面向产品族开发设计的知识发现方法与应用研究
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摘要
产品族开发设计(PFDD)是面向大规模定制开发设计的核心和基础,其目的是:通过对产品族的合理规划,以产品族为管理核心,以产品族通用结构为模型,将产品族视为开发设计流程的组织单元,应用产品族配置实现个性化定制,以较低的成本迅速满足客户的个性化需求。本文采用与产品族开发设计相关的企业信息资源作为基础数据源,针对性地面向产品族开发设计的不同环节研究特定类型的知识发现新方法和若干关键应用技术,包括产品族规划中的偏好型知识、产品族建模中的相似性知识、产品族配置中的聚类和关联知识、产品族配置性能的分类和预测知识等特定类型知识的发现方法,并将其应用到PFDD实践中,以挖掘产品族开发设计潜力、提高产品族开发设计能力和减少开发设计成本。
首先分析了产品族信息资源特点和在产品族开发设计流程中的信息分布情况,给出了面向产品族开发设计的知识发现(简称:KDD-PF)基本原理及其形式化描述,建立了KDD-PF层次框架体系。
其次,沿着PFDD基本流程,结合PFDD不同环节中的具体问题和知识偏重点,针对性地讨论了若干特定类型的知识发现新方法。具体包括:
(1)针对目前客户需求分析模型粒度不够细和对产品族规划决策支持能力不强的问题,在已有调查问卷分析的基础上,从客户主体特征的特有角度出发,基于群决策理论构建了群偏好模式下知识发现的优势概念产品决策信息表;采用扩展粗糙集模型发现产品族规划中的偏好知识,以科龙冰箱的市场问卷调查数据为基础进行了实例验证。结果表明:相对于传统的需求分析模型和产品族规划过程,产品族规划粒度更细,所挖掘的偏好知识对于目标产品定位和潜在客户挖掘具有更好的决策支持作用。
(2)基于形式概念分析(FCA)方法对零部件在产品族派生产品中的分布规律和分类形态进行了研究;对BOM实例聚类和相似性匹配算法进行了探讨。分别将这两种方法应用到科龙冰箱产品族建模中,结果表明:将FCA应用于产品族建模早期,能够对组件(零部件)在整个派生产品中的分布进行宏观控制,较Pareto方法更先进;BOM实例的相似度匹配提高了基于BOM的产品族建模效率,其方法可以应用到GBOM模型优化过程中。
(3)讨论了产品族配置中的聚类知识发现方法,进而构建由客户功能需求聚类和工程技术指标聚类组成的产品族配置目标决策信息表,综合采用由变精度粗糙集和模糊聚类组成的依赖度分析模型,以发现需求配置中的特例事务,并定量化度量“需求域->功能域”的映射效果;研究了工程配置中的“功能域->结构域”的配置关联规则和结构域中的配置约束发现方法。将该方法应用到电动自行车产品族配置过程中,其结果表明:产品族配置中的聚类和关联配置知识可以丰富传统的配置知识库,增强配置柔性;定量化依赖度分析模型为度量“需求域->功能域”的映射效果提供了新方法,为发现配置特例提供了理论依据。
(4)针对目前配置性能参数值只能通过试验手段来获取、配置过程更改频繁和配置重用性不高的问题,基于产品族配置历史数据,综合应用遗传算法、粗糙集理论和神经网络模型,提出了产品族配置性能的预测型知识发现新方法,对新配置实例进行性能预测,并将预测值作为衡量客户需求满意度的直接依据。将该方法应用到科龙几款新型冰箱产品族的性能预测过程中,其结果表明:配置性能预测作为传统配置过程的补充,可以变传统的被动配置为主动配置,提高对客户需求的响应速度,提高产品族配置性能预测知识的重用能力,其配置预测精度、预测知识的可信度和可复用度都较高。
接着初步开发了KDD-PF的平台原型。对平台的架构和具体功能进行了研究,并给出了以上几个实例的具体知识发现过程中界面及其结果表示。
最后对全文进行了总结,指出了进一步的研究方向。
Product family development and design(PFDD)methodology is the core and fundamental of development and design for mass customization. Its objective lies in how to realize the individual customization and satisfy the individual requirements with less cost through effective product family planning, taking the product family as the management core, on the basis of the generic product family structure and regarding the product family as the organization units of the development process. The thesis aims at discovering all kinds of knowledge at different PFDD stages, furthermore applying the knowledge into engineering practices of PFDD. Such kinds of knowledge include the dominance knowledge in product family planning, similarity analysis in product family modeling, clustering knowledge and association rule discovery in product family configuration, classification and predictive knowledge discovery of configuration performance and they are discovered from different sorts of information system implemented in Enterprise. In this way, the potential of traditional PFDD can be excavated and the ability of PFDD is improved, the cost of PFDD is decreased accordingly as well.
