CN115204022A - Data-driven discrete manufacturing workshop layout optimization decision method and system - Google Patents
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Abstract
The invention provides a data-driven discrete manufacturing workshop layout optimization decision method and a system, wherein the method comprises the following steps: daily management and maintenance are carried out on user basic information and operation logs in the system, and a production database related to the system is managed and upgraded, and after classified storage, the database is regularly updated and cleaned, so that the normal operation of the database is guaranteed and maintained; in the layout optimization module, different meta-heuristic algorithms are called due to the difference of the problem types and the optimization target dimensions; further simulation verification is needed to be obtained in the calculation of the theoretical facility position, the cost and the non-logistics cost relation obtained after the algorithm is optimized; the selection of multi-attribute qualitative and quantitative evaluation indexes, the acquisition of evaluation matrixes of all levels, the selection of weight calculation, preference coefficients and decision methods are realized, and all schemes in the scheme set are sequenced to obtain the most appropriate layout scheme. The invention solves the technical problems of poor planning effect, dependence on manpower and poor information real-time performance.
Description
Technical Field
The invention relates to the field of intelligent manufacturing, in particular to a data-driven discrete manufacturing workshop layout optimization decision method and a data-driven discrete manufacturing workshop layout optimization decision system.
Background
The layout of facilities in a manufacturing workshop is a complex system engineering and runs through a plurality of links of people, machines, methods, materials and rings. Before the layout scheme is implemented, whether the planning scheme can be objectively evaluated or not finally determines the quality of the whole layout design, so that objective and reasonable scheme evaluation research is very important. And a manufacturing shop layout optimization decision system is developed by combining the real-time facility position data, layout optimization, layout simulation verification and layout evaluation decision methods.
At present, the overall performance of a discrete manufacturing workshop is seriously influenced by several problems existing in the layout of the discrete manufacturing workshop, and the problems are mainly reflected in the following aspects: (1) inefficient, repetitive and inefficient handling of materials: due to the fact that materials are carried in a long distance due to disordered operation flows and unreasonable layout planning, materials in a workshop are repeatedly carried or carried inefficiently, and accordingly logistics carrying cost of enterprises is increased. (2) large work-in-process inventory: due to the unreasonable layout of the workshop, overstocking of products is easy to occur, pressure is brought to the limited space of the workshop and the storehouse, and meanwhile, the capital cost is increased. (3) low utility ratio: many devices which are already installed, accepted and put into production exist in more enterprises, but due to unreasonable layout planning, the devices cannot be used temporarily, so that the phenomenon of idle devices easily occurs, and the waste of enterprise resources is caused. Therefore, the optimization and improvement of the shop floor has become one of the challenges facing the transformation of the intelligent manufacturing shop from the discrete manufacturing shop and the improvement of the enterprise core competitiveness.
The patent document CN110020484A of the prior invention, namely the intelligent workshop rapid customization design method based on the generalized encapsulation technology, comprises the following steps: step A, classifying intermediate equipment; step B, abstracting the commonality of the intermediate equipment; step C, packaging the geometric model; step D, packaging the motion script; step E, triggering mechanism encapsulation; step F, establishing an intermediate equipment public library; and G, calling an intermediate equipment library to realize rapid customized design of the production line. The intelligent workshop customized design method based on the generalized encapsulation technology is based on a three-dimensional simulation system, and carries out high-dimensional encapsulation on a geometric model, a motion script, a control network and an optimization algorithm of intermediate equipment. The prior patent document does not disclose the technical solution of the present application, and the technical effect of the present application cannot be achieved. The existing three-dimensional simulation software (Arena, flexsim, plant, etc.) of the manufacturing system in the market mainly faces to the simulation of the manufacturing system, corresponding layout optimization and evaluation decision function modules are lacked, and the existing commercial software with facility planning capability has universality and limited solution scale, and cannot aim at specific layout problems and reflect the latest algorithm research results. The input parameters of the existing workshop facility layout simulation software are mainly derived from empirical data or set parameters, the traditional workshop production information acquisition depends on manual data acquisition for resource utilization of a production site, and the problem of poor information real-time performance exists. Therefore, the development of a workshop facility layout optimization decision system based on commercial software has certain research significance.
In conclusion, the prior art has the technical problems of poor planning effect, dependence on manual work and poor information real-time performance.
