, buffer quantity, etc.). It covers basic material data as well as logistics data from various stages such as procurement, transportation, factory entry, warehousing, outbound delivery, distribution, and processing. It is an important data source for improving supply chain and workshop production line planning.

[0004] Regarding PFEP data maintenance, a small portion of the data can be directly retrieved from systems such as MES, WMS, and SAP, or calculated using nested formulas based on other data. However, the supplementation, maintenance, and verification of most PFEP data still rely on manual methods, with dedicated PFEP personnel performing data maintenance periodically. In terms of production line planning, current practices primarily rely on accumulated traditional experience, although some OEMs are gradually adopting logistics simulation to validate planning schemes.

[0005] PFEP data covers both the basic and logistical attributes of materials, involving all aspects from supply chain procurement to warehousing and distribution, and its data structure is exceptionally complex.

[0006] Relying on the traditional "Excel storage + manual maintenance" method is prone to data loss due to the numerous fields and large data volumes, resulting in excessive manual maintenance, untimely maintenance, and data distortion. Even with the "data platform + manual maintenance" approach, where some fields can be directly read from other systems, the data still needs to exist in those systems and be interactive. This portion of data is relatively small, and PFEP data maintenance still relies mainly on manual methods, which easily leads to the recurrence of the aforementioned problems. Over time, PFEP data will lose its integrity, authenticity, and value, severely impacting production line planning based on PFEP data. This will result in significant discrepancies between the implemented plan and the actual plan, ultimately affecting the overall operation and construction of the factory. Summary of the Invention

[0007] The present invention addresses the technical problems mentioned in the background section by providing a method for maintaining PFEP data and optimizing production line planning based on logistics simulation. By using a logistics simulation model, PFEP data is maintained and optimized, resulting in an accurate optimization plan for the production line.

[0008] According to the present invention, the technical solution provided by the present invention is: a method for PFEP data maintenance and production line planning optimization based on logistics simulation, comprising the following steps:

[0009] S1. Create a PFEP form based on the requirements of the new production line, collect and fill in the form based on the existing production line data, and develop a layout plan for the new production line.

[0010] S2. Based on the new production line planning scheme, select appropriate logistics simulation software, import the two-dimensional layout diagram and the three-dimensional model of materials and equipment into the simulation software, and construct a proportional simulation model consistent with the planning scheme.

[0011] S3. Based on the new production line planning scheme and PFEP data, set the three-dimensional model using relevant basic data and business processes as simulation parameter inputs;

[0012] S4. Debug and run the simulation model to ensure that the simulation model can run stably and continuously;

[0013] S5. After the simulation model reaches a stable state, run multiple simulation cycles continuously and output and save the simulation results;

[0014] S6. Automatically identify and improve the initial PFEP form based on the output simulation results.

[0015] Furthermore, in S6, methods for improving PFEP data include:

[0016] S61. Import the simulation results into the PFEP data platform, remove outliers, and then average the simulation results. With accuracy calculate;

[0017] S62, with accuracy The simulation value closest to 1 is used as the simulation result data. If the PFEP data is consistent with the simulation result data, it remains unchanged.

[0018] If the two data are inconsistent but one of them is empty, the PFEP data shall be based on the existing data.

[0019] If the two data are inconsistent and neither is empty, the judgment is first made based on the threshold range or calculation formula provided by the PFEP data platform. If only one of them conforms to the threshold range or calculation formula of the data platform, the conforming data shall prevail.

[0020] If both data meet or do not meet the threshold range or calculation formula provided by the PFEP data platform, or if the field data does not have the threshold range or calculation formula provided by the PFEP data platform and cannot be judged, then an error threshold judgment system is established based on the two data to make a secondary judgment.

[0021] S63. Calculate the error threshold range;

[0022] S4. Continue to make a secondary judgment on the PFEP data and simulation results based on the error threshold;

[0023] S65. When neither of the two data sets exceeds the error threshold range, or one exceeds the threshold while the other does not, the PFEP data platform automatically selects the data to fill in. When both data sets exceed the error threshold range, the simulation results and initial data are fed back to the PFEP maintenance personnel for manual judgment and selection. Based on this rule, the entire PFEP data is supplemented and maintained.