Firstly, the principles and infrastructure of Knowledge Discovery in Database for Product Family development and design (KDD-PF) is presented and the generic product family development and design process is described as well. The basic principles and formulations are addressed and the hierarchy framework is built up in which some important concepts and definitions in KDD-PF are introduced, as well as the formal representation method of the knowledge discovery process in PFDD.
Secondly, along with the process of the product family development and design process, some knowledge discovery methodology and application technologies are showed as follows:
(1) Combined with the traditional utility-based difference analysis for customer group, the rough analysis is carried out preliminarily. In order to carry out the more elaborate customer requirement analysis model and increase the effectiveness of decision support for product family planning, from the specific perspective of the customer agent features, the requirement information views and planning decision information tables in the mode of customer group preferences are achieved in terms of the survey questionnaires and group-decision theory, while not taking into account the conventional customer requirement features alone. Moreover, the dominant knowledge discovery for product family planning is discussed based on the extended rough set model. The methodology is verified by the market information of Kelon refrigeration and it is showed that the dominant knowledge process is more elaborate than the conventional requirement analysis, meanwhile the knowledge discovered are significant for the product family planning.
(2) The distribution status and classification formulations of the parts and components of the derivation products are analyzed innovatively based on Formal Concept Analysis (FCA). In addition, BOM is the most popular and efficient representation in product family structure modeling, and most product family history information are stored as the form of BOM, therefore, the research focuses on improvement of the clustering of BOM instances and similarity matching algorithm. By applying FCA into the early stage of product family modeling, the distribution of all the components in all the product variants can be clarified and manipulated from the macro perspective, so it is more superior to the traditional Pareto method. Additionally, Similarity matching of BOM instances can increase the BOM–based product family modeling efficiency to some extent and can be utilized into the GBOM based model optimization.
(3) Clustering knowledge discovery in product family configuration is discussed also, including the configuration requirement clustering and product functional specification clustering. Furthermore, the quantitative analyze model integrating variable precision rough set (VPRS) and fuzzy clustering is adopted to find out the unusual transactions in requirement configuration and to measure the performance of the mapping between requirement domain and function domain, the configuration rule discovery and configuration constraint discovery in physical domain are studied as well. The methodology is applied into the electrical power bicycles configuration and it is shown that on the one hand the discovered knowledge can enrich the traditional configuration knowledge base and increase the configuration flexibility, On the other hand quantitative analysis model provides the novel method to measure the mapping performance between requirement domain and function domain, also provides the theoretical base for discovering the unusual and unreasonable configuration.
(4) The configuration performances are usually be achieved by experiment which leads to the low configuration reusability and frequent configuration changes. Based on the historical information of product family configuration, the attribute reduction algorithm based on Genetic Algorithm (GA) is proposed, as well as the integration of rough set and Artificial Neural Network (ANN). In the way the performance of the newly configuration instance can be predicted and the predicted value can be regarded as the indexes to evaluate the satisfaction degree of customer requirement. The proposed method is verified through a refrigerator product family. The results show that configuration performance prediction can turn the passive configuration into initiative configuration, decreasing the configuration period, increasing the reusability of the configuration performance prediction knowledge and the prediction precision, the reliability and reusability of the performance knowledge utilizing this method is better than the knowledge acquired by other methods.