Disclosure of Invention
The technical problem to be solved by the invention is how to solve the technical problems of poor planning effect, dependence on manpower and poor information real-time performance in the prior art.
The invention adopts the following technical scheme to solve the technical problems: a data-driven discrete manufacturing shop layout optimization decision method comprises the following steps:
s1, deploying a system architecture and a logic processing module of a workshop layout optimization decision system, and configuring and deploying hardware equipment, wherein the system architecture comprises: a resource supporting layer, a service logic layer and a user interaction layer;
s2, acquiring preset management upgrading data so as to maintain user basic information and operation logs in the workshop layout optimization decision system and upgrade a production management database, wherein the production management database is associated with the workshop layout optimization decision system;
s3, obtaining a layout problem type and optimization target dimension data from a production management database, obtaining problem type and optimization dimension difference data according to the layout problem type and the optimization target dimension data, calling a pre-packaging meta-heuristic algorithm according to the problem type and the optimization dimension difference data, and setting an applicable optimization parameter according to the pre-packaging meta-heuristic algorithm to obtain a layout optimization solution through processing;
s4, optimizing layout single targets and layout multiple targets in the preset production system simulation software to verify the material handling cost among facilities, the non-logistics relationship among facilities and the dynamic production re-layout cost so as to obtain a simulation verification result;
s5, extracting non-inferior solution sets in the simulation verification result, forming a decision scheme set to be selected by at least 2 non-inferior solutions in the non-inferior solution sets, calling a preset decision logic to judge the goodness degree between the non-inferior solutions so as to obtain a workshop layout optimization decision result, wherein the preset decision logic comprises: a multi-attribute decision method.
The invention relates the developed system with the external software (Plant Simulation12 and MatlabR2016 b) and the database SQL Server 2015 through the interface, to realize the data access, exchange and transmission. In the calling of different function modules, a classical model and a verified algorithm, loading facility appearance data, facility real-time position data, material demand data among facilities and the like can be called through a system interface association database to provide data and method support for layout simulation optimization, and the method can also enter different platforms of external software respectively, so that the operation on the model, the method and the data is facilitated, and powerful support is provided for improving the layout efficiency and the quality of the facilities.
In a more specific technical solution, the step S1 includes:
s11, in the system architecture, the resource supporting layer is used as a software and hardware platform basic layer, and the hardware equipment is configured; the hardware device includes: the system comprises a server, a client, an ultra-wideband UWB sensor and workshop equipment, wherein the server is used for collecting the position data of the assembly facility in real time based on a verified UWB deployment scheme;
s12, constructing a business logic database at the business logic layer so as to provide user authority management service and database access service, performing data interaction among a software development platform, application software and the business logic database by using a preset interface, realizing development data of a logic acquisition layout optimization module, a simulation verification module and an evaluation decision module by using a preset function, and constructing a workshop layout optimization decision system;
s13, displaying workshop layout data in a preset visualization method on the user interaction layer, wherein the workshop layout data comprise: the method comprises the following steps of overall three-dimensional scene of a workshop, production state of manufacturing elements, layout simulation result, layout optimization result and optimization scheme release.
The Ultra Wide Band (UWB) real-time collection facility position data is integrated, a discrete manufacturing workshop facility layout optimization method and a multi-attribute decision method are used, four functional modules of basic information and database management, layout optimization, simulation verification and evaluation decision are developed, a data-driven discrete manufacturing workshop layout optimization decision system is constructed, layout optimization, evaluation and decision are carried out on the discrete manufacturing workshop, and layout efficiency and quality are improved.
In a more specific technical solution, the step S12 includes: the business logic database comprises: a basic information base, a layout model base, a production database and an optimization evaluation method base.
In a more specific technical solution, the step S2 includes:
s21, constructing a basic information base by using user authority, an operation log, user management and information input data, setting system user authority, and reserving user operation data by using the operation log;
s22, constructing and calling a layout model library according to a preset manufacturing element model;
s23, establishing the production management database by using the production data, and managing real-time position data of facilities, basic data of various production resources, simulation historical data, data interaction among different types and historical data tracing data;
and S24, constructing an optimization evaluation method library by using the optimization evaluation logic data, and managing optimization processing logic and parameters, optimizing image-text data, evaluating index data and decision method data.