[0024] Furthermore, it also includes:

[0025] S7. Further analyze the simulation results of the program target parameters of the new production line. If the simulation results meet the planning requirements, the improved PFEP data and the production line planning scheme are the optimal production line scheme. If the simulation results do not meet the program planning requirements, the improved PFEP data are used as the secondary input of the simulation model. At the same time, a hierarchical optimization coefficient is added to the column data related to the program target for iterative simulation.

[0026] Furthermore, S1, based on the requirements of the new production line, create a PFEP form, collect and fill in the form based on the existing production line data, and formulate a new production line layout plan.

[0027] S11. Based on the planning and construction requirements of the new production line, develop a PFEP form with detailed field names, and collect and fill in the form based on the material number as the identifier of the existing production line data. Store and maintain the form through an Excel spreadsheet or data platform.

[0028] S12. Based on the collected PFEP data and planning objectives, a preliminary layout plan for the new production line is formulated.

[0029] Furthermore, in S2, importing the two-dimensional layout diagram and the three-dimensional models of materials and equipment into the simulation software to construct a scaled simulation model consistent with the planning scheme means: importing the two-dimensional layout diagram and the three-dimensional models of materials and equipment into the simulation software, using the two-dimensional layout diagram as the base map, placing the three-dimensional models of equipment according to the layout diagram, and naming the three-dimensional models of materials in the material number format and placing them in the initial library, and finally outputting a scaled simulation model consistent with the planning scheme.

[0030] Furthermore, S3, based on the new production line planning scheme and the collected PFEP data, uses relevant basic data and business processes as simulation parameter inputs to set the 3D model; some parameters use function formulas, probability distributions, etc. to improve the confidence of the simulation model.

[0031] Furthermore, in step S61, the simulation results are imported into the PFEP data platform, outliers are removed, and the simulation results are averaged. With accuracy calculate;

[0032] If the initial PFEP data is in Excel format, then the PFEP data can be associated with the simulation results using functions or macros.

[0033] If the initial PFEP data is managed in a data platform manner, the simulation results are traversed and searched using the material number as the primary identifier and the field name as the secondary identifier.

[0034] If the initial PFEP data read is The simulation results output after m simulation cycles are as follows: , … First, outliers in the simulation are detected and removed using the IQR method. After removal, n simulation results remain as follows: , … Then, the remaining simulation results are averaged. With accuracy calculate:

[0035] Mean: ;

[0036] Accuracy: ;

[0037] In the formula, To find a1, a2...a n The average value; This represents the simulation result value currently being calculated; and These are the maximum and minimum values ​​among the n remaining simulation results after removing abnormal simulation results.

[0038] Furthermore, S63, calculate the error threshold range; the formula is as follows:

[0039] Standard deviation: = ;

[0040] Error threshold range: ;

[0041] In the formula, To find a1, a2...a n standard deviation To find a1, a2...a n The average value, This is the initial PFEP data.

[0042] Furthermore, in S7, if the simulation results do not meet the requirements of the program planning, the improved PFEP data is used as the secondary input to the simulation model. At the same time, a hierarchical optimization coefficient is added to the column data related to the program objectives for iterative simulation, as follows:

[0043] Suppose there are m guiding objectives in the production line planning process, and their objectives are as follows: , … The corresponding average simulation results are as follows: , … Regarding the simulation results If n columns of data in the PFEP form affect the simulation results output, then the relevant column data will influence the simulation results. The impact result can be written as:

[0044] ;

[0045] in, For constant terms, To optimize the coefficient matrix, These are data columns related to the simulation results;

[0046] First, calculate each relevant data column. For the target result The influence coefficient, if the values ​​of the parameters in the second to nth columns remain unchanged, assuming that the parameters in the first column are negatively correlated with the target result, the data in the first column... Value multiplied by the coefficient of variation Simulation results The change is Then its influence coefficient on the target result is Assuming the parameters in the first column are positively correlated with the target result, the data in the first column... Multiply by the coefficient of variation Simulation results The change is Then its influence coefficient on the target result is ;

[0047] Further calculations are performed on each relevant data column. For the target result Contribution coefficient:

[0048] ;

[0049] In the formula, , The sum of the influence coefficients of all input columns related to the result column.

