制造业产品配置管理的若干关键技术研究
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摘要
产品配置管理一直是制造业重点关注的研究领域,是制造企业快速响应市场、制定并执行各个企业的产品发展策略、优化企业基于产品配置的管理流程,以及同先进的信息科技相结合,在虚拟制造、敏捷制造、协同设计、数字化工厂等先进理念指导下,选择最优的合作伙伴,在基于Web的异构分布式数据库环境下完成产品配置的虚拟组合、优化和动态模拟,从而为制造企业的实际运作提供有力的工具。
本文正是基于这样一种产品配置管理思路的基础上,首先归纳总结了目前产品配置管理的主要方法和设计技术,提出了产品生命周期概念的配置管理模板,然后基于智能多Agent技术进行产品配置管理的动态建模,基于协同过滤、模糊聚类、组合进化等优化算法进行产品配置的相应优化,最后对众多企业基于Web的异构分布式数据环境下的数据集成接口进行了深入研究。
基于本文所提的研究理论和方法,本文给出了国内铁路车辆制造行业在产品配置管理方面的应用,以及其与吉林大学合作基于上海地铁运营部门平台开发的异构分布式智能维护信息管理系统。
产品配置管理,是一项浩大的、跨越众多领域的系统工程,本文最后对本文关注的研究技术给出了未来的研究重点和方向。
Product configuration management(PCM) has been a powerful weapon for manufacturing enterprises to analyze markets clearly, responds quickly to diverse individual needs of customers, shortens the product development and delivery time, makes the most economic product life-cycle model, ensures stable and reliable product quality, achieves network enterprise collaboration under web-based distributed heterogeneous environment. This article uses PCM of whole product life-cycle as the research object based on summarization and generalization of PCM methods and mainstream technology, applies dynamic modeling of PCM based on intelligent multi-agent technology, also based on the PCM templates creation of the whole configuration life-cycle, creates then restriction rules library through the extraction of core elements, e.g. PCM related key performance indicators (KPIs) ; optimizes the dynamic models of PCM through collaborative filtering arithmetic, clustering analysis arithmetic, organizational evolution arithmetic, etc. meanwhile, this chapter focuses on the research of data interface integration technology of multi-enterprises under web-based distributed heterogeneous data environment. Finally, based on above theory and method, through the actual application of the concept model of PCM in one railway vehicle manufacturing enterprise, and also the intelligent maintenance information management system developed through the cooperation between the company and Jilin University based on web distributed heterogeneous data environment based on Shanghai Metro Operation system platform, all these prove that the theory and method put forward in this article is effective and feasible.
The article is organized as follows:
In Chapter one, this chapter firstly presents the importance of PCM on surviving and developing of manufacturing enterprises and the necessity of research of PCM; based on the research route map of this article, this chapter presents systematically the main technology development and current application status of PCM, summarizes and generalizes the main research methods of PCM, i.e. restriction based method, rules based method, resources based method, ontology based method, logic based method, case based method, etc. discusses deeply the mainstream design technology of PCM, i.e. modular design technology, collaborative design technology, evolution design technology and knowledge engineering technology, etc. then finally clarities the research background, application technology and research contents of this article.
In Chapter two, this chapter puts forward the life-cycle concept of PCM of manufacturing products, based on the idea of product life-cycle management (PLM), which involves PCM at engineering stage (As Design), PCM at manufacturing stage (As Build) and PCM at maintenance stage (As Maintain); among which the engineering stage is the core for creating dynamic, full description of product parts and structure management, file management and drawings management, etc. which describes sufficiently the core control elements at engineering stage, e.g. part revisions management, batches validation management, milestone management, etc. Based on above idea and actual production requirements, we can create multi-directional views of concerned PCM at engineering stage, manufacturing stage and maintenance stage.
In Chapter three, this chapter applies the dynamic modeling of product life-cycle configuration management through widely used intelligent multi-agent technology; this chapter gives the system organization architecture based on human perception concept, firstly constructs the system response to customers through the customer requirements and configuration restriction modeling layer of the central processing sub-system, applies the consistency analysis of system configuration data for the extracted customer requirement information through three kinds of rules-style checking methods based on core system database and knowledge base, submits then the statistic results and reports to the decision and planning layer of this sub-system; decision and planning layer involves three groups of agents, which are responsible for the consistency monitoring of PCM, optimization of PCM and product configuration variation management respectively, and finally submits the execution plan to the execution sub-system for its execution. Central control sub-system is responsible for the monitoring, evaluation and management of all the agents inside the system. The system also designs one human-machine interactive interface, which is used for the intervention of designers or operators when necessary. This chapter finally points out that the core question of the research in this chapter is to solve the product configuration changing management based on the actual production features of large scale manufacturing enterprises.