In a more specific technical solution, the step S3 includes:
s31, processing by adopting a MatlabR2014b tool according to the problem type and the optimization dimension difference data to obtain a layout optimization meta-heuristic algorithm;
s32, packaging the layout optimization meta-heuristic algorithm in the workshop layout optimization decision system into a dll file and storing the dll file in a preset server so as to construct and manage an optimization evaluation method library in a server database;
s33, calling excellent solutions and verified simulation parameters of the historical algorithm in the server-side database, loading the pre-packaged meta-heuristic algorithm and heuristic algorithm parameters, and operating the workshop layout optimization decision system accordingly.
The invention sets corresponding layout optimization and evaluation decision function modules, improves the solving scale, and aims at specific layout problems and reflects the latest algorithm research results. In the invention, the developed system is associated with external software (Plant Simulation12 and MatlabR2014 b) and a database SQL Server 2015 through an interface, and a meta-heuristic and multi-attribute decision-making method is developed to realize personalized and customized function development and optimization.
In a more specific technical solution, the layout optimization meta-heuristic algorithm in step S31 includes: particle swarm, genetic, cuckoo algorithms.
In a more specific technical solution, the step S4 includes:
s41, acquiring and loading a light CAD model required in a preset layout model database to the preset production system simulation software of the local client according to an actual layout scene;
s42, loading a data model, reading and loading the data model into the preset production system simulation software in the local client, wherein the simulation software can directly acquire data in a corresponding format through a related function so as to create a simulation object and set simulation data through a preset interface;
s43, calling the existing simulation parameters in the layout model database for the verified simulation problem;
and S44, optimizing the layout single target and the layout multiple targets by the preset production system simulation software according to the simulation data and the existing simulation parameters so as to verify the material handling cost among facilities, the non-logistics relationship among facilities and the dynamic production re-layout cost, and obtaining a simulation verification result for displaying and calling by a client.
The invention reduces the dependence on experience data or set parameters, reduces the dependence on labor for acquiring workshop production information, improves the real-time property of information, avoids the problem that certain hysteresis exists between simulation data and results and the real-time property, and is suitable for directly guiding actual production.
In a more specific technical solution, in the step S42, the simulation object is created through the preset interface, and the simulation data is set, where the simulation data includes: material buffer area capacity, material handling time, and facility failure probability.
In a more specific technical solution, the step S5 includes:
s51, developing a fuzzy multi-attribute decision method based on MatlabR2014b software, wherein the fuzzy multi-attribute decision method comprises the following steps: analytic hierarchy process, network analytic hierarchy process and TOPSIS process;
s52, packaging the fuzzy multi-attribute decision method into a dll file, storing the dll file in a preset server, and constructing and managing the optimized evaluation method in an optimized evaluation method library;
and S53, calling the optimization evaluation method so as to update the decision scheme set to be selected.
According to the invention, the UWB technology is fused to collect relevant facility position data in real time, a discrete manufacturing workshop facility layout optimization method and a multi-attribute decision method are adopted, a discrete manufacturing workshop facility layout optimization decision system is developed, and optimization and evaluation decision results provide scientific decision basis for actual production.
In a more specific aspect, a data-driven discrete manufacturing shop layout optimization decision system includes:
the system deployment module is used for deploying a system architecture and a logic processing module of the workshop layout optimization decision system, and configuring and deploying hardware equipment, wherein the system architecture comprises: a resource supporting layer, a service logic layer and a user interaction layer;
a system basic information and database management module, configured to obtain preset management upgrade data, maintain user basic information and operation logs in the workshop layout optimization decision system, and upgrade a preset production database, where the production management database is associated with the workshop layout optimization decision system, and the system basic information and database management module is connected to the system deployment module and the system deployment module;
the development layout optimization module is used for acquiring a layout problem type and optimization target dimension data from the production management database, acquiring problem type and optimization dimension difference data, calling a pre-packaging meta-heuristic algorithm according to the problem type and optimization dimension difference data, setting an applicable optimization parameter according to the problem type and optimization dimension difference data, and processing the applicable optimization parameter to obtain a layout optimization solution, wherein the development layout optimization module is connected with the system basic information and database management module and the system deployment module;
the system comprises a development simulation verification module, a system deployment module and a system deployment module, wherein the development simulation verification module is used for optimizing layout single targets and layout multiple targets in preset production system simulation software so as to verify material handling cost among facilities, non-logistics relationship among facilities and dynamic production re-layout cost among facilities to obtain a simulation verification result;
the evaluation decision module is used for extracting a non-inferior solution set in the simulation verification result, forming a decision scheme set to be selected by at least 2 non-inferior solutions in the non-inferior solution set, calling a preset decision logic to judge the goodness and badness degree between the non-inferior solutions so as to obtain a workshop layout optimization decision result, wherein the preset decision logic comprises: and the evaluation decision module is connected with the development simulation verification module and the development layout optimization module.
