基于聚类分析与遗传算法的产品多样性优化研究
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摘要
大规模定制是能够以接近大规模生产的效率和成本为客户提供个性化定制产品的新型生产模式,其实施成败的关键在于能否有效解决同时满足客户个性化的特定需求且保持批量生产的规模效益之间的矛盾,其中产品设计与生产管理过程中有效的多样性决策至关重要,合理的多样性能够提供客户所需要的特定产品与服务,同时尽量提高产品之间的共性。目前的事实是企业缺乏有效的方法和工具支持科学的产品多样性决策,以期既要展现尽可能丰富的产品外部多样性,又要减少产品内部多样性以降低额外成本和时间。本文的研究目标是研究、开发或应用合适的计算方法,识别客户及产品间的共性与差异化知识,优化产品功能(外部)多样性与产品设计(内部)多样性,为大规模定制生产模式的实现提供方法支持。
首先,为提高客户满意度,需要对产品外部多样性作合理决策,客户需求分析势在必行,实际上这也是实现大规模定制的首要环节,本研究侧重于对历史交易记录进行分析、挖掘,从客户需求和产品特征两个角度分别划分客户群与产品类,从大量历史客户需求和产品信息中准确分析出客户的真实偏好及水平,在此基础上分析并预测具有动态特性的客户/功能需求的发展趋势,为产品外部多样性决策提供支持。同时,通过对产品的历史数据分析获取功能需求模式,优化产品功能多样性,并可利用现有产品中积聚的信息与知识帮助定义新产品,从而更有效的处理客户订单。本文结合聚类分析与信息熵理论提出一种新的功能多样性优化方法,用于分析历史数据以提取具有共性特征的功能需求模式。通过实例分析更加细致的阐述了该方法的整个实施过程,并将所获取的功能需求模式与其他方法得到的结果相比较,证明该方法的可行性及有效性。
在产品内部多样性优化方面,本文深入探讨了定量化的产品平台规划以及基于平台的产品族优化方法。目前多数产品族设计方法在平台已构造基础上进行,或者由设计者预先确定平台变量,然而,选择合理的平台与差异化变量组合对整个平台与最终的产品族设计至关重要;此外,大部分方法采用单平台策略,即全部产品在设计变量上取公共或完全不同的值,而多平台方法中,平台变量可依产品相似性取几个公共值,可获得更优的产品族全局设计方案。本文结合聚类分析、信息熵理论以及模糊理论中的有效性分析提出一种定量的平台规划方法。产品族设计的目标与面临的最大难题在于合理平衡产品间的共性与各自性能,成功的设计方法应能在满足客户个性化性能需求的前提下获取产品族内的最大共性。本文将研究参数化的多平台产品族优化问题及具体方法,特别是单阶段优化方法。两部分都结合通用电动机设计实例详细阐述了方法的具体实现步骤,并对设计方案与其它方法得到的结果作了比较分析,验证了本文方法的优势。
为克服传统方法工具的不足,本文还着重研究开发相应的计算方法,应用于解决产品多样性决策的相关核心主题。结合粗集模型与熵改进现有的聚类算法,以克服传统算法不能处理数值/语义属性空间与不完整数据、需要指定类数、聚类结果对数据输入顺序敏感等问题,并应用于产品功能多样性优化与平台规划中的共性知识识别。同时,提出并开发了具有两层染色体结构的遗传算法,用于解决多平台产品族设计优化问题,该算法在运行过程中可自动改变平台共性并搜索共性与产品性能之间的最佳平衡点,经过单次优化过程即可得到产品平台及其相应产品族的设计方案,在满足性能需求与约束的前提下获得最大产品族共性。通过实验数据分析与结果比较,证明了各算法的有效性。
Mass customization (MC) is a new production strategy which aims to provide customized products with near mass production efficiency and cost. The key is to solve the conflict between scale economies with batch production while meeting customers’individual requirements. Thus effective variety decision in the product design and production management process is critical. Reasonable variety can provide customers with specific products and service and at the same time increase the commonality among them. However, effective methods and tools are lack to support scientific product variety decision at present. The dissertation is to research and develop suitable methods and algorithms to solve key problems of product variety decision area, including optimization of both exterior product functions and interior design diversification and related common knowledge identification, to provide theory and method support for the implementation of MC strategy.