In Chapter four, based on the actual features of large scale manufacturing enterprises, i.e. multi-versions of product parts, frequent changes during product life-cycle processes, etc, this chapter applies product configuration modeling analysis through adopting advanced arithmetic, e.g. collaborative filtering arithmetic, clustering analysis arithmetic and organizational evolution arithmetic, creates PCM rules library based on concerned key performance indicators (KPIs) of PCM, e.g. economic, reliable models of PCM, etc., and achieves dynamic feedback style re-building of PCM. This chapter describes the details of the applications of collaborative filtering arithmetic, clustering analysis arithmetic and organizational evolution arithmetic in PCM.
In Chapter five, as an extension of traditional PCM and in order to meet the requirements of multi-enterprises collaboration under web-based distributed heterogeneous data environment, this chapter deeply applies the analysis and research on its achievement of deep-web based data interface integration technology, gives the specific achievement method and process, applies sufficient tests based web open research resources, the final testing results prove that the method of this chapter is effective and feasible.
In Chapter six, based on the theory and method framework of this article, this chapter lists all the main application views what one railway vehicle manufacturing enterprise has applied on creating the concept model of life-cycle PCM management; meanwhile, this chapter also lists the main framework and views of one intelligent maintenance information management system under web-based distributed heterogeneous data environment, developed through the cooperation between the company and Jilin University based on Shanghai Metro Operation system platform.
In Chapter seven, this chapter summarizes and generalizes the research theory and method of life-cycle PCM management what is put forward in this article; points out the future research direction on fining and reasoning mechanism of agent, advanced PCM optimization arithmetic, etc. This article also points out that manufacturing PCM is a huge system engineering project, which needs the deep research and cooperation of more scholars, research institutions and large-scale enterprises. For the achievement of multi-enterprises collaboration under web-based distributed heterogeneous data environment, data interface integration technology based on deep-web is still the mainstream research direction and a new research hot spot, which still needs more researching works on it.
本文正是基于这样一种产品配置管理思路的基础上,首先归纳总结了目前产品配置管理的主要方法和设计技术,提出了产品生命周期概念的配置管理模板,然后基于智能多Agent技术进行产品配置管理的动态建模,基于协同过滤、模糊聚类、组合进化等优化算法进行产品配置的相应优化,最后对众多企业基于Web的异构分布式数据环境下的数据集成接口进行了深入研究。
基于本文所提的研究理论和方法,本文给出了国内铁路车辆制造行业在产品配置管理方面的应用,以及其与吉林大学合作基于上海地铁运营部门平台开发的异构分布式智能维护信息管理系统。
产品配置管理,是一项浩大的、跨越众多领域的系统工程,本文最后对本文关注的研究技术给出了未来的研究重点和方向。
Product configuration management(PCM) has been a powerful weapon for manufacturing enterprises to analyze markets clearly, responds quickly to diverse individual needs of customers, shortens the product development and delivery time, makes the most economic product life-cycle model, ensures stable and reliable product quality, achieves network enterprise collaboration under web-based distributed heterogeneous environment. This article uses PCM of whole product life-cycle as the research object based on summarization and generalization of PCM methods and mainstream technology, applies dynamic modeling of PCM based on intelligent multi-agent technology, also based on the PCM templates creation of the whole configuration life-cycle, creates then restriction rules library through the extraction of core elements, e.g. PCM related key performance indicators (KPIs) ; optimizes the dynamic models of PCM through collaborative filtering arithmetic, clustering analysis arithmetic, organizational evolution arithmetic, etc. meanwhile, this chapter focuses on the research of data interface integration technology of multi-enterprises under web-based distributed heterogeneous data environment. Finally, based on above theory and method, through the actual application of the concept model of PCM in one railway vehicle manufacturing enterprise, and also the intelligent maintenance information management system developed through the cooperation between the company and Jilin University based on web distributed heterogeneous data environment based on Shanghai Metro Operation system platform, all these prove that the theory and method put forward in this article is effective and feasible.
