A data-driven discrete manufacturing workshop layout optimization decision method and system

Abstract

The present invention provides a data-driven discrete manufacturing workshop layout optimization decision method and system, the method includes: daily management and maintenance of user basic information and operation logs in the system and management and upgrading of the production database associated with the system, after classified storage, regular updating and cleaning, to ensure and maintain the normal operation of the database; in the layout optimization module, due to the different problem types and optimization target dimensions, different meta-heuristic algorithms are called; the theoretical facility location, cost and non-logistics cost relationship calculation obtained after optimization by the aforementioned algorithm needs to be further simulated and verified; the selection of multi-attribute qualitative and quantitative evaluation indicators, the acquisition of evaluation matrices at each level, the calculation of weights and preference coefficients, the selection of decision methods, and the sorting of each solution in the solution set to obtain the most suitable layout solution. The present invention solves the technical problems of poor planning effect, reliance on manual labor and poor information real-time performance.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing, and in particular to a data-driven discrete manufacturing workshop layout optimization decision method and system. Background Art

[0002] The layout of manufacturing workshop facilities is a complex system engineering, which runs through many links such as people, machines, methods, materials, and environment. Before the layout plan is implemented, whether the planning plan can be objectively evaluated will ultimately determine the quality of the entire layout design. Therefore, objective and reasonable scheme evaluation research is crucial. A manufacturing workshop layout optimization decision system was developed by combining real-time facility location data, layout optimization, layout simulation verification, and layout evaluation decision methods.

[0003] At present, there are several types of problems in the layout of discrete manufacturing workshops that seriously affect the improvement of the overall performance of the manufacturing workshop, which are mainly reflected in the following aspects: (1) Inefficient, repeated and ineffective material handling: Due to the chaotic operation process and unreasonable layout planning, materials are transported over long distances, resulting in repeated or ineffective transportation of materials in the workshop, which will inevitably increase the logistics and transportation costs of the enterprise. (2) Huge inventory of work-in-progress: Due to unreasonable workshop layout planning, it is easy to have a backlog of work-in-progress, which puts pressure on the limited space of the workshop and warehouse, and also increases capital costs. (3) Low facility utilization: In many companies, there are many equipment that have been installed, accepted and put into production, but due to unreasonable layout planning, the equipment is temporarily unavailable, which is easy to cause equipment idleness, resulting in a waste of enterprise resources. Therefore, the optimization and improvement of workshop layout has become one of the challenges faced by the current discrete manufacturing workshop in transforming into an intelligent manufacturing workshop and improving the core competitiveness of the enterprise.

[0004] The existing invention patent document "Intelligent workshop rapid customization design method based on generalized encapsulation technology" with publication number CN110020484A includes the following steps: step A, intermediate equipment classification; step B, abstract commonality of intermediate equipment; step C, geometric model encapsulation; step D, motion script encapsulation; step E, trigger mechanism encapsulation; step F, establish a common library of intermediate equipment; step G, call the intermediate equipment library to realize rapid customization design of production line. The intelligent workshop customization design method based on generalized encapsulation technology is based on a three-dimensional simulation system, and performs high-dimensional encapsulation of the geometric model, motion script, control network and optimization algorithm of the intermediate equipment. The existing patent document does not disclose the technical solution of the present application and cannot achieve the technical effect of the present application. The existing three-dimensional simulation software for manufacturing systems on the market (Arena, Flexsim, Plantsimulation, etc.) is mainly oriented to manufacturing system simulation, lacks corresponding layout optimization and evaluation decision-making function modules, and the existing commercial software with facility planning capabilities has a general algorithm and a limited solution scale, which cannot target specific layout problems and reflect the latest algorithm research results. The input parameters of existing workshop facility layout simulation software are mainly derived from empirical data or set parameters, and the traditional workshop production information acquisition relies on manual data collection of resource utilization on the production site, which has the problem of poor information real-time performance. The above reasons lead to a certain lag between its simulation data and results and real-time performance, and the optimization results are difficult to directly guide actual production. Therefore, it is of certain research significance to develop a workshop facility layout optimization decision system based on commercial software.

[0005] In summary, the existing technology has technical problems such as poor planning effect, reliance on manual labor and poor information real-time performance. Summary of the invention

[0006] The technical problem to be solved by the present invention is how to solve the technical problems existing in the prior art, such as poor planning effect, reliance on manual labor and poor information real-time performance.