Function variety decision of products is needed to improve customer satisfaction and customer requirements (CRs) analysis is imperative. In fact, this is also a chief step to realize MC. Research of this dissertation focuses on analyzing and mining historical transaction records to discover customers’eal preference and level. Customers and products are to be grouped from customer requirements and product features, respectively, and based on this to analyze and predict trends of dynamic CRs and functional requirements (FRs), aiming to facilitate product definition and function variety decision. Then FRs patterns are to be captured by analyzing existing product data, with the aim to help customization using information stored in existing products and consequently to deal with orders more effectively. A novel method integrating clustering analysis and information entropy is proposed to recognize FRs patterns with higher commonality by historical FRs data analysis. Steps of the method are demonstrated by a case study and its feasibility and validity are proved through the comparison of results derived by other published methods.
Product design variety optimization, especially quantitative product platform planning and product family optimization, is another research focus of this dissertation. Many existing product family design methods assume a given platform configuration, i.e., the platform variables are specified a priori by designers. However, selecting the right combination of common and scaling variables is not trivial. Most approaches are single-platform methods, in which design variables are either shared across all product variants or not at all. While in multiple-platform design, platform variables can have special value with regard to a subset of product variants within the product family, offering opportunities for superior overall design. In this dissertation, a new quantitative platform planning method is presented combining clustering analysis, information theoretics, and validity analysis of fuzzy theory. The objective and obstacle of product family design lie in the optimal tradeoff of individual product performance and commonality among them. A successful product family design method should achieve an optimal tradeoff among a set of conflicting objectives, which involves maximizing commonality across the family of products without comporising the capability to satisfy customers’performance requirements. Multiple-objective optimization problems and suitable approaches for scaled-based multiple-platform product family design are to be studied in the dissertation, especially a single stage optimization approach. The single-stage approach can yield improvements in the overall performance of the product family compared with two-stage approaches, in which the first stage involves determining the best settings for the platform variables and values of unique variables are found for each product in the second stage. A case study of designing a set of universal electric motors is applied both to illustrate the methodology and verify its performance.
To overcome disadvantages of traditional algorithms, computation algorithms are studied and developed to solve relevant topics of product variety decision under MC environment. An improved clustering algorithm integrating rough set model and entropy is proposed. It aims at avoiding the need to pre-specify number of clusters, and clustering in both numerical and nominal attribute space with the similarity introduced to replace the distance index. At the same time, the RS theory endows the algorithm with the function to deal with vagueness and uncertainty in data analysis. Shannon’s entropy was used to refine the clustering results by assigning relative weights to the set of attributes according to the mutual entropy values. It’s applied to discover commonality knowledge for product function variety optimization and platform planning. This dissertation also presents and develops a two-level chromosome structured genetic algorithm to simultaneously determine the optimal settings for the product platform and corresponding family of products, by automatically varying the amount of platform commonality within the product family during a single optimization process. The augmented scope of 2LCGA allows multiple platforms to be considered during product family optimization, offering opportunities for superior overall design by more efficacious tradeoffs between commonality and performance. Validity and effectiveness of both algorithms are verified through case studies and results comparisons against previous work.
首先,为提高客户满意度,需要对产品外部多样性作合理决策,客户需求分析势在必行,实际上这也是实现大规模定制的首要环节,本研究侧重于对历史交易记录进行分析、挖掘,从客户需求和产品特征两个角度分别划分客户群与产品类,从大量历史客户需求和产品信息中准确分析出客户的真实偏好及水平,在此基础上分析并预测具有动态特性的客户/功能需求的发展趋势,为产品外部多样性决策提供支持。同时,通过对产品的历史数据分析获取功能需求模式,优化产品功能多样性,并可利用现有产品中积聚的信息与知识帮助定义新产品,从而更有效的处理客户订单。本文结合聚类分析与信息熵理论提出一种新的功能多样性优化方法,用于分析历史数据以提取具有共性特征的功能需求模式。通过实例分析更加细致的阐述了该方法的整个实施过程,并将所获取的功能需求模式与其他方法得到的结果相比较,证明该方法的可行性及有效性。
在产品内部多样性优化方面,本文深入探讨了定量化的产品平台规划以及基于平台的产品族优化方法。目前多数产品族设计方法在平台已构造基础上进行,或者由设计者预先确定平台变量,然而,选择合理的平台与差异化变量组合对整个平台与最终的产品族设计至关重要;此外,大部分方法采用单平台策略,即全部产品在设计变量上取公共或完全不同的值,而多平台方法中,平台变量可依产品相似性取几个公共值,可获得更优的产品族全局设计方案。本文结合聚类分析、信息熵理论以及模糊理论中的有效性分析提出一种定量的平台规划方法。产品族设计的目标与面临的最大难题在于合理平衡产品间的共性与各自性能,成功的设计方法应能在满足客户个性化性能需求的前提下获取产品族内的最大共性。本文将研究参数化的多平台产品族优化问题及具体方法,特别是单阶段优化方法。两部分都结合通用电动机设计实例详细阐述了方法的具体实现步骤,并对设计方案与其它方法得到的结果作了比较分析,验证了本文方法的优势。
为克服传统方法工具的不足,本文还着重研究开发相应的计算方法,应用于解决产品多样性决策的相关核心主题。结合粗集模型与熵改进现有的聚类算法,以克服传统算法不能处理数值/语义属性空间与不完整数据、需要指定类数、聚类结果对数据输入顺序敏感等问题,并应用于产品功能多样性优化与平台规划中的共性知识识别。同时,提出并开发了具有两层染色体结构的遗传算法,用于解决多平台产品族设计优化问题,该算法在运行过程中可自动改变平台共性并搜索共性与产品性能之间的最佳平衡点,经过单次优化过程即可得到产品平台及其相应产品族的设计方案,在满足性能需求与约束的前提下获得最大产品族共性。通过实验数据分析与结果比较,证明了各算法的有效性。
