面对随机需求的产品平台配置生产模型

提出在需求不确定情况下,利用平台方法以最小的综合生产成本为目标生产给定产品族.该方法考虑每一产品类型的随机需求,以生产成本、平台库存持有成本和缺货成本三者作为决策变量,以带补偿的二阶随机规划对问题建模,并描述了模型的最优算法.通过一个产品族实例证明了该方法的有效性.     

机械产品族规划设计技术研究

大规模定制生产方式就是用尽量少的产品内部的多样化来满足客户的多样化需求,产品内部多样化的最小直接表现形式就是企业生产零部件的数量和种类最少。要实现这一目标,最有效的方式就是企业对产品进行产品族设计。     

物理规划方法及其在产品族设计中的应用

针对物理规划偏好函数的确定的难点,提出用模糊统计近似估计偏好函数的方法,并将物理规划方法应用于产品族设计中平台参数的确定,建立了产品族设计的物理规划模型.实证分析表明,物理规划方法得出的计算结果更优.最后给出了进一步研究的方向.     

基于模块化产品族与客户需求转化的产品配置

建立了模块化产品族结构,将产品族分解为类模块,为每一类模块建立一个模块模型,以属性变量表示各模块模型.利用QFD思想建立从客户需求到模块属性的映射,确定客户所需产品的各模块属性变量取值和权重.然后在同类模块的范围内检索出与客户需求最相近的模块实例集,再纳入各模块及属性之间的相关约束,有效地组合模块,避免大量无效配置,提高产品配置效率.     

基于图文法的产品族配置及其建模研究

根据产品族生成机理与图语言生成机理的相似性,提出了一种产品族的图文法表达方法,并根据产品族的图文法模型构建了基于图文法的产品族配置系统的体系结构.用UML对基于图文法的产品族配置系统进行用例建模、静态建模、动态建模以及编码与部署分析,从而为系统的具体开发做准备.     

约束对象的产品族模型知识表示及配置方法

针对目前产品族模型存在冗余、配置效率低、不易更新的问题,研究了基于约束对象的产品族模型的知识表示方法,有效地降低了产品结构冗余以及配置的复杂度,实现模型知识的有效封装.在此基础上研究了基于约束和实例的配置推理方法,给出了具体的配置推理过程.最后,给出了一个摩托车产品族建模的案例.该模型及其推理方法能够方便地利用计算机实现,可用于建立一个高效的产品族配置系统.     

面向产品族协同优化设计的模糊双层规划模型

针对含有不确定参数的产品族协同优化设计问题中的一种基本类型——产品参数与产品功能之间的映射含有模糊系数并且平台参数和个性化参数间具有一定的主从关系的情形，建立了一种对平台参数和非平台参数进行协同优化的模糊双层线性规划模型，并对其决策机制和求解方法进行了研究，给出一个数值算例。     

面向体验的产品族形象研究

分析了体验的内涵,对产品族的概念进行拓展;定义了产品族形象,提出基于核心理念层、中间特征层和外在综合表现层的产品族形象构成模型,产品族特征平台是产品族形象的基础;构建了以情境知识型线索和身体体验型线索为主体的产品族形象线索模型。     

面向产品族的模块化设计方法研究

为使模块化设计中模块之间的功能独立,在基于功能相关与结构相关的模块划分准则的基础上进行模糊聚类,通过设定阀值确定模块划分结果,并在此基础上对划分的模块进行变型设计,组成产品族,为客户的个性化需求提供产品设计方法．     

面向产品族的混流装配线平衡研究

基于产品族的模块化与通用性,研究了面向产品族装配的混流线平衡问题.基于对一族产品装配过程的聚类分析,提出了面向产品族的混流装配线的平衡设计方法和模型,可以为大规模定制企业实现面向产品族的混流装配提供参考,并用案例说明本文方法的应用.     

Solving the multiple platforms configuration problem

The focus of manufacturing has been shifting from mass production to mass customization and producers are seeking ways to reduce production costs, still offering a competitive basket of products. One approach for implementing mass customization is to develop or produce products based on platform architecture. Variant products make use of the product platform as the starting point and then add or remove components to change features of the base product. This allows the manufacturer to offer the variety of products that meet market demands without developing each product independently. In this paper, we propose multiple platforms for the production of a given product family while minimizing the overall production cost. The methodology considers the demand for each product variant, with the decision variables as the optimal number of platforms, optimal configuration of each platform, and assignment of the products to the platforms. The problem is formulated as a mixed integer program, and both the optimal formulation and an evolutionary strategy based on Genetic Algorithm are presented. The approach is illustrated with an example from a family of cordless drills.     