[0050] Further calculate the optimization coefficients for each relevant data column:

[0051] ;

[0052] In the formula, S is the simulation result value, T is the target result value, and E is the target result value. i is the contribution coefficient of the i-th relevant input column to the target result.

[0053] Based on this method, optimization coefficients are added to the parameters of data columns related to the goals of the planning outline. The PFEP data with added optimization coefficients was then used as the secondary input for a second simulation analysis.

[0054] If the results of the secondary simulation meet the planning objectives, the results will be linked to the PFEP table or data platform according to the S6 operation, and used as the final PFEP data and planning scheme.

[0055] Furthermore, if the results of the second simulation still do not meet the planning objectives, repeat steps S6 and S7 above, update and improve the PFEP data and optimization coefficients, and perform iterative optimization simulations until the simulation results meet the planning objectives.

[0056] The advantages of this invention compared to existing technologies are as follows: 1. It proposes a method that uses the material number as the primary identifier and the field name as the secondary identifier to correlate simulation result data with PFEP data, and establishes an accuracy and threshold range evaluation model to achieve automatic identification and supplementary maintenance of PFEP data, thereby improving the authenticity and integrity of PFEP data and significantly reducing the workload of maintenance personnel; 2. It proposes a method for calculating the PFEP data iterative optimization coefficient based on the program objectives, which can realize rapid quantitative calculation of PFEP data related to the program objectives, providing data support for subsequent production line optimization and improvement; 3. It proposes a method for iteratively optimizing production line planning schemes based on simulation models and PFEP data, which can simultaneously optimize PFEP data and production line planning schemes based on program objectives, effectively improving the authenticity of PFEP data and the rationality of planning schemes.

[0057] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the PFEP data maintenance and production line planning optimization method based on logistics simulation according to an embodiment of the present invention;

[0059] Figure 2 This is a schematic diagram of the PFEP data platform according to an embodiment of the present invention;

[0060] Figure 3 This is a schematic diagram of the PFEP data supplementation and maintenance process according to an embodiment of the present invention;

[0061] Figure 4 This is a schematic diagram illustrating the process of using Dassault Systèmes 3DE for logistics simulation and production line optimization in an embodiment of the present invention. Detailed Implementation

[0062] The present invention will now be described in further detail.

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments will be clearly and completely described below with reference to the accompanying drawings. The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0064] Combination Figure 2 As shown, this embodiment provides a method for maintaining PFEP data based on logistics simulation, and for subsequent production line planning optimization based on the simulation model and PFEP data.

[0065] Logistics simulation is conducted by using initial PFEP data as parameter input to a proportional simulation model of the planned production line. The simulation results are correlated with PFEP fields using material numbers and field names as identifiers, and an error threshold early warning mechanism is established to achieve automatic replenishment and rapid maintenance of PFEP data. Simultaneously, if the planned production line results do not meet the program objectives, the improved PFEP data is used as a secondary input to the simulation model, and optimization coefficients are added to the relevant columns of the program objectives for iterative simulation to find the optimal production line planning scheme.

[0066] The flowchart of the PFEP data maintenance and production line planning optimization method based on logistics simulation in this embodiment is as follows:

[0067] (1) Combination Figure 1 As shown, based on the planning and construction requirements of the new production line, a PFEP form with detailed field names was developed. The form was collected and filled in using the material number as an identifier based on the existing production line data, and stored and maintained through an Excel spreadsheet or data platform.

[0068] (2) Based on the collected PFEP data and planning objectives, a preliminary new production line layout plan was formulated.