[0007] The present invention adopts the following technical solutions to solve the above technical problems: A data-driven discrete manufacturing workshop layout optimization decision method includes:

[0008] S1. Deploy the system architecture and logic processing module of the workshop layout optimization decision system, configure and deploy hardware equipment, wherein the system architecture includes: resource support layer, business logic layer, and user interaction layer;

[0009] S2. Acquire preset management upgrade data to maintain basic user information and operation logs in the workshop layout optimization decision system, and upgrade the production management database, wherein the production management database is associated with the workshop layout optimization decision system;

[0010] S3, obtaining layout problem type and optimization target dimension data from the production management database, thereby obtaining problem type and optimization dimension difference data, calling a prepackaged meta-heuristic algorithm according to the problem type and optimization dimension difference data, and setting applicable optimization parameters accordingly to obtain a layout optimization solution;

[0011] S4. Optimize the layout of single objectives and layout of multiple objectives in the pre-set production system simulation software to verify the material handling cost between facilities, the non-logistics relationship between facilities and the dynamic production re-layout cost to obtain simulation verification results;

[0012] S5. Extract the non-inferior solution set from the simulation verification result, form a set of candidate decision solutions with no less than 2 non-inferior solutions in the non-inferior solution set, call the preset decision logic to judge the superiority or inferiority between the non-inferior solutions, and obtain the workshop layout optimization decision result accordingly, wherein the preset decision logic includes: a multi-attribute decision method.

[0013] The system developed in this invention is associated with external software (Plant Simulation 12 and MatlabR2016b) and database SQL Server 2015 through interfaces to achieve data access, exchange and transmission. In the call of different functional modules, the database can be associated through the system interface, classic models and verified algorithms can be called, facility shape data, facility real-time location data, material demand data between facilities, etc. can be loaded to provide data and method support for layout simulation optimization, and different external software platforms can be entered separately to facilitate the operation of models, methods and data, providing strong support for improving the efficiency and quality of facility layout.

[0014] In a more specific technical solution, step S1 includes:

[0015] S11. In the system architecture, the resource support layer is used as the basic layer of the software and hardware platform, and the hardware equipment is configured; the hardware equipment includes: a server, a client, an ultra-wideband UWB sensor and workshop equipment, and based on the verified UWB deployment solution, the assembly facility location data is collected in real time;

[0016] S12. In the business logic layer, a business logic database is constructed to provide user authority management services and database access services, and data is exchanged between the software development platform, the application software and the business logic database using a preset interface. The development data of the layout optimization module, the simulation verification module and the evaluation and decision module are obtained by using a preset function to realize logic, so as to construct the workshop layout optimization decision system;

[0017] S13. In the user interaction layer, the workshop layout data is displayed using a preset visualization method, wherein the workshop layout data includes: a global three-dimensional scene of the workshop, production status of manufacturing elements, layout simulation results, layout optimization results, and optimization plan release.

[0018] The present invention integrates ultra-wideband (UWB) real-time collection of facility location data, discrete manufacturing workshop facility layout optimization method, and multi-attribute decision-making method, develops four functional modules of basic information and database management, layout optimization, simulation verification, and evaluation decision-making, and constructs a data-driven discrete manufacturing workshop layout optimization decision-making system. The discrete manufacturing workshop layout is optimized, evaluated, and decided, which improves the layout efficiency and quality.

[0019] In a more specific technical solution, the step S12 includes: the business logic database includes: a basic information library, a layout model library, a production database and an optimization evaluation method library.

[0020] In a more specific technical solution, step S2 includes:

[0021] S21. Build a basic information base with user rights, operation logs, user management, and information entry data, set system user rights, and retain user operation data with operation logs;

[0022] S22, constructing and calling a layout model library according to a preset manufacturing element model;

[0023] S23, constructing the production management database with production data to manage real-time location data of facilities, basic data of various production resources, simulation historical data, data interaction between different types and historical data tracing data;

[0024] S24. Construct an optimization evaluation method library based on the optimization evaluation logic data, and manage the optimization processing logic and parameters, optimization graphic data, evaluation index data, and decision-making method data.

[0025] In a more specific technical solution, step S3 includes:

[0026] S31, according to the problem type and optimization dimension difference data, using MatlabR2014b tool to process and obtain a layout optimization meta-heuristic algorithm;

[0027] S32, encapsulating the layout optimization meta-heuristic algorithm in the workshop layout optimization decision system into a dll file and storing it in a preset server, so as to construct and manage an optimization evaluation method library in a server database;

[0028] S33, calling the historical algorithm excellent solutions and verified simulation parameters in the server database, loading the pre-packaged meta-heuristic algorithm and heuristic algorithm parameters, and running the workshop layout optimization decision system accordingly.