The article is organized as follows:
In Chapter one, this chapter firstly presents the importance of PCM on surviving and developing of manufacturing enterprises and the necessity of research of PCM; based on the research route map of this article, this chapter presents systematically the main technology development and current application status of PCM, summarizes and generalizes the main research methods of PCM, i.e. restriction based method, rules based method, resources based method, ontology based method, logic based method, case based method, etc. discusses deeply the mainstream design technology of PCM, i.e. modular design technology, collaborative design technology, evolution design technology and knowledge engineering technology, etc. then finally clarities the research background, application technology and research contents of this article.
In Chapter two, this chapter puts forward the life-cycle concept of PCM of manufacturing products, based on the idea of product life-cycle management (PLM), which involves PCM at engineering stage (As Design), PCM at manufacturing stage (As Build) and PCM at maintenance stage (As Maintain); among which the engineering stage is the core for creating dynamic, full description of product parts and structure management, file management and drawings management, etc. which describes sufficiently the core control elements at engineering stage, e.g. part revisions management, batches validation management, milestone management, etc. Based on above idea and actual production requirements, we can create multi-directional views of concerned PCM at engineering stage, manufacturing stage and maintenance stage.
In Chapter three, this chapter applies the dynamic modeling of product life-cycle configuration management through widely used intelligent multi-agent technology; this chapter gives the system organization architecture based on human perception concept, firstly constructs the system response to customers through the customer requirements and configuration restriction modeling layer of the central processing sub-system, applies the consistency analysis of system configuration data for the extracted customer requirement information through three kinds of rules-style checking methods based on core system database and knowledge base, submits then the statistic results and reports to the decision and planning layer of this sub-system; decision and planning layer involves three groups of agents, which are responsible for the consistency monitoring of PCM, optimization of PCM and product configuration variation management respectively, and finally submits the execution plan to the execution sub-system for its execution. Central control sub-system is responsible for the monitoring, evaluation and management of all the agents inside the system. The system also designs one human-machine interactive interface, which is used for the intervention of designers or operators when necessary. This chapter finally points out that the core question of the research in this chapter is to solve the product configuration changing management based on the actual production features of large scale manufacturing enterprises.
In Chapter four, based on the actual features of large scale manufacturing enterprises, i.e. multi-versions of product parts, frequent changes during product life-cycle processes, etc, this chapter applies product configuration modeling analysis through adopting advanced arithmetic, e.g. collaborative filtering arithmetic, clustering analysis arithmetic and organizational evolution arithmetic, creates PCM rules library based on concerned key performance indicators (KPIs) of PCM, e.g. economic, reliable models of PCM, etc., and achieves dynamic feedback style re-building of PCM. This chapter describes the details of the applications of collaborative filtering arithmetic, clustering analysis arithmetic and organizational evolution arithmetic in PCM.
In Chapter five, as an extension of traditional PCM and in order to meet the requirements of multi-enterprises collaboration under web-based distributed heterogeneous data environment, this chapter deeply applies the analysis and research on its achievement of deep-web based data interface integration technology, gives the specific achievement method and process, applies sufficient tests based web open research resources, the final testing results prove that the method of this chapter is effective and feasible.
In Chapter six, based on the theory and method framework of this article, this chapter lists all the main application views what one railway vehicle manufacturing enterprise has applied on creating the concept model of life-cycle PCM management; meanwhile, this chapter also lists the main framework and views of one intelligent maintenance information management system under web-based distributed heterogeneous data environment, developed through the cooperation between the company and Jilin University based on Shanghai Metro Operation system platform.
In Chapter seven, this chapter summarizes and generalizes the research theory and method of life-cycle PCM management what is put forward in this article; points out the future research direction on fining and reasoning mechanism of agent, advanced PCM optimization arithmetic, etc. This article also points out that manufacturing PCM is a huge system engineering project, which needs the deep research and cooperation of more scholars, research institutions and large-scale enterprises. For the achievement of multi-enterprises collaboration under web-based distributed heterogeneous data environment, data interface integration technology based on deep-web is still the mainstream research direction and a new research hot spot, which still needs more researching works on it.
引文
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