[0069] (3) Select appropriate logistics simulation software based on the new production line planning scheme, import the two-dimensional layout diagram and the three-dimensional models of materials and equipment into the simulation software, use the two-dimensional layout diagram as the base map, place the three-dimensional models of equipment according to the layout diagram, and place the three-dimensional models of materials in the initial library with the material number as the name. Finally, output a proportional simulation model consistent with the planning scheme.

[0070] (4) Based on the new production line planning scheme and the collected PFEP data, the three-dimensional model is set using relevant basic data and business processes as simulation parameters. Some parameters can be improved by using function formulas, probability distributions, etc. to enhance the confidence of the simulation model.

[0071] (5) After all parameters are set, the simulation model is debugged and run to ensure that the simulation model can run stably and continuously.

[0072] (6) Combination Figure 3 As shown, after the simulation model reaches a stable state, multiple simulation cycles are run continuously and the simulation results are output and saved.

[0073] If the initial PFEP data is in Excel format, then the PFEP data can be associated with the simulation results using functions or macros.

[0074] If PFEP data is managed using a data platform, the simulation results are traversed and searched using the material number as the primary identifier and the field name as the secondary identifier.

[0075] If the initial PFEP data is The simulation results output after m simulation cycles are as follows: , … First, outliers in the simulation are detected and removed using the IQR method. After removal, n simulation results remain as follows: , … Then, the remaining simulation results are averaged. With accuracy calculate:

[0076] Mean: ;

[0077] Accuracy: ;

[0078] In the formula, In order to obtain , … The average value; This represents the simulation result value currently being calculated; and These are the maximum and minimum values ​​among the n remaining simulation results after removing abnormal simulation results.

[0079] In terms of accuracy Closest to 1 The values ​​are used as simulation result data. If the PFEP data is consistent with the simulation result data, they remain unchanged. If the two data are inconsistent but one of them is empty, the PFEP data with existing data shall prevail. If the two data are inconsistent and neither is empty, the judgment is first made based on the threshold range or calculation formula built into the PFEP data platform. If one meets the criteria and the other does not, the data with the criteria shall prevail. If both meet the criteria or neither meets the criteria, or if the field data does not have a built-in threshold / formula and cannot be judged, an error threshold judgment system is established based on the two data to make a secondary judgment.

[0080] Standard deviation: = ;

[0081] Error threshold range: ;

[0082] When neither set of data exceeds the error threshold, or one exceeds the threshold while the other does not, the PFEP data platform automatically selects the appropriate data for filling. When both sets of data exceed the error threshold, the simulation results will be... Compared with initial data The feedback is sent to the PFEP maintenance personnel, who then make a manual judgment and selection based on this rule to supplement and maintain the entire PFEP data.

[0083] (7) Combination Figure 3 As shown, the simulation results of the program objectives are further analyzed. If the simulation results meet the planning requirements, the improved PFEP data and production line planning scheme are the final scheme. If the simulation results do not meet the program planning requirements, the improved PFEP data is used as the secondary input of the simulation model, and a hierarchical optimization coefficient is added to the column data related to the program objectives for iterative simulation.

[0084] If there are m guiding objectives in the production line planning process, their objective results are as follows: , … The corresponding average simulation results are as follows: , … Regarding the simulation results The N columns of data in the PFEP form will affect the simulation results output, as shown in the table below:

[0085]

[0086] The relevant column data is relevant to the simulation results. The impact result can be written as:

[0087] ;

[0088] in, For constant terms, To optimize the coefficient matrix, ;

[0089] First, each column of parameters is calculated based on simulation. For the target result The influence coefficient, if the values ​​of the parameters in the second to Nth columns remain unchanged, assuming that the parameters in the first column are negatively correlated with the target result, their values ​​are... Multiply by the coefficient of variation Simulation results The change is Then its influence coefficient on the target result is ;

[0090] Assuming the first column of parameters is positively correlated with the target result, its numerical column... Multiply by the coefficient of variation Simulation results The change is Then its influence coefficient on the target result is ;

[0091] Further calculation of parameters in each column The contribution coefficient to the target result Si:

[0092] ;

[0093] Further calculate the optimization coefficients for each column of parameters:

[0094] ;

[0095] Based on this method, optimization coefficients are added to the parameters of data columns related to the goals of the planning outline. The PFEP data with added optimization coefficients was then used as the secondary input for a second simulation analysis.