[0029] The present invention sets corresponding layout optimization and evaluation decision function modules, improves the solution scale, and targets specific layout problems and reflects the latest algorithm research results. The system developed in the present invention is associated with external software (Plant Simulation 12 and MatlabR2014b) and database SQL Server 2015 through interfaces, develops meta-heuristic and multi-attribute decision methods, and realizes personalized and customized function development and optimization.

[0030] In a more specific technical solution, the layout optimization meta-heuristic algorithm in step S31 includes: particle swarm, genetic, and cuckoo algorithms.

[0031] In a more specific technical solution, step S4 includes:

[0032] S41, obtaining and loading the required lightweight CAD model in the preset layout model database into the preset production system simulation software of the local client according to the actual layout scenario;

[0033] S42, loading a data model, reading and loading the data model into the preset production system simulation software in the local client, the simulation software can directly obtain data in a corresponding format through relevant functions, and accordingly create a simulation object and set simulation data through a preset interface;

[0034] S43, calling the existing simulation parameters in the layout model database for the verified simulation problem;

[0035] S44. Use the preset production system simulation software to optimize the single layout objective and the multiple layout objectives according to the simulation data and the existing simulation parameters to verify the material handling cost between facilities, the non-logistics relationship between facilities and the dynamic production re-layout cost, so as to obtain the simulation verification results for display and calling by the client.

[0036] The present invention reduces the reliance on empirical data or set parameters, reduces the reliance on manual labor for obtaining workshop production information, improves the real-time nature of information, avoids the problem of a certain lag between simulation data and results and real-time, and is suitable for directly guiding actual production.

[0037] In a more specific technical solution, in step S42, the simulation object is created through the preset interface, and the simulation data is set, wherein the simulation data includes: material buffer capacity, material handling time and facility failure probability.

[0038] In a more specific technical solution, step S5 includes:

[0039] S51. Developing a fuzzy multi-attribute decision-making method based on MatlabR2014b software, wherein the fuzzy multi-attribute decision-making method includes: analytic hierarchy process, network analytic hierarchy process and TOPSIS method;

[0040] S52, encapsulating the fuzzy multiple attribute decision-making method into a dll file and storing it in a preset server, and constructing and managing the optimization evaluation method in an optimization evaluation method library;

[0041] S53, calling the optimization evaluation method to update the set of candidate decision solutions accordingly.

[0042] The present invention integrates UWB technology to collect relevant facility location data in real time, discrete manufacturing workshop facility layout optimization method, multi-attribute decision-making method, develops a discrete manufacturing workshop facility layout optimization decision-making system, optimizes and evaluates decision results to provide a scientific decision-making basis for actual production.

[0043] In a more specific technical solution, a data-driven discrete manufacturing workshop layout optimization decision system includes:

[0044] System deployment module, used to deploy the system architecture and logic processing module of the workshop layout optimization decision system, configure and deploy hardware equipment, wherein the system architecture includes: resource support layer, business logic layer, and user interaction layer;

[0045] A system basic information and database management module is used to obtain preset management upgrade data, based on which the user basic information and operation logs in the workshop layout optimization decision system are maintained, and the preset production database is upgraded, wherein the production management database is associated with the workshop layout optimization decision system, and the system basic information and database management module is connected with the system deployment module and the system deployment module;

[0046] Develop a layout optimization module, which is used to obtain layout problem type and optimization target dimension data from the production management database, thereby obtaining problem type and optimization dimension difference data, and calling a pre-packaged meta-heuristic algorithm according to the problem type and optimization dimension difference data, thereby setting applicable optimization parameters to process and obtain a layout optimization solution. The development layout optimization module is connected to the system basic information and database management module and the system deployment module;

[0047] Develop a simulation verification module to optimize the layout of single objectives and multiple objectives in the preset production system simulation software to verify the material handling cost between facilities, the non-logistics relationship between facilities and the dynamic production re-layout cost to obtain simulation verification results, and the development simulation verification module is connected to the system deployment module;

[0048] An evaluation decision module extracts a non-inferior solution set from the simulation verification result, forms a candidate decision solution set with no less than 2 non-inferior solutions in the non-inferior solution set, calls a preset decision logic to judge the superiority or inferiority between the non-inferior solutions, and obtains a workshop layout optimization decision result accordingly, wherein the preset decision logic includes: a multi-attribute decision method, and the evaluation decision module is connected with the development simulation verification module and the development layout optimization module.