Compared with the prior art, the invention has the following advantages: the invention relates the developed system with external software (Plant Simulation12 and MatlabR2016 b) and database SQL Server 2015 through interface, to realize data access, exchange and transmission. In the calling of different function modules, a classical model and a verified algorithm, loading facility appearance data, facility real-time position data, material demand data among facilities and the like can be called through a system interface correlation database, data and method support are provided for layout simulation optimization, the method can also enter different external software platforms respectively, the model, the method and the data are convenient to operate, and powerful support is provided for improving the layout efficiency and the quality of the facilities.
The Ultra Wide Band (UWB) real-time collection facility position data, the discrete manufacturing workshop facility layout optimization method and the multi-attribute decision method are integrated, four functional modules of basic information and database management, layout optimization, simulation verification and evaluation decision are developed, a data-driven discrete manufacturing workshop layout optimization decision system is constructed, layout optimization, evaluation and decision of the discrete manufacturing workshop are achieved, and layout efficiency and quality are improved.
The invention sets corresponding layout optimization and evaluation decision function modules, improves the solving scale, and aims at specific layout problems and reflects the latest algorithm research results. In the invention, the developed system is associated with external software (Plant Simulation12 and MatlabR2014 b) and a database SQL Server 2015 through an interface, and a meta-heuristic and multi-attribute decision-making method is developed to realize personalized and customized function development and optimization.
The invention reduces the dependence on experience data or set parameters, reduces the dependence on labor for acquiring workshop production information, improves the real-time property of information, avoids the problem that certain hysteresis exists between simulation data and results and the real-time property, and is suitable for directly guiding actual production.
According to the invention, the UWB technology is fused to collect relevant facility position data in real time, a discrete manufacturing workshop facility layout optimization method and a multi-attribute decision method are adopted, a discrete manufacturing workshop facility layout optimization decision system is developed, and optimization and evaluation decision results provide scientific decision basis for actual production. The invention solves the technical problems of poor planning effect, dependence on manpower and poor information real-time performance in the prior art.
Drawings
Fig. 1 is a schematic diagram illustrating steps of a data-driven decision-making method for optimizing a layout of a discrete manufacturing shop according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of a data-driven decision-making system for optimizing a layout of a discrete manufacturing shop according to embodiment 1 of the present invention;
FIG. 3 is a diagram illustrating the detailed steps of system basic information and database management according to embodiment 1 of the present invention;
FIG. 4 is a schematic diagram of the detailed steps of the layout optimization of the discrete manufacturing plant facilities according to embodiment 1 of the present invention;
FIG. 5 is a schematic diagram of the detailed steps of simulation verification of a discrete manufacturing shop facility layout according to embodiment 1 of the present invention;
FIG. 6 is a schematic diagram of specific steps of a discrete manufacturing plant facility layout scenario evaluation according to example 1 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1 and fig. 2, a data-driven discrete manufacturing shop layout optimization decision method includes the following steps:
s1: system basic information and database management: the system architecture is deployed by software and hardware, the system architecture is composed of a resource supporting layer, a service logic layer and a user interaction layer, and the hardware part mainly configures a server-client, ultra Wide Band (UWB) sensor deployment and typical facilities in a workshop. In this embodiment, with a Client-Server (C/S) architecture, a software operating environment (development tool: visual Studio 2015, programming language: C #, simTalk, development platform: microsoft. NET, database: SQL Server 2015), a software operating environment (Server: operating platform Windows Server 2015, database SQL Server 2015, client: application software Plant Simulation12, matlabR2014b, microsoft. NET platform 4.6.2) are defined, and a hardware part mainly configures the Server-Client, UWB sensor deployment and typical facilities in a workshop. The system comprises a development system basic information and database management module, a production database and a database management module, wherein the development system basic information and database management module is used for performing daily management and maintenance on user basic information and operation logs in the system and performing management upgrading on the production database associated with the system;
s2: optimizing the layout of facilities in a discrete manufacturing workshop: the layout development layout optimization module calls a packaged meta-heuristic algorithm to set reasonable optimization parameters according to different problem types and optimization target dimensions to obtain an optimized solution; in this embodiment, a layout optimization module is developed based on Matlab design or an improved meta-heuristic algorithm;