Then, the platform prototype of KDD-PF is developed including the platform selection, the main function modules and main realization interfaces.
Finally, a conclusion is drawn and the trend on KDD-PF is anticipated.
首先分析了产品族信息资源特点和在产品族开发设计流程中的信息分布情况,给出了面向产品族开发设计的知识发现(简称:KDD-PF)基本原理及其形式化描述,建立了KDD-PF层次框架体系。
其次,沿着PFDD基本流程,结合PFDD不同环节中的具体问题和知识偏重点,针对性地讨论了若干特定类型的知识发现新方法。具体包括:
(1)针对目前客户需求分析模型粒度不够细和对产品族规划决策支持能力不强的问题,在已有调查问卷分析的基础上,从客户主体特征的特有角度出发,基于群决策理论构建了群偏好模式下知识发现的优势概念产品决策信息表;采用扩展粗糙集模型发现产品族规划中的偏好知识,以科龙冰箱的市场问卷调查数据为基础进行了实例验证。结果表明:相对于传统的需求分析模型和产品族规划过程,产品族规划粒度更细,所挖掘的偏好知识对于目标产品定位和潜在客户挖掘具有更好的决策支持作用。
(2)基于形式概念分析(FCA)方法对零部件在产品族派生产品中的分布规律和分类形态进行了研究;对BOM实例聚类和相似性匹配算法进行了探讨。分别将这两种方法应用到科龙冰箱产品族建模中,结果表明:将FCA应用于产品族建模早期,能够对组件(零部件)在整个派生产品中的分布进行宏观控制,较Pareto方法更先进;BOM实例的相似度匹配提高了基于BOM的产品族建模效率,其方法可以应用到GBOM模型优化过程中。
(3)讨论了产品族配置中的聚类知识发现方法,进而构建由客户功能需求聚类和工程技术指标聚类组成的产品族配置目标决策信息表,综合采用由变精度粗糙集和模糊聚类组成的依赖度分析模型,以发现需求配置中的特例事务,并定量化度量“需求域->功能域”的映射效果;研究了工程配置中的“功能域->结构域”的配置关联规则和结构域中的配置约束发现方法。将该方法应用到电动自行车产品族配置过程中,其结果表明:产品族配置中的聚类和关联配置知识可以丰富传统的配置知识库,增强配置柔性;定量化依赖度分析模型为度量“需求域->功能域”的映射效果提供了新方法,为发现配置特例提供了理论依据。
(4)针对目前配置性能参数值只能通过试验手段来获取、配置过程更改频繁和配置重用性不高的问题,基于产品族配置历史数据,综合应用遗传算法、粗糙集理论和神经网络模型,提出了产品族配置性能的预测型知识发现新方法,对新配置实例进行性能预测,并将预测值作为衡量客户需求满意度的直接依据。将该方法应用到科龙几款新型冰箱产品族的性能预测过程中,其结果表明:配置性能预测作为传统配置过程的补充,可以变传统的被动配置为主动配置,提高对客户需求的响应速度,提高产品族配置性能预测知识的重用能力,其配置预测精度、预测知识的可信度和可复用度都较高。
接着初步开发了KDD-PF的平台原型。对平台的架构和具体功能进行了研究,并给出了以上几个实例的具体知识发现过程中界面及其结果表示。
最后对全文进行了总结,指出了进一步的研究方向。
Product family development and design(PFDD)methodology is the core and fundamental of development and design for mass customization. Its objective lies in how to realize the individual customization and satisfy the individual requirements with less cost through effective product family planning, taking the product family as the management core, on the basis of the generic product family structure and regarding the product family as the organization units of the development process. The thesis aims at discovering all kinds of knowledge at different PFDD stages, furthermore applying the knowledge into engineering practices of PFDD. Such kinds of knowledge include the dominance knowledge in product family planning, similarity analysis in product family modeling, clustering knowledge and association rule discovery in product family configuration, classification and predictive knowledge discovery of configuration performance and they are discovered from different sorts of information system implemented in Enterprise. In this way, the potential of traditional PFDD can be excavated and the ability of PFDD is improved, the cost of PFDD is decreased accordingly as well.