[0096] If the results of the second simulation meet the planning objectives, the results of the second simulation will be linked to the PFEP table or data platform according to the S6 operation, and used as the final version of PFEP data and planning scheme.

[0097] If the results of the second simulation still do not meet the planning objectives, repeat steps (6) and (7) above, update the PFEP data and optimization coefficients, and perform iterative optimization simulations until the simulation results meet the planning objectives.

[0098] The above-mentioned method of using logistics simulation to supplement and maintain PFEP data and optimize production line planning can be applied to multiple stages such as early planning and construction of production lines, mid-to-late stage upgrades and renovations, or daily operation data maintenance. It can significantly reduce the workload of PFEP maintenance personnel, improve the authenticity and integrity of data, and at the same time enhance the rationality of production line planning schemes, reduce production line operation risks and trial and error costs.

[0099] The following examples further illustrate this point:

[0100] A company, facing insufficient capacity in its existing production lines, plans to build a new automated assembly line for the production of large-tonnage products. Based on its existing product lines, the company has initially completed the PFEP field setup and data collection, and has placed it under the MES system as a functional module for platform-based management.

[0101] Combination Figure 2 As shown, the PFEP data specifically includes six major categories and more than 40 columns: material basic information, assembly and processing information, packaging carrier information, warehousing and caching information, delivery information, and other information. However, due to the lack of data maintenance in the early stages of the product, coupled with the conversion of some workstations to automated operations, there are a large number of missing and distorted data in the PFEP data, such as the maximum cache size in the warehousing and caching information and the delivery time in the delivery information.

[0102] Based on the PFEP data and past experience, the technical upgrade team has initially planned a new production line construction scheme. However, there are still concerns about the production line capacity and layout. They plan to further improve the PFEP data and production line planning scheme through logistics simulation.

[0103] Currently, common logistics simulation software includes Flexsim, Siemens Plant Simulation, and Dassault 3DE. Based on the production line planning requirements, the technical upgrade team ultimately selected Dassault 3DE for PFEP data maintenance and solution optimization. The new production line is planned to have 20 assembly stations, all using AGVs to transport materials from the warehouse to the line for assembly. The planned target is a monthly production capacity of over 30 units.

[0104] Combination Figure 2 , Figure 4 As shown, firstly, the two-dimensional layout diagram and three-dimensional model of the new production line are imported into the 3DE simulation software. The two-dimensional layout diagram is in dwg format, and the three-dimensional model is in stp or step format. The three-dimensional model specifically includes: material product model, assembly equipment model, tooling model, logistics equipment model, etc. The material product model is named according to the material number, and the assembly equipment model is placed according to the two-dimensional layout diagram. The output is a scaled simulation model consistent with the planning scheme.

[0105] Furthermore, the simulation model is defined by resources, such as defining the material product model as a physical product, the assembly equipment model as an NC machine, the tooling model as a storage device, and the logistics equipment model as a transmission device. After the definition is completed, based on the collected PFEP data and production line planning scheme, the basic parameters of the simulation model are set, including assembly cycle time, loading and unloading time, and AGV loading capacity. For business processes such as unpacking and palletizing, settings are made through simulation modules such as mobile storage and packing / unpacking. For AGV transfer aspects such as obstacle avoidance and charging, settings are made through simulation modules such as declaration groups and power consumption. At the same time, to further improve the confidence of the simulation model, all time-related parameters are set using functions, and variable parameters such as failure rate are added to the assembly equipment and logistics equipment to closely resemble actual usage scenarios.