Joint decision of product configuration and remanufacturing for product family design

Product family design (PFD) is a popular method for increasing product variety to satisfy the needs of diversified markets. With the increasing concern for environmental friendliness in society, more and more companies develop launching remanufactured products and include them in their product families. Therefore, PFD should be considered in a broad decision space where configuration of product variants and remanufacturing are considered simultaneously. However, this issue was not addressed properly in previous research. In this paper, a methodology for joint decision of product configuration and remanufacturing is proposed in which a bi-objective mixed integer programming model is formulated to determine the configurations of both new and remanufactured products for minimising product cost, maximising total market share, and satisfying reliability requirements. Then, Non-dominated Sorting Genetic Algorithm II (NSGAII) is adopted to solve the optimization problem. Computational experiments were conducted and their results show that NSGAII is convergent to the model well. A case study is presented to illustrate the applicability and effectiveness of the proposed methodology.     

Web-based configuration design system for product customization

In mass customization, web-centric knowledge-based configuration systems have become an important tool to help customers configure products that satisfy their different needs and the complex configuration constraints of a product family (PF). An effective knowledge representation scheme and the associated reasoning mechanisms are essential for the success of these systems. This paper presents an object-oriented approach that integrates the functional and structural models of a PF with design process knowledge. Invasion-based search algorithms are proposed to deal with different situations in product customization and heuristic knowledge is introduced in these algorithms to improve the search efficiency. Optimization models have been constructed in terms of customers’ preferences to provide them with the highest satisfaction in product customization. Based on the representation scheme and reasoning algorithms, a web-based configuration system prototype is developed to support product customization. An example of a bicycle family is used as a study case and described in details.     

Inventory-shortage driven optimisation for product configuration variation

Inventory control is a critical problem in manufacturing systems. Inventory shortage significantly affects system productivity, while excessive stocks increase the operation cost. It is difficult to avoid fully inventory shortage under mass customisation manufacturing based on product configuration. In this paper, we propose a new approach for inventory-shortage driven optimisation of dynamic product configuration variation to meet the requirements of product configuration change and find suitable combination of parts by considering cost, lead-time and inventory variation. The multi-objective optimisation model uses a multi-objective genetic algorithm and adds impact cost, lead-time and inventory factors to the normal configuration optimisation model. An industrial case study demonstrates the practicality and effectiveness of the proposed approach. By means of this research, valid solutions for configuration variation are available to the decision makers.     

大批量定制环境中的产品配置规则

本文主要对产品配置中的配置机理及其实现进行研究;首先,论述了大批量定制模式中的产品配置技术,分析了目前基于规则驱动的产品配置技术的发展和优缺点;其次,讨论了面向产品族群的产品配置中的规则构件方法,并提出了基于此理论的产品配置框架;然后,重点阐述了面向产品族群的、通用的产品配置中规则的结构,并给出了规则配置的算法和规则匹配引擎的推理流程;最后,通过案例分析验证了此技术的可行性,并给出了基于此技术实现的产品配置系统的运行实例.     

Using product family evaluation graphs in product family design

Product family design and platform-based product development have garnered much attention. They have been used to provide nearly customised products to satisfy individual customer requirements and simultaneously achieve economies of scale during production. The inherent challenge in product family design is to balance the trade-off between product commonality (how well the components and functions can be shared across a product family) and variety (the range of different products in a product family). Quantifying this trade-off at the product family planning stage in a way that supports the engineering design process has yet to be accomplished. In this paper, we introduce a graphical evaluation method, the product family evaluation graph (PFEG), that allows designers to choose the ‘best’ product family design option among sets of alternatives based on their performance with respect to an ideal commonality/variety trade-off determined by a company's particular competitive focus, and guides designers towards a more desirable trade-off between commonality and variety in an existing product family. Two necessary supporting pieces for developing the PFEG are also proposed. One piece is the development of commonality and variety indices to quantitatively capture the degree of commonality and variety in a product family and its functions and components. We introduce two sets of commonality and variety indices–the CDI (commonality versus diversity index) for commonality (CDI_C) and variety (CDI_V), and the CMC (comprehensive metric for commonality) for commonality (CMC_C) and variety (CMC_V)–to achieve this. The other supporting piece is the development of a quantitative representation of the ideal trade-off between commonality and variety in a product family, known as the commonality/variety trade-off angle α, based on the elements that characterise a company's competitive focus and their industry-wide competitors. A linear regression model is used to link the qualitative competitive focus to a quantitative engineering perspective, and then to estimate the ideal trade-off angle. The commonality/variety trade-off angle can then be applied to the PFEG to help designers evaluate a product family or compare product family design alternatives. Most importantly, the PFEG is not just the graph of the two sets of indices; it is the representation of the commonality/variety trade-off relative to the desired competitive focus. Four families of power tools are used to illustrate how the computation of such indices supports product family design evaluation in the PFEG. In this paper, we only use the CDI in the example application, but the CMC can be computed using the same approach.     