[0049] Compared with the prior art, the present invention has the following advantages: the system developed by the present invention is associated with external software (Plant Simulation 12 and MatlabR2016b) and database SQL Server 2015 through an interface to achieve data access, exchange and transmission. In the call of different functional modules, the database can be associated through the system interface, the classic model and verified algorithm can be called, the facility shape data, the facility real-time location data, the material demand data between facilities, etc. can be loaded to provide data and method support for layout simulation optimization, and different external software platforms can be entered respectively to facilitate the operation of models, methods and data, and provide strong support for improving the efficiency and quality of facility layout.

[0050] The present invention integrates ultra-wideband (UWB) real-time collection of facility location data, discrete manufacturing workshop facility layout optimization method, and multi-attribute decision-making method, develops four functional modules of basic information and database management, layout optimization, simulation verification, and evaluation decision-making, and constructs a data-driven discrete manufacturing workshop layout optimization decision-making system. The discrete manufacturing workshop layout is optimized, evaluated, and decided, which improves the layout efficiency and quality.

[0051] The present invention sets corresponding layout optimization and evaluation decision function modules, improves the solution scale, and targets specific layout problems and reflects the latest algorithm research results. The system developed in the present invention is associated with external software (Plant Simulation 12 and MatlabR2014b) and database SQL Server 2015 through interfaces, develops meta-heuristic and multi-attribute decision methods, and realizes personalized and customized function development and optimization.

[0052] The present invention reduces the reliance on empirical data or set parameters, reduces the reliance on manual labor for obtaining workshop production information, improves the real-time nature of information, avoids the problem of a certain lag between simulation data and results and real-time, and is suitable for directly guiding actual production.

[0053] The present invention integrates UWB technology to collect relevant facility location data in real time, discrete manufacturing workshop facility layout optimization method, multi-attribute decision method, develops discrete manufacturing workshop facility layout optimization decision system, optimizes and evaluates decision results to provide scientific decision-making basis for actual production. The present invention solves the technical problems of poor planning effect, reliance on manual labor and poor information real-time in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of the steps of a data-driven discrete manufacturing workshop layout optimization decision method according to Embodiment 1 of the present invention;

[0055] Figure 2 A schematic diagram of a data-driven discrete manufacturing workshop layout optimization decision system according to Embodiment 1 of the present invention;

[0056] Figure 3 This is a schematic diagram of specific steps for system basic information and database management in Example 1 of the present invention;

[0057] Figure 4 This is a schematic diagram of specific steps for optimizing the layout of discrete manufacturing workshop facilities according to Embodiment 1 of the present invention;

[0058] Figure 5 This is a schematic diagram of specific steps for simulation verification of discrete manufacturing workshop facility layout according to Embodiment 1 of the present invention;

[0059] Figure 6 It is a schematic diagram of specific steps for evaluating the discrete manufacturing workshop facility layout plan according to Example 1 of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] Example 1

[0062] like Figure 1 and Figure 2 As shown, a data-driven discrete manufacturing workshop layout optimization decision method includes the following steps:

[0063] S1: System basic information and database management: system architecture and software and hardware deployment. The system architecture consists of a resource support layer, a business logic layer, and a user interaction layer. The hardware part mainly configures the server-client, the deployment of ultra-wideband (UltraWide Brand, UWB) sensors, and typical facilities in the workshop. In this embodiment, the client-server (Client / Server, C / S) architecture is used to clarify the software operating environment (development tools: Visual Studio 2015, programming language: C#, SimTalk, development platform: Microsoft.NET, database: SQL Server 2015), the software operating environment (server: operating platform Windows Server 2015, database SQL Server 2015, client: application software PlantSimulation12, MatlabR2014b, Microsoft.NET platform 4.6.2), and the hardware part mainly configures the server-client, UWB sensor deployment, and typical facilities in the workshop. Develop a system basic information and database management module to perform daily management and maintenance of user basic information and operation logs in the system and manage and upgrade the production database associated with the system. In this embodiment, develop a system basic information and database management module, where the basic information database mainly includes basic user permissions, operation logs, user management, information entry, etc., and the database management mainly includes layout model library (CAD model import after lightweight processing), production database, and optimization evaluation database;

[0064] S2: Optimization of facility layout of discrete manufacturing workshop: layout development layout optimization module, according to the problem type and optimization target dimension, call the packaged meta-heuristic algorithm to set reasonable optimization parameters to obtain the optimization solution; in this embodiment, the layout optimization module is developed based on Matlab design or improvement of meta-heuristic algorithm;