[0106] Furthermore, after all parameters are set, the simulation model is run to handle and debug abnormal situations such as collisions and production stoppages, ensuring that the simulation model can run stably and continuously according to the planned scheme. One month is used as one simulation cycle. After the simulation model stabilizes, it is run at double speed for 5 months, and 5 sets of simulation result data are output and saved in Excel format.

[0107] Furthermore, the five sets of simulation result data were imported into the PFEP data platform. Using the material number as the primary identifier and the field name as the secondary identifier, the simulation results were traversed and searched. The initial PFEP data included the line-side buffer quantity for material A. =4, the data platform's built-in threshold range is [1, 6], and the 5 sets of simulation results are: 5, 5, 5, 5, 6. First, based on the IQR judgment method, these 5 sets of simulation results have no outliers and are all within the built-in threshold range. Then, the accuracy of each simulation result is calculated:

[0108] The accuracies were 0.8, 0.8, 0.8, 0.8, and 0.2, respectively.

[0109] When the value is 5, the accuracy is 0.8, which is closest to 1. Therefore, with... =5 was used as the simulation result data. The initial data and the simulation result data were inconsistent, and neither was empty, but both were within the built-in threshold range. Therefore, an error threshold range was established based on the two data for secondary judgment:

[0110] Standard deviation: σ = 0.58;

[0111] =4.02;

[0112] =5.18;

[0113] Error threshold range: [4.02, 5.18];

[0114] =4 does not meet the error threshold range. =5 meets the threshold range, so the PFEP data platform automatically reads the value 5 as the line-side buffer quantity field data of material A for correction and filling. Based on this method, the entire PFEP data is automatically updated and maintained, which effectively reduces the workload of PFEP maintenance personnel and improves the accuracy and completeness of the initial data.

[0115] Furthermore, the target parameter of the new production line planning outline is "monthly capacity," with a target value T≥30. However, the simulation result S=28, which does not meet the target requirement. Iterative optimization simulation of the PFEP data and planning scheme is required. Monthly capacity in the PFEP form is mainly dominated by three main data columns: assembly cycle time (min), loading / unloading time (min), and line-side buffer quantity. Therefore, optimization coefficients are added to these three columns for secondary simulation. First, the influence coefficients of each parameter column are calculated. Assembly cycle time and loading / unloading time are negatively correlated with monthly capacity, while line-side buffer quantity is positively correlated. Keeping the other data unchanged, when the assembly cycle time becomes 80% of the original data, the monthly capacity can increase by 30%. Therefore, the influence coefficient of assembly cycle time on monthly capacity is 1.5. Similarly, the influence coefficients of loading / unloading time and line-side buffer quantity on monthly capacity are 0.25 and 0.5, respectively. Further calculation of the contribution coefficients of these three parameters to monthly capacity is then performed. The corresponding contribution coefficients are 0.67, 0.11, and 0.22, respectively. With a planned capacity T=30 and a simulation result S=28, the optimization coefficients for each column are calculated based on the above formulas and positive / negative correlations. The optimization coefficient for the assembly cycle time column is 1-(1-28 / 30)*0.67=0.955, the optimization coefficient for the loading / unloading time column is 0.993, and the optimization coefficient for the line-side buffer quantity column is 1.015. Optimization coefficients are added to these three columns of data, and the PFEP data after adding optimization coefficients is used as the secondary input for simulation analysis.

[0116] Furthermore, repeat the above steps, modifying and improving the PFEP data again based on the secondary simulation results, and analyzing the target "monthly production capacity" data. At this point, the monthly production capacity simulation result S=31, meeting the target requirements. Therefore, the improved PFEP data at this point is used as the final data, and the production line plan at this point is the final implementation plan that meets the planning target. If the secondary simulation result value S is still less than 30, then based on the improved PFEP data, new optimization coefficients are added to the above three columns of relevant data according to the optimization coefficient calculation formula, and iterative simulation is performed until the target requirements are met.