A framework for designing balanced product platforms by estimating the versatility of components

Mass customisation is a common trend in many industries, and the platform-based product family strategy is a widely used method for this purpose. While the platform strategy can reduce the cost of variety by sharing common assets such as components and production processes, it has the risk of losing market share owing to its limitation on diversity. A balance between commonality and variety needs to be achieved when designing platforms that are both efficient and effective. In this paper, we focus on developing a platform that is versatile for highly effective differentiation to increase market share, incorporating the preferences of customers for different kinds of diversity. By distinguishing preferred and non-preferred diversity and ignoring the unnecessary need for differentiation, a platform designer can increase commonality without loss of market share. Under the assumption that a versatile platform is composed of versatile components, we estimate the versatility of components to identify the versatile ones. The estimation method consists of two phases: the market analysis phase, for identifying which specifications are preferred to be differentiated, and the product analysis phase, for assessing how much impact the differentiation may have on the component. A high versatility score indicates that the corresponding component is suitable for being platformed since it is not likely to be changed to increase market share. At the same time, a low versatility score provides a clue for improving the product architecture. The proposed method was applied to computer mouse design and yielded a reasonable platform plan.     

Adaptable product configuration system based on neural network

In the rapidly diversifying and globalising market, product configuration is implemented in a dynamic environment with continuous change of configuration knowledge. The adaptability of the product configuration system, which is defined as the capability to adjust product configurator, human resources and organisational resources to fit a new environment, is becoming more and more crucial. To keep the adaptability, this research suggests an adaptable product configuration (APC) system which transforms the development of the configuration system in a dynamic environment from a straightforward process to a closed circle. In the existing research on product configuration, most issues are addressed separately by different approaches and most approaches lack a systematic view which considers the interaction among product configurator, resources and environment. The circle of APC is therefore divided into several isolated stages and involves intensive human work, consumes a lot of organisational resources and results in a long response time. To successfully implement APC, this research adopts an artificial neural network and a specific rule extraction mechanism to develop a product configuration system. The neural network is able to automatically acquire configuration knowledge from historical transaction data and then directly apply it without further knowledge programming. Rule extraction mechanism has the capability to interpret the behaviour of the trained neural network and make it comprehensible and adjustable. Finally, knowledge acquisition, representation and application in product configuration are incorporated into the same connectionist methodology. And consequently, the APC circle is accelerated and the adaptability of the product configuration system is improved. A case study of computer configuration is presented.     

A methodology for developing product platforms in the specific setting of the housebuilding industry

Platform based strategies have proved to be a successful approach for achieving optimum balances between standardization and variation in many industries. However, application of this concept in the housebuilding industry is relatively new. This article describes a new methodology for developing product platform architectures in the specific setting of the housebuilding industry. This methodology comprises a reference framework describing the basic elements that constitutes a product platform, supported by a protocol for developing product platforms. The applicability of the proposed methodology has been tested at a Dutch housebuilding company. In this study, the methodology demonstrated its added value in determining which modules to standardize and defining a product platform. This article also describes a distinctive method of housing classification that is based on the spatial use of houses. Compared to the traditional classification system based on technical construction elements, the proposed new classification system facilitates a better translation of functional requirements into technical specifications.     

Flexible product platforms: framework and case study

Customization and market uncertainty require increased functional and physical bandwidth in product platforms. This paper presents a platform design process in response to such future uncertainty. The process consists of seven iterative steps and is applied to an automotive body-in-white where 10 out of 21 components are identified as potential candidates for embedding flexibility. The paper shows how to systematically pinpoint and value flexible elements in platforms. This allows increased product family profit despite uncertain variant demand, and specification changes. We show how embedding flexibility suppresses change propagation and lowers switching costs, despite an increase of 34% in initial investment for equipment and tooling. Monte Carlo simulation results of 12 future scenarios reveal the value of embedding flexibility.