[0065] S3: Simulation verification of discrete manufacturing workshop facility layout: Develop a simulation verification module, carry out single-objective and multi-objective optimization of layout in the production system simulation software, and verify the material handling cost between facilities, the non-logistics relationship between facilities, and the re-layout cost caused by the dynamic production stage; in this embodiment, the simulation verification module is developed using Plant Simulation12 and its simtalk language. In this embodiment, C#, Matlab, and simtalk software tools in Plant Simulation are used to write programs to develop a data-driven discrete manufacturing workshop layout optimization decision system;

[0066] S4: Evaluation of discrete manufacturing workshop facility layout options;

[0067] S5. Determine the optimal layout plan for discrete manufacturing workshop facilities: develop an evaluation and decision-making module, and organize multiple solutions in the non-inferior solution set of the simulation verification results into a set of candidate solutions. The degree of superiority and inferiority between them can be judged by calling the multi-attribute decision-making method. In this embodiment, an evaluation and decision-making module is developed based on Matlab design or improved multi-attribute decision-making algorithm to improve the efficiency, quality and effectiveness of discrete manufacturing workshop layout.

[0068] The integrated management and development system of the present invention includes four functional modules: basic information and database management, layout optimization, simulation verification, and evaluation and decision-making. The developed system is associated with external software (Plant Simulation 12 and MatlabR2016b) and database SQL Server 2015 through interfaces to achieve data access, exchange and transmission. In the call of different functional modules, it is possible to associate the database through the system interface, call classic models and verified algorithms, load facility shape data, facility real-time location data, material demand data between facilities, etc., to provide data and method support for layout simulation optimization, and it is also possible to enter different external software platforms respectively to facilitate the operation of models, methods and data, and provide strong support for improving the efficiency and quality of facility layout.

[0069] like Figure 3 As shown, further, the step S1 includes:

[0070] S11: In the development hierarchical architecture, the resource support layer serves as the foundation of the software and hardware platform, and the hardware part mainly configures the server-client, ultra-wideband (Ultra Wide Brand, UWB) sensor deployment, and typical facilities in the workshop. Based on the verified UWB deployment plan, the location data of various facilities (tooling, material handling equipment, shelves, etc.) during the assembly process are collected in real time; in this embodiment, in the development hierarchical architecture, the resource support layer serves as the foundation of the software and hardware platform, and the hardware part mainly configures the server-client, ultra-wideband (Ultra Wide Brand, UWB) sensor deployment, and typical facilities in the workshop. In the UWB system deployment, four Ubisense sensors are generally placed at the four right angles of the rectangular / square area to be tested, with a height of about 3-5m. Generally, no physical obstruction is required, and the coverage area is 35x35m 2 Each sensor is connected to a POE switch via a single network cable, and then connected to a location server via the switch. Based on the proven UWB deployment solution, the location data of various facilities (tooling, material handling equipment, shelves, etc.) during the assembly process are collected in real time.

[0071] S12: In the business logic layer, user authority management services and database access services are provided by building a basic information library, a layout model library, a production database, and an optimization evaluation method library. Based on the corresponding interface, the software development platform and application software can complete data interaction with the database, refer to the function implementation logic, and realize the layout optimization, simulation verification, and evaluation decision function module development.

[0072] S13: In the user interaction layer, the global three-dimensional scene of the workshop, the production status of manufacturing elements, layout simulation results, layout optimization results, optimization plan release, etc. are visualized, and users can trace historical information data, query, add, modify and delete data according to the roles with different permissions assigned by the system.

[0073] The basic information database mainly includes basic user permissions, operation logs, user management, information entry, etc. System user permissions are divided into two types: ordinary users and system administrators. All user access, modification and deletion operation records are retained in the form of operation logs, which are convenient for system administrators and ordinary users to view and retrieve.

[0074] The layout model library mainly stores, modifies and calls physical and data models such as products, processes, resources, etc. according to different classifications of manufacturing elements; in the embodiment, various entity models need to be called in the process of building a site layout simulation model. The content in the layout model library is the index position of the model on the server side, and the entity models on the server side are all saved in .jt format to facilitate Plant Simulation calls.

[0075] The production database manages the real-time location data of facilities, various basic data of production resources, simulation historical data, data interaction between different types, and historical data tracing, etc., to realize the functions of querying, changing, adding and deleting various types of data. In this embodiment, production-related data (real-time facility location data, dynamic material demand data between facilities, assembly operation area area demand data, etc.) are all stored in the database in the form of tables, and various types of data are updated regularly to ensure the timeliness of relevant data. Users can upload, download, modify and query the data in this type of database.