[0117] Based on this approach, simulation models and optimization coefficients can be used for iterative simulation. On the one hand, the PFEP data can be modified and improved multiple times to enhance the data's accuracy and completeness. On the other hand, the optimal production line scheme can be sought based on the guiding objectives to improve the rationality of the logistics planning scheme.

[0118] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for PFEP data maintenance and production line planning optimization based on logistics simulation, characterized in that, Includes the following steps: S1. Create a PFEP form based on the requirements of the new production line, collect and fill in the form based on the existing production line data, and develop a layout plan for the new production line. S2. Based on the new production line planning scheme, select appropriate logistics simulation software, import the two-dimensional layout diagram and the three-dimensional model of materials and equipment into the simulation software, and construct a proportional simulation model consistent with the planning scheme. S3. Based on the new production line planning scheme and PFEP data, set the three-dimensional model using relevant basic data and business processes as simulation parameter inputs; S4. Debug and run the simulation model to ensure that the simulation model can run stably and continuously; S5. After the simulation model reaches a stable state, run multiple simulation cycles continuously and output and save the simulation results; S6. Automatically identify and improve the initial PFEP form based on the output simulation results.

2. The PFEP data maintenance and production line planning optimization method based on logistics simulation according to claim 1, characterized in that, In S6, the methods for refining and judging PFEP data include: S61. Import the simulation results into the PFEP data platform, remove outliers, and then average the simulation results. With accuracy calculate; S62, with accuracy The simulation value closest to 1 is used as the simulation result data. If the PFEP data is consistent with the simulation result data, it remains unchanged. If the two data are inconsistent but one of them is empty, the PFEP data shall be based on the existing data. If the two data are inconsistent and neither is empty, the judgment is first made based on the threshold range or calculation formula provided by the PFEP data platform. If only one of them conforms to the threshold range or calculation formula of the data platform, the conforming data shall prevail. If both data meet or do not meet the threshold range or calculation formula provided by the PFEP data platform, or if the field data does not have the threshold range or calculation formula provided by the PFEP data platform and cannot be judged, then an error threshold judgment system is established based on the two data to make a secondary judgment. S63. Calculate the error threshold range; S4. Continue to make a secondary judgment on the PFEP data and simulation results based on the error threshold; S65. When neither of the two data sets exceeds the error threshold range, or one exceeds the threshold while the other does not, the PFEP data platform automatically selects the data to fill in. When both data sets exceed the error threshold range, the simulation results and initial data are fed back to the PFEP maintenance personnel for manual judgment and selection. Based on this rule, the entire PFEP data is supplemented and maintained.

3. The PFEP data maintenance and production line planning optimization method based on logistics simulation according to claim 1, characterized in that, Also includes: S7. Further analyze the simulation results of the program target parameters of the new production line. If the simulation results meet the planning requirements, the improved PFEP data and the production line planning scheme are the optimal production line scheme. If the simulation results do not meet the program planning requirements, the improved PFEP data are used as the secondary input of the simulation model. At the same time, a hierarchical optimization coefficient is added to the column data related to the program target for iterative simulation.

4. The PFEP data maintenance and production line planning optimization method based on logistics simulation according to claim 1, characterized in that: S1. Create a PFEP form based on the requirements of the new production line, collect and fill in the form based on the existing production line data, and develop a layout plan for the new production line. S11. Based on the planning and construction requirements of the new production line, develop a PFEP form with detailed field names, and collect and fill in the form based on the material number as the identifier of the existing production line data. Store and maintain the form through an Excel spreadsheet or data platform. S12. Based on the collected PFEP data and planning objectives, a preliminary layout plan for the new production line is formulated.

5. The PFEP data maintenance and production line planning optimization method based on logistics simulation according to claim 1, characterized in that: In S2, importing the two-dimensional layout diagram and the three-dimensional models of materials and equipment into the simulation software to construct a scaled simulation model consistent with the planning scheme means: importing the two-dimensional layout diagram and the three-dimensional models of materials and equipment into the simulation software, using the two-dimensional layout diagram as the base map, placing the three-dimensional models of equipment according to the layout diagram, and naming the three-dimensional models of materials in the material number format and placing them in the initial library, and finally outputting a scaled simulation model consistent with the planning scheme.