s3: and (3) simulation verification of facility layout of the discrete manufacturing workshop: developing a simulation verification module, respectively developing layout single-target and multi-target optimization in simulation software of a production system, and verifying material handling cost among facilities, non-logistics relation among facilities and re-layout cost caused by a dynamic production stage; in this embodiment, a Simulation verification module is developed by using Plant Simulation12 and its simulk language, and in this embodiment, a data-driven discrete manufacturing shop layout optimization decision system is developed by using C #, matlab, and simulk software tool in Plant Simulation to write programs;
s4: evaluating a facility layout scheme of a discrete manufacturing workshop;
s5, determining a facility layout optimization scheme of the discrete manufacturing workshop: the development evaluation decision module is used for forming a scheme set to be selected for a plurality of solutions in the non-inferior solution set of the simulation verification result and judging the degree of superiority and inferiority among the solutions by calling a multi-attribute decision method; in the embodiment, an evaluation decision module is developed based on Matlab design or improved multi-attribute decision algorithm, so that the layout efficiency, quality and effectiveness of a discrete manufacturing workshop are improved.
The integrated management development system comprises four functional modules of basic information and database management, layout optimization, simulation verification and evaluation decision, and associates the developed system with external software (Plant Simulation12 and MatlabR2016 b) and a database SQL Server 2015 through an interface to realize data access, exchange and transmission. In the calling of different function modules, a classical model and a verified algorithm, loading facility appearance data, facility real-time position data, material demand data among facilities and the like can be called through a system interface association database to provide data and method support for layout simulation optimization, and the method can also enter different platforms of external software respectively, so that the operation on the model, the method and the data is facilitated, and powerful support is provided for improving the layout efficiency and the quality of the facilities.
As shown in fig. 3, further, the step S1 includes:
s11, in a development level architecture, a resource supporting layer is used as a software and hardware platform foundation, and a hardware part is mainly used for configuring a server-client, ultra Wide Band (UWB) sensor deployment and typical facilities in a workshop. Based on a verified UWB deployment scheme, acquiring position data of various facilities (tools, material handling equipment, goods shelves and the like) in the assembly process in real time; in this embodiment, in the development level architecture, the resource support layer serves as a software and hardware platform foundation, and the hardware part is mainly a configuration server-client, ultra Wide Band (UWB) sensor deployment, and typical facilities in a workshop. In UWB system deployment, four Ubisense sensors are generally placed at four right angles of a rectangular/square region to be measured, the placement height is about 3-5m, no physical shielding is generally required, and the coverage area is 35x35m 2 . Each sensor is connected with the POE switch through a single network cable and then connected with the position server through the switch. Based on the verified UWB deployment scheme, the position data of various facilities (tools, material handling equipment, goods shelves and the like) in the assembly process are collected in real time.
And S12, at a service logic layer, providing user authority management service and database access service by constructing a basic information base, a layout model base, a production database and an optimization evaluation method base. Based on corresponding interfaces, the software development platform and the application software can complete data interaction with the database, and layout optimization, simulation verification and evaluation decision function module development are realized by referring to function realization logic.
And S13, on a user interaction layer, visually displaying a workshop global three-dimensional scene, a manufacturing element production state, a layout simulation result, a layout optimization result, an optimization scheme release and the like, and enabling a user to trace historical information data, inquire, add, modify and delete data according to roles with different authorities allocated by the system.
The basic information base mainly comprises basic user authority, operation logs, user management, information input and the like. The system user authority is divided into a common user and a system administrator. All operation records of user access, modification, deletion and the like are reserved in the form of operation logs, and system administrators and common users can conveniently check and retrieve the operation records.
The layout model library is mainly used for storing, modifying and calling physical and data models such as products, processes, resources and the like according to different classifications of manufacturing elements; in the embodiment, various entity models need to be called in the process of building the station layout Simulation model, the content in the layout model library is the index position of the model at the server end, and the entity models at the server end are all stored in a jt format, so that the Plant Simulation can be called conveniently.