Firstly, the principles and infrastructure of Knowledge Discovery in Database for Product Family development and design (KDD-PF) is presented and the generic product family development and design process is described as well. The basic principles and formulations are addressed and the hierarchy framework is built up in which some important concepts and definitions in KDD-PF are introduced, as well as the formal representation method of the knowledge discovery process in PFDD.
Secondly, along with the process of the product family development and design process, some knowledge discovery methodology and application technologies are showed as follows:
(1) Combined with the traditional utility-based difference analysis for customer group, the rough analysis is carried out preliminarily. In order to carry out the more elaborate customer requirement analysis model and increase the effectiveness of decision support for product family planning, from the specific perspective of the customer agent features, the requirement information views and planning decision information tables in the mode of customer group preferences are achieved in terms of the survey questionnaires and group-decision theory, while not taking into account the conventional customer requirement features alone. Moreover, the dominant knowledge discovery for product family planning is discussed based on the extended rough set model. The methodology is verified by the market information of Kelon refrigeration and it is showed that the dominant knowledge process is more elaborate than the conventional requirement analysis, meanwhile the knowledge discovered are significant for the product family planning.
(2) The distribution status and classification formulations of the parts and components of the derivation products are analyzed innovatively based on Formal Concept Analysis (FCA). In addition, BOM is the most popular and efficient representation in product family structure modeling, and most product family history information are stored as the form of BOM, therefore, the research focuses on improvement of the clustering of BOM instances and similarity matching algorithm. By applying FCA into the early stage of product family modeling, the distribution of all the components in all the product variants can be clarified and manipulated from the macro perspective, so it is more superior to the traditional Pareto method. Additionally, Similarity matching of BOM instances can increase the BOM–based product family modeling efficiency to some extent and can be utilized into the GBOM based model optimization.
(3) Clustering knowledge discovery in product family configuration is discussed also, including the configuration requirement clustering and product functional specification clustering. Furthermore, the quantitative analyze model integrating variable precision rough set (VPRS) and fuzzy clustering is adopted to find out the unusual transactions in requirement configuration and to measure the performance of the mapping between requirement domain and function domain, the configuration rule discovery and configuration constraint discovery in physical domain are studied as well. The methodology is applied into the electrical power bicycles configuration and it is shown that on the one hand the discovered knowledge can enrich the traditional configuration knowledge base and increase the configuration flexibility, On the other hand quantitative analysis model provides the novel method to measure the mapping performance between requirement domain and function domain, also provides the theoretical base for discovering the unusual and unreasonable configuration.
(4) The configuration performances are usually be achieved by experiment which leads to the low configuration reusability and frequent configuration changes. Based on the historical information of product family configuration, the attribute reduction algorithm based on Genetic Algorithm (GA) is proposed, as well as the integration of rough set and Artificial Neural Network (ANN). In the way the performance of the newly configuration instance can be predicted and the predicted value can be regarded as the indexes to evaluate the satisfaction degree of customer requirement. The proposed method is verified through a refrigerator product family. The results show that configuration performance prediction can turn the passive configuration into initiative configuration, decreasing the configuration period, increasing the reusability of the configuration performance prediction knowledge and the prediction precision, the reliability and reusability of the performance knowledge utilizing this method is better than the knowledge acquired by other methods.
Then, the platform prototype of KDD-PF is developed including the platform selection, the main function modules and main realization interfaces.
Finally, a conclusion is drawn and the trend on KDD-PF is anticipated.
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