[0076] The optimization evaluation method library realizes the storage, modification and calling of intelligent algorithms and their related optimization parameters, optimized charts and texts and other data, evaluation index data, and decision methods; in this embodiment, a variety of verified metaheuristic algorithms (PSO, MPSO-SA and MMPSO algorithms, etc.), weight calculation methods (entropy method and ANP method), sorting methods (ANP method, PROMETHEE method and TOPSIS method) developed based on Matlab, as well as the corresponding parameter settings, evaluation indicators, decision methods, optimization results and decision results of the algorithms are classified and stored for easy calling.

[0077] like Figure 4 As shown, further, the specific process of step S2 is as follows:

[0078] S21: According to different problem types and optimization objectives, different layout optimization meta-heuristic algorithms (particle swarm, genetic, cuckoo algorithm, etc.) are developed based on MatlabR2014b software. In this embodiment, different layout optimization meta-heuristic algorithms (particle swarm, genetic, cuckoo algorithm, etc.) are developed based on MatlabR2014b software.

[0079] S22: The layout optimization module encapsulates the Matlab optimization algorithms used in the system into dll files and stores them on the server side, and at the same time builds and manages the optimization evaluation method library in the database on the server side.

[0080] S23: Call the excellent solutions of the historical algorithms and verified simulation parameters in the database, or load the algorithms and related parameters into the system for operation.

[0081] like Figure 5 As shown, further, the specific process of step S3 is as follows:

[0082] S31: According to the actual layout scenario, the required lightweight CAD model in the layout model database is loaded into the local client. The simulation software can realize the rapid import of the physical model that has been loaded into the local client through the interface. In this embodiment, according to the actual layout scenario, the required lightweight CAD model in the layout model database is loaded into the local client. The Plant Simulation simulation software can realize the rapid import of the physical model that has been loaded into the local client through interfaces such as 3D.importGraphic.

[0083] S32: Load the data model, read the required data model and load it into the local client. The simulation software can directly obtain data in the corresponding format through related functions. The interface can realize the rapid creation of simulation objects, and the simulation data such as material buffer capacity, material handling time and facility failure probability can be automatically set in the simulation system. In this embodiment, the database is connected through ODBC, the data model is loaded, and the required data model is read and loaded into the local client. Plant Simulation can directly obtain data in the corresponding format through readExcelFile, readTable or readXMLFile. The loadObjectAs interface can be used in conjunction with createObject to realize the rapid creation of simulation objects, and the simulation data such as material buffer capacity, material handling time and facility failure probability can be automatically set in the simulation system.

[0084] S33: For the verified classic simulation problems, the simulation parameters in the database can be directly called, and the simulation software can obtain the already coded Matlab algorithm program through the function reading, writing and calling interface. In this embodiment, for the verified classic simulation problems, the simulation parameters in the database can be directly called, and Plant Simulation can obtain the already coded Matlab algorithm program through the function reading, writing and calling interface by controlling the ActiveX interface embedded in Matlab.

[0085] S34: After the simulation is completed, the simulation results are output in the form of charts or directly published as an executable program of the simulation model for display or calling by the client.

[0086] like Figure 6 As shown, further, the specific process of step S4 is as follows:

[0087] S41: Develop different fuzzy multi-attribute decision-making methods based on MatlabR2014b software (such as hierarchical analysis method, network analytic hierarchy process, TOPSIS method, etc.).

[0088] S42: Encapsulate the above-mentioned multi-attribute decision-making method into a dll file and store it on the server side, and at the same time build and manage an optimization evaluation method library in the optimization evaluation method library on the server side.

[0089] In this embodiment, in step S5, the user can download and call the method, and can also upload a new decision method to update the database content and publish update messages to the client in a timely manner. In this embodiment, the database is connected through JDBC, and the user can download and call the method, and can also upload a new multi-attribute method to update the database content, determine the optimal facility layout plan, and publish update messages to the client in a timely manner.

[0090] In summary, the system developed in this invention is associated with external software (Plant Simulation 12 and MatlabR2016b) and database SQL Server 2015 through interfaces to achieve data access, exchange and transmission. In the call of different functional modules, the database can be associated through the system interface, the classic model and verified algorithm can be called, the facility shape data, the facility real-time location data, the material demand data between facilities, etc. can be loaded to provide data and method support for layout simulation optimization, and different external software platforms can be entered respectively to facilitate the operation of models, methods and data, and provide strong support for improving the efficiency and quality of facility layout.