In the embodiment, production related data (real-time facility position data, inter-facility dynamic material demand data, assembly work area demand data and the like) are stored in a database in a table form, and various types of data are updated regularly to ensure the timeliness of the related data, so that a user can upload, download, modify and query data in the database.
The optimization evaluation method library is used for storing, modifying and calling the intelligent algorithm and relevant optimization parameters, optimized data such as charts and texts, evaluation index data and decision methods; in this embodiment, a plurality of verified meta-heuristic algorithms (PSO, MPSO-SA, MMPSO, and the like), weight calculation methods (entropy method and ANP method), sorting methods (ANP method, advance method, and TOPSIS method), parameter settings, evaluation indexes, decision methods, optimization results, decision results, and the like corresponding to the algorithms are developed based on Matlab to perform classified storage, which facilitates calling.
As shown in fig. 4, further, the specific process of step S2 is as follows:
and S21, developing different layout optimization meta-heuristic algorithms (particle swarm, heredity, cuckoo algorithm and the like) based on MatlabR2014b software according to different problem types and optimization targets, and developing different layout optimization meta-heuristic algorithms (particle swarm, heredity, cuckoo algorithm and the like) based on MatlabR2014b software in the embodiment.
And S22, packaging each optimization algorithm of Matlab used in the system into a dll file by the layout optimization module, storing the dll file in a server, and constructing and managing an optimization evaluation method library in a database of the server.
S23: and calling the excellent solution of the historical algorithm and the verified simulation parameters in the database, or loading the algorithm and the related parameters into the system for running.
As shown in fig. 5, further, the specific process of step S3 is as follows:
s31: and loading the light CAD model required in the layout model database to a local client according to the actual layout scene. The simulation software can realize rapid import of the physical model loaded to the local client through the interface, and in this embodiment, the lightweight CAD model required in the layout model database is loaded to the local client according to the actual layout scenario. Plant Simulation software can rapidly import a physical model which is loaded to a local client through interfaces 3D.
S32: and loading the data model, reading the required data model and loading the data model to a local client, wherein the simulation software can directly acquire data in a corresponding format through a related function. The Simulation system has the advantages that Simulation data such as the rapid creation of a Simulation object, the capacity of a material buffer area, the material handling time and the facility fault probability can be automatically set in the Simulation system through the interface, in the embodiment, the ODBC is connected with the database, the data model is loaded, the required data model is read and loaded to the local client, and the Plant Simulation can directly acquire data in a corresponding format through readExcelFile, readTable or readXMLFile. The loadObjectAs interface is matched with the createObject to realize the automatic setting of simulation data such as the quick creation of simulation objects, the capacity of a material buffer area, the material handling time, the failure probability of facilities and the like in a simulation system.
S33: for the verified classical Simulation problem, the Simulation parameters in the database can be directly called, and the Simulation software can obtain the Matlab algorithm program with the completed code through the function read-write and call interface.
And S34, after the simulation operation is finished, outputting a simulation result in a chart form or directly issuing the simulation result into an executable program of the simulation model for the client to display or call.
As shown in fig. 6, further, the specific process of step S4 is as follows:
and S41, developing different fuzzy multi-attribute decision methods (such as an analytic hierarchy process, a network analytic hierarchy process, a TOPSIS method and the like) based on MatlabR2014b software.
S42: and packaging the multi-attribute decision method into a dll file, storing the dll file in a server, and constructing and managing an optimized evaluation method library in an optimized evaluation method library of the server.
In this embodiment, in step S5, the user may download and call the method, and at the same time, may upload a new decision method to update the database content, and issue an update message to the client in time.
In summary, the present invention associates the developed system with external software (Plant Simulation12 and MatlabR2016 b) and the database SQL Server 2015 through an interface, so as to implement data access, exchange and transmission. In the calling of different function modules, a classical model and a verified algorithm, loading facility appearance data, facility real-time position data, material demand data among facilities and the like can be called through a system interface association database to provide data and method support for layout simulation optimization, and the method can also enter different platforms of external software respectively, so that the operation on the model, the method and the data is facilitated, and powerful support is provided for improving the layout efficiency and the quality of the facilities.
The Ultra Wide Band (UWB) real-time collection facility position data is integrated, a discrete manufacturing workshop facility layout optimization method and a multi-attribute decision method are used, four functional modules of basic information and database management, layout optimization, simulation verification and evaluation decision are developed, a data-driven discrete manufacturing workshop layout optimization decision system is constructed, layout optimization, evaluation and decision are carried out on the discrete manufacturing workshop, and layout efficiency and quality are improved.