6. The PFEP data maintenance and production line planning optimization method based on logistics simulation according to claim 1, characterized in that: S3. Based on the new production line planning scheme and PFEP data, the 3D model is set with relevant basic data and business processes as simulation parameters; some parameters are set using function formulas, probability distributions and other methods to improve the confidence of the simulation model.

7. The PFEP data maintenance and production line planning optimization method based on logistics simulation according to claim 2, characterized in that: S61. Import the simulation results into the PFEP data platform, remove outliers, and then average the simulation results. With accuracy calculate; If the initial PFEP data is in Excel format, then the PFEP data can be associated with the simulation results using functions or macros. If the initial PFEP data is managed in a data platform manner, the simulation results are traversed and searched using the material number as the primary identifier and the field name as the secondary identifier. If the initial PFEP data read is The simulation results output after m simulation cycles are as follows: , … First, outliers in the simulation are detected and removed using the IQR method. After removal, n simulation results remain as follows: , … Then, the remaining simulation results are averaged. With accuracy calculate: Mean: ; Accuracy: ; In the formula, To find a1, a2...a n The average value; This represents the simulation result value currently being calculated; and These are the maximum and minimum values ​​among the n remaining simulation results after removing abnormal simulation results.

8. The PFEP data maintenance and production line planning optimization method based on logistics simulation according to claim 2, characterized in that: S63. Calculate the error threshold range; the formula is as follows: Standard deviation: = ; Error threshold range: ; In the formula, To find a1, a2...a n standard deviation To find a1, a2...a n The average value, This is the initial PFEP data.

9. The PFEP data maintenance and production line planning optimization method based on logistics simulation according to claim 3, characterized in that, In S7, if the simulation results do not meet the requirements of the program plan, the improved PFEP data is used as the secondary input to the simulation model. Simultaneously, a hierarchical optimization coefficient is added to the column data related to the program objectives for iterative simulation. The method is as follows: Suppose there are m guiding objectives in the production line planning process, and their objectives are as follows: , … The corresponding average simulation results are as follows: , … Regarding the simulation results If there are n columns of data in the PFEP form that affect the simulation results output, then the relevant column data will affect the simulation results. The impact result can be written as: ； in, For constant terms, To optimize the coefficient matrix, These are data columns related to the simulation results; First, calculate each relevant data column. For the target result The influence coefficient, if the values ​​of the parameters in the second to nth columns remain unchanged, assuming that the parameters in the first column are negatively correlated with the target result, the data in the first column... Value multiplied by the coefficient of variation Simulation results The change is Then its influence coefficient on the target result is Assuming the parameters in the first column are positively correlated with the target result, the data in the first column... Multiply by the coefficient of variation Simulation results The change is Then its influence coefficient on the target result is ; Further calculations are performed on each relevant data column. For the target result Contribution coefficient: ； In the formula, , The sum of the influence coefficients of all input columns related to the result column; Further calculate the optimization coefficients for each relevant data column: ； In the formula, S is the simulation result value, T is the target result value, and E is the target result value. i Let be the contribution coefficient of the i-th relevant input column to the target result; Based on this method, optimization coefficients are added to the parameters of data columns related to the goals of the planning outline. The PFEP data with added optimization coefficients was then used as the secondary input for a second simulation analysis. If the results of the secondary simulation meet the planning objectives, the results will be linked to the PFEP table or data platform according to the S6 operation, and used as the basis for improving the PFEP data and the optimal planning scheme.

10. The PFEP data maintenance and production line planning optimization method based on logistics simulation according to claim 9, characterized in that: If the results of the second simulation still do not meet the planning objectives, repeat steps S6 and S7 above, update and improve the PFEP data and optimization coefficients, and perform iterative optimization simulations until the simulation results meet the planning objectives.

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