[0091] The present invention integrates ultra-wideband (UWB) real-time collection of facility location data, discrete manufacturing workshop facility layout optimization method, and multi-attribute decision-making method, develops four functional modules of basic information and database management, layout optimization, simulation verification, and evaluation decision-making, and constructs a data-driven discrete manufacturing workshop layout optimization decision-making system. The discrete manufacturing workshop layout is optimized, evaluated, and decided, which improves the layout efficiency and quality.

[0092] The present invention sets corresponding layout optimization and evaluation decision function modules, improves the solution scale, and targets specific layout problems and reflects the latest algorithm research results. The system developed in the present invention is associated with external software (Plant Simulation 12 and MatlabR2014b) and database SQL Server 2015 through interfaces, develops meta-heuristic and multi-attribute decision methods, and realizes personalized and customized function development and optimization.

[0093] The present invention reduces the reliance on empirical data or set parameters, reduces the reliance on manual labor for obtaining workshop production information, improves the real-time nature of information, avoids the problem of a certain lag between simulation data and results and real-time, and is suitable for directly guiding actual production.

[0094] The present invention integrates UWB technology to collect relevant facility location data in real time, discrete manufacturing workshop facility layout optimization method, multi-attribute decision method, develops discrete manufacturing workshop facility layout optimization decision system, optimizes and evaluates decision results to provide scientific decision-making basis for actual production. The present invention solves the technical problems of poor planning effect, reliance on manual labor and poor information real-time in the prior art.

[0095] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data-driven decision-making method for discrete manufacturing workshop layout optimization. It is characterized in that The method comprises: S1. Deploy the system architecture and logic processing modules of the workshop layout optimization decision system, configure and deploy hardware equipment, where the system architecture includes: resource support layer, business logic layer, and user interaction layer; S2. Obtaining preset management upgrade data to maintain basic user information and operation logs in the workshop layout optimization decision system, and upgrading the production management database, wherein the production management database is associated with the workshop layout optimization decision system; S3. Obtain layout problem type and optimization target dimension data from the production management database, obtain problem type and optimization dimension difference data accordingly, call a pre-packaged meta-heuristic algorithm according to the problem type and optimization dimension difference data, and set applicable optimization parameters accordingly to obtain a layout optimization solution; S3 includes: S31. Based on the problem type and optimization dimension difference data, the layout optimization meta-heuristic algorithm is obtained by using MatlabR2014b tool; S32, encapsulating the layout optimization meta-heuristic algorithm in the workshop layout optimization decision system into a dll file and storing it in a preset server, so as to construct and manage an optimization evaluation method library in a server database; S33, calling the historical algorithm excellent solution and verified simulation parameters in the server database, loading the pre-packaged meta-heuristic algorithm and heuristic algorithm parameters, and running the workshop layout optimization decision system accordingly; S4. Optimize the layout of single objectives and layout of multiple objectives in the pre-set production system simulation software to verify the material handling cost between facilities, the non-logistics relationship between facilities and the dynamic production re-layout cost to obtain simulation verification results; S4 includes: S41, obtaining and loading the required lightweight CAD model in the preset layout model database into the preset production system simulation software of the local client according to the actual layout scenario; S42, loading the data model, reading and loading the data model into the preset production system simulation software in the local client, the simulation software can directly obtain data in a corresponding format through relevant functions, and accordingly create a simulation object and set simulation data through a preset interface; S43, calling existing simulation parameters in the layout model database for the verified simulation problem; S44, using the preset production system simulation software to optimize the layout of single objectives and layout of multiple objectives according to the simulation data and the existing simulation parameters, so as to verify the material handling cost between facilities, the non-logistics relationship between facilities and the dynamic production re-layout cost, so as to obtain the simulation verification results for display and calling by the client; S5. Extract the non-inferior solution set from the simulation verification results, and form a candidate decision plan set with no less than 2 non-inferior solutions in the non-inferior solution set. Call the preset decision logic to judge the superiority or inferiority between the non-inferior solutions, and obtain the workshop layout optimization decision result based on it. The preset decision logic includes: multi-attribute decision method.