The invention sets corresponding layout optimization and evaluation decision function modules, improves the solving scale, and aims at specific layout problems and reflects the latest algorithm research results. In the invention, the developed system is associated with external software (Plant Simulation12 and MatlabR2014 b) and a database SQL Server 2015 through an interface, and a meta-heuristic and multi-attribute decision-making method is developed to realize personalized and customized function development and optimization.
The invention reduces the dependence on experience data or set parameters, reduces the dependence on labor for acquiring workshop production information, improves the real-time property of information, avoids the problem that certain hysteresis exists between simulation data and results and real time, and is suitable for directly guiding actual production.
According to the invention, the UWB technology is fused to acquire relevant facility position data in real time, a discrete manufacturing workshop facility layout optimization method and a multi-attribute decision method are integrated, a discrete manufacturing workshop facility layout optimization decision system is developed, and optimization and evaluation decision results provide scientific decision basis for actual production. The invention solves the technical problems of poor planning effect, dependence on manpower and poor information real-time performance in the prior art.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A data-driven discrete manufacturing shop layout optimization decision method, the method comprising:
s1, deploying a system architecture and a logic processing module of a workshop layout optimization decision system, and configuring and deploying hardware equipment, wherein the system architecture comprises: a resource supporting layer, a service logic layer and a user interaction layer;
s2, acquiring preset management upgrading data so as to maintain user basic information and operation logs in the workshop layout optimization decision system and upgrade a production management database, wherein the production management database is associated with the workshop layout optimization decision system;
s3, obtaining a layout problem type and optimization target dimension data from a production management database, obtaining problem type and optimization dimension difference data according to the layout problem type and the optimization target dimension data, calling a pre-packaging meta-heuristic algorithm according to the problem type and the optimization dimension difference data, and setting an applicable optimization parameter according to the pre-packaging meta-heuristic algorithm to obtain a layout optimization solution through processing;
s4, optimizing layout single targets and layout multiple targets in preset production system simulation software to verify material handling cost among facilities, non-logistics relationship among facilities and dynamic production re-layout cost to obtain a simulation verification result;
s5, extracting non-inferior solution sets in the simulation verification result, forming a decision scheme set to be selected by at least 2 non-inferior solutions in the non-inferior solution sets, calling a preset decision logic to judge the goodness degree between the non-inferior solutions, and obtaining a workshop layout optimization decision result, wherein the preset decision logic comprises: provided is a multi-attribute decision method.
2. The method according to claim 1, wherein the step S1 comprises:
s11, in the system architecture, the resource supporting layer is used as a software and hardware platform basic layer, and the hardware equipment is configured; the hardware device includes: the system comprises a server, a client, an ultra-wideband UWB sensor and workshop equipment, wherein the server is used for collecting the position data of the assembly facility in real time based on a verified UWB deployment scheme;
s12, constructing a business logic database at the business logic layer so as to provide user authority management service and database access service, performing data interaction among a software development platform, application software and the business logic database by using a preset interface, realizing development data of a logic acquisition layout optimization module, a simulation verification module and an evaluation decision module by using a preset function, and constructing a workshop layout optimization decision system;
s13, displaying workshop layout data in a preset visualization method on the user interaction layer, wherein the workshop layout data comprise: the method comprises the following steps of overall three-dimensional scene of a workshop, production state of manufacturing elements, layout simulation result, layout optimization result and optimization scheme release.
3. The method according to claim 2, wherein the step S12 comprises: the business logic database includes: a basic information base, a layout model base, a production database and an optimization evaluation method base.
4. The method of claim 1, wherein the step S2 comprises:
s21, constructing a basic information base by using user authority, an operation log, user management and information input data, setting system user authority, and reserving user operation data by using the operation log;
s22, constructing and calling a layout model library according to a preset manufacturing element model;
s23, establishing the production management database by using the production data, and managing real-time position data of facilities, basic data of various production resources, simulation historical data, data interaction among different types and historical data tracing data;
and S24, constructing an optimization evaluation method library by using the optimization evaluation logic data, and managing optimization processing logic and parameters, optimizing image-text data, evaluating index data and decision method data.