Mass customization (MC) is a new production strategy which aims to provide customized products with near mass production efficiency and cost. The key is to solve the conflict between scale economies with batch production while meeting customers’individual requirements. Thus effective variety decision in the product design and production management process is critical. Reasonable variety can provide customers with specific products and service and at the same time increase the commonality among them. However, effective methods and tools are lack to support scientific product variety decision at present. The dissertation is to research and develop suitable methods and algorithms to solve key problems of product variety decision area, including optimization of both exterior product functions and interior design diversification and related common knowledge identification, to provide theory and method support for the implementation of MC strategy.
Function variety decision of products is needed to improve customer satisfaction and customer requirements (CRs) analysis is imperative. In fact, this is also a chief step to realize MC. Research of this dissertation focuses on analyzing and mining historical transaction records to discover customers’eal preference and level. Customers and products are to be grouped from customer requirements and product features, respectively, and based on this to analyze and predict trends of dynamic CRs and functional requirements (FRs), aiming to facilitate product definition and function variety decision. Then FRs patterns are to be captured by analyzing existing product data, with the aim to help customization using information stored in existing products and consequently to deal with orders more effectively. A novel method integrating clustering analysis and information entropy is proposed to recognize FRs patterns with higher commonality by historical FRs data analysis. Steps of the method are demonstrated by a case study and its feasibility and validity are proved through the comparison of results derived by other published methods.
Product design variety optimization, especially quantitative product platform planning and product family optimization, is another research focus of this dissertation. Many existing product family design methods assume a given platform configuration, i.e., the platform variables are specified a priori by designers. However, selecting the right combination of common and scaling variables is not trivial. Most approaches are single-platform methods, in which design variables are either shared across all product variants or not at all. While in multiple-platform design, platform variables can have special value with regard to a subset of product variants within the product family, offering opportunities for superior overall design. In this dissertation, a new quantitative platform planning method is presented combining clustering analysis, information theoretics, and validity analysis of fuzzy theory. The objective and obstacle of product family design lie in the optimal tradeoff of individual product performance and commonality among them. A successful product family design method should achieve an optimal tradeoff among a set of conflicting objectives, which involves maximizing commonality across the family of products without comporising the capability to satisfy customers’performance requirements. Multiple-objective optimization problems and suitable approaches for scaled-based multiple-platform product family design are to be studied in the dissertation, especially a single stage optimization approach. The single-stage approach can yield improvements in the overall performance of the product family compared with two-stage approaches, in which the first stage involves determining the best settings for the platform variables and values of unique variables are found for each product in the second stage. A case study of designing a set of universal electric motors is applied both to illustrate the methodology and verify its performance.
To overcome disadvantages of traditional algorithms, computation algorithms are studied and developed to solve relevant topics of product variety decision under MC environment. An improved clustering algorithm integrating rough set model and entropy is proposed. It aims at avoiding the need to pre-specify number of clusters, and clustering in both numerical and nominal attribute space with the similarity introduced to replace the distance index. At the same time, the RS theory endows the algorithm with the function to deal with vagueness and uncertainty in data analysis. Shannon’s entropy was used to refine the clustering results by assigning relative weights to the set of attributes according to the mutual entropy values. It’s applied to discover commonality knowledge for product function variety optimization and platform planning. This dissertation also presents and develops a two-level chromosome structured genetic algorithm to simultaneously determine the optimal settings for the product platform and corresponding family of products, by automatically varying the amount of platform commonality within the product family during a single optimization process. The augmented scope of 2LCGA allows multiple platforms to be considered during product family optimization, offering opportunities for superior overall design by more efficacious tradeoffs between commonality and performance. Validity and effectiveness of both algorithms are verified through case studies and results comparisons against previous work.
引文
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