2. According to the data-driven discrete manufacturing workshop layout optimization decision method of claim 1, It is characterized in that The step S1 comprises: S11. In the system architecture, the resource support layer is used as the basic layer of the software and hardware platform, and the hardware equipment is configured; the hardware equipment includes: a server, a client, an ultra-wideband UWB sensor and workshop equipment, and based on the verified UWB deployment solution, the assembly facility location data is collected in real time; S12. In the business logic layer, a business logic database is constructed to provide user authority management services and database access services, and data is exchanged between the software development platform, the application software and the business logic database using a preset interface. The development data of the layout optimization module, the simulation verification module and the evaluation and decision module are obtained by using a preset function to realize logic, so as to construct the workshop layout optimization decision system; S13. In the user interaction layer, the workshop layout data is displayed using a preset visualization method, wherein the workshop layout data includes: a global three-dimensional scene of the workshop, production status of manufacturing elements, layout simulation results, layout optimization results, and optimization plan release.

3. According to the data-driven discrete manufacturing workshop layout optimization decision method of claim 2, It is characterized in that The step S12 includes: the business logic database includes: a basic information library, a layout model library, a production database and an optimization evaluation method library.

4. According to claim 1, a data-driven discrete manufacturing workshop layout optimization decision method, It is characterized in that The step S2 comprises: S21. Build a basic information base with user rights, operation logs, user management, and information entry data, set system user rights, and retain user operation data with operation logs; S22, constructing and calling a layout model library according to a preset manufacturing element model; S23, constructing the production management database with production data to manage real-time location data of facilities, basic data of various production resources, simulation historical data, data interaction between different types and historical data tracing data; S24. Construct an optimization evaluation method library based on the optimization evaluation logic data, and manage the optimization processing logic and parameters, optimization graphic data, evaluation index data, and decision-making method data.

5. According to claim 1, a data-driven discrete manufacturing workshop layout optimization decision method, It is characterized in that The layout optimization meta-heuristic algorithms in step S31 include: particle swarm, genetic, and cuckoo algorithms.

6. A data-driven discrete manufacturing workshop layout optimization decision method according to claim 1, It is characterized in that In the step S42, the simulation object is created through the preset interface, and the simulation data is set, wherein the simulation data includes: material buffer capacity, material handling time and facility failure probability.

7. The data-driven discrete manufacturing workshop layout optimization decision method according to claim 1, It is characterized in that The step S5 comprises: S51. Developing a fuzzy multi-attribute decision-making method based on MatlabR2014b software, wherein the fuzzy multi-attribute decision-making method includes: analytic hierarchy process, network analytic hierarchy process and TOPSIS method; S52, encapsulating the fuzzy multiple attribute decision-making method into a dll file and storing it in a preset server, and constructing and managing the optimization evaluation method in an optimization evaluation method library; S53, calling the optimization evaluation method to update the set of candidate decision solutions accordingly.

8. A data-driven discrete manufacturing workshop layout optimization decision system, used to execute the data-driven discrete manufacturing workshop layout optimization decision method according to any one of claims 1 to 7. It is characterized in that The system comprises: System deployment module, used to deploy the system architecture and logic processing module of the workshop layout optimization decision system, configure and deploy hardware equipment, wherein the system architecture includes: resource support layer, business logic layer, and user interaction layer; A system basic information and database management module is used to obtain preset management upgrade data, based on which the user basic information and operation logs in the workshop layout optimization decision system are maintained, and the preset production database is upgraded, wherein the production management database is associated with the workshop layout optimization decision system, and the system basic information and database management module is connected with the system deployment module and the system deployment module; Develop a layout optimization module, which is used to obtain layout problem type and optimization target dimension data from the production management database, thereby obtaining problem type and optimization dimension difference data, and calling a pre-packaged meta-heuristic algorithm according to the problem type and optimization dimension difference data, thereby setting applicable optimization parameters to process and obtain a layout optimization solution. The development layout optimization module is connected to the system basic information and database management module and the system deployment module; Develop a simulation verification module to optimize the layout of single objectives and multiple objectives in the preset production system simulation software to verify the material handling cost between facilities, the non-logistics relationship between facilities and the dynamic production re-layout cost to obtain simulation verification results, and the development simulation verification module is connected to the system deployment module; An evaluation decision module extracts a non-inferior solution set from the simulation verification result, forms a candidate decision solution set with no less than 2 non-inferior solutions in the non-inferior solution set, calls a preset decision logic to judge the superiority or inferiority between the non-inferior solutions, and obtains a workshop layout optimization decision result accordingly, wherein the preset decision logic includes: a multi-attribute decision method, and the evaluation decision module is connected with the development simulation verification module and the development layout optimization module.

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