5. The method of claim 1, wherein the step S3 comprises:
s31, processing by adopting a MatlabR2014b tool according to the problem type and the optimization dimension difference data to obtain a layout optimization meta-heuristic algorithm;
s32, packaging the layout optimization meta-heuristic algorithm in the workshop layout optimization decision system into a dll file and storing the dll file in a preset server so as to construct and manage an optimization evaluation method library in a server database;
s33: and calling the excellent solution of the historical algorithm and the verified simulation parameters in the server database, loading the pre-packaged meta-heuristic algorithm and the heuristic algorithm parameters, and operating the workshop layout optimization decision system according to the loaded meta-heuristic algorithm and the heuristic algorithm parameters.
6. The data-driven discrete manufacturing plant layout optimization decision method according to claim 5, wherein the layout optimization meta-heuristic algorithm in step S31 comprises: particle swarm, genetic, cuckoo algorithms.
7. The method of claim 1, wherein the step S4 comprises:
s41, acquiring and loading a light CAD model required in a preset layout model database to the preset production system simulation software of the local client according to an actual layout scene;
s42, loading a data model, reading and loading the data model into the preset production system simulation software in the local client, wherein the simulation software can directly acquire data in a corresponding format through a related function so as to create a simulation object and set simulation data through a preset interface;
s43, calling the existing simulation parameters in the layout model database for the verified simulation problem;
and S44, optimizing the layout single target and the layout multiple targets by the preset production system simulation software according to the simulation data and the existing simulation parameters so as to verify the material handling cost among facilities, the non-logistics relationship among facilities and the dynamic production re-layout cost, and obtaining a simulation verification result for displaying and calling by a client.
8. The data-driven discrete manufacturing plant layout optimization decision method according to claim 7, wherein said step S42 creates said simulation object through said preset interface, and sets said simulation data, wherein said simulation data comprises: material buffer area capacity, material handling time, and facility failure probability.
9. The method of claim 1, wherein the step S5 comprises:
s51, developing a fuzzy multi-attribute decision method based on MatlabR2014b software, wherein the fuzzy multi-attribute decision method comprises the following steps: analytic hierarchy process, network analytic hierarchy process and TOPSIS process;
s52, packaging the fuzzy multi-attribute decision method into a dll file, storing the dll file in a preset server, and constructing and managing the optimized evaluation method in an optimized evaluation method library;
and S53, calling the optimization evaluation method so as to update the decision scheme set to be selected.
10. A data-driven discrete manufacturing shop layout optimization decision system, the system comprising:
the system deployment module is used for deploying a system architecture and a logic processing module of the workshop layout optimization decision-making system, and configuring and deploying hardware equipment, wherein the system architecture comprises: a resource supporting layer, a service logic layer and a user interaction layer;
a system basic information and database management module, configured to obtain preset management upgrade data, maintain user basic information and operation logs in the workshop layout optimization decision system, and upgrade a preset production database, where the production management database is associated with the workshop layout optimization decision system, and the system basic information and database management module is connected to the system deployment module and the system deployment module;
the development layout optimization module is used for acquiring a layout problem type and optimization target dimension data from the production management database, acquiring problem type and optimization dimension difference data, calling a pre-packaging meta-heuristic algorithm according to the problem type and optimization dimension difference data, setting an applicable optimization parameter according to the pre-packaging meta-heuristic algorithm, and processing the parameters to obtain a layout optimization solution, and is connected with the system basic information and database management module and the system deployment module;
the system comprises a development simulation verification module, a system deployment module and a system deployment module, wherein the development simulation verification module is used for optimizing layout single targets and layout multiple targets in preset production system simulation software so as to verify material handling cost among facilities, non-logistics relationship among facilities and dynamic production re-layout cost among facilities to obtain a simulation verification result;
the evaluation decision module is used for extracting a non-inferior solution set in the simulation verification result, forming a decision scheme set to be selected by at least 2 non-inferior solutions in the non-inferior solution set, calling a preset decision logic to judge the goodness and badness degree between the non-inferior solutions so as to obtain a workshop layout optimization decision result, wherein the preset decision logic comprises: and the evaluation decision module is connected with the development simulation verification module and the development layout optimization module.
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| CN116303749B (en) * | 2023-04-03 | 2023-10-20 | 新之航传媒科技集团有限公司 | Layout optimization method and system for intelligent exhibition hall management |
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