A guided search genetic algorithm using mined rules for optimal affective product design

Affective design is an important aspect of new product development, especially for consumer products, to achieve a competitive edge in the marketplace. It can help companies to develop new products that can better satisfy the emotional needs of customers. However, product designers usually encounter difficulties in determining the optimal settings of the design attributes for affective design. In this article, a novel guided search genetic algorithm (GA) approach is proposed to determine the optimal design attribute settings for affective design. The optimization model formulated based on the proposed approach applied constraints and guided search operators, which were formulated based on mined rules, to guide the GA search and to achieve desirable solutions. A case study on the affective design of mobile phones was conducted to illustrate the proposed approach and validate its effectiveness. Validation tests were conducted, and the results show that the guided search GA approach outperforms the GA approach without the guided search strategy in terms of GA convergence and computational time. In addition, the guided search optimization model is capable of improving GA to generate good solutions for affective design.     

基于优化的产品平台参数规划方法

 为满足客户化和全球竞争的需求,企业要实现大规模定制(mass customization,MC).基于公共产品平台的产品族设计是实现大规模定制的一种有效方式,而平台规划是面向产品族设计方法学的核心内容,也是目前研究中的一个热点问题.基于模型参数的平台设计是其方法之一.针对基于一系列标准可变参数的产品平台,用优化方法对产品平台参数进行规划,以满足各种客户需求.该规划方法无需事先人为指定,而是在满足客户需求的前提下,尽可能提高产品族中设计变量的共性,从而确定最好的产品平台的公共参数及其最优值,以及个性参数及其变化值,并以带式输送机为例验证了该方法.     

A framework for affective customer needs in product design

A systematic framework is proposed to conceptualize customer needs in product design. Customer needs were derived for current and future electronic devices in automobiles. Subjects rated their preferences for 15 product attributes on 10-point semantic differential scales. Using factor analysis, three generic factors were extracted, namely holistic attributes, styling and functional design. Depending upon the familiarity of the device, there were clear differences among potential customers. Unknown devices such as a navigation map were assessed first hand by using holistic attributes. Familiar designs such as car radio and cell phone were assessed using styling and functionality attributes. Customer reactions and preferences may be caused by product design parameters that operate either through their perceptual attributes or from the experience they acquire in using the artifacts or interfaces. There are both functional and affective needs. Functional (or cognitive) customer needs can be derived top-down, using product design features. Affective customer needs are difficult to derive top-down—typically they are evaluated by looking at several design propositions.     

Fuzzy expected value modelling approach for determining target values of engineering characteristics in QFD

Quality function deployment (QFD) is a planning and problem-solving tool that is renowned for translating customer requirements into the technical attributes of a product. To deal with the imprecise elements in the development process, fuzzy set theory is incorporated into QFD methodology. A novel fuzzy expected value operator approach is proposed in this paper to model the QFD process in a fuzzy environment, and two fuzzy expected value models are established to determine the target values of engineering characteristics in handling different practical design scenarios. Analogous to stochastic programming, the underlying philosophy in the proposed approach is based on selecting the decision with maximum expected returns. Furthermore, the proposed approach considers not only the inherent fuzziness in the relationships between customer requirements and engineering characteristics, but also the correlation among engineering characteristics. These two kinds of fuzzy relationships are aggregated to give the fuzzy importance of individual engineering characteristics. Finally, an example of a quality improvement problem of a motor car design is given to demonstrate the application and performance of the proposed modelling approach.     

Optimized sampling design and rationale for verification and validation

An optimized sampling design that meets customer, design, or process requirements, while balancing technology limitations, is still a common challenge to engineering communities. This is especially true in the medical device industry. Acceptance sampling plans for manufacturing are widely available, but the appropriate sampling plans for verification and validation (V&V) are less well known. This paper applies established statistical theory to derive sampling plans appropriate for estimating product reliability during V&V, where reliability must exceed an established threshold with an appropriate margin of statistical confidence. The paper provides insight on how to estimate parameters of interest and interpret acceptance criteria. Operating characteristic curves are used to examine if a design or process is capable of producing future product that meets design specifications and/or customer requirements in terms of confidence and reliability. The methodology is applied to both attribute and variable sampling plans, including examples showing how to achieve a high probability of passing the acceptance criteria. Formulas, sample size tables, and operating characteristic curves are provided for engineering practitioners to use. The paper aims at providing a practical quantitative approach and a valid statistical rationale to assess overall product quality during V&V.     

A data-driven approach for the optimisation of product specifications

In order to develop the profit-maximising, market share-maximising or cost-minimising bundle of product engineering specifications with proper performance levels, an optimisation model driven by operating data is proposed. The operating data are input as the sources to conduct the optimisation and a data-based customer satisfaction function can be formed. Then, a customer choice model developed from the customer satisfaction is constructed to estimate the customer choice probability. The expected market share (EMS) then can be derived from the choice probability. After all, a multi-objective model is constructed to maximise the EMS and minimise the total engineering cost. The candidate Pareto-optimal solutions can be obtained by solving the optimisation model. Then a membership function is defined to select the optimal solution from the Pareto-optimal solutions. A case study for optimising the smartphone’s specifications is conducted to demonstrate the effectiveness of the newly developed approach. Compared with the commonly used Conjoint Analysis (CA) method in determining the most desired levels for product specifications, the proposed data-driven method can avoid the situation where the user’s preferences are irrational, making the proposed method be more practical in measuring customer preferences than the utility-based model.     

Product design resources optimization using a non-linear fuzzy quality function deployment model

Quality Function Deployment (QFD) is a well-known customer-oriented methodology that is widely used to assist decision-making in product design and development in various types of production including highly customized One-of-a-Kind Production (OKP), batch production as well as continuous/ mass production. Determining how and to what extent (degree) certain characteristics/technical attributes (TA) of products are to be met with a view to gaining a higher level of overall customer satisfaction is a key to successful product design and development. Most of the existing approaches and models for QFD planning seldom consider the resource constraints in product design, nor do they normally take into account the impacts of the correlation among various TA. In other words, most of the existing QFD applications assume that the resources committed fully to attaining the design target for one TA have no impacts on those for other TA. Hence, the costs/resources required are usually worked out individually by linear formulation. In practice, design resource requirements should be expressed in fuzzy terms to accommodate the imprecision and uncertainties innate in the design process, such as ill-defined or incomplete understanding of the relationship between a given set of customer requirements (CR) and TA, the complexity of interdependence among TA, etc. A non-linear fuzzy model is proposed here to offer a more practical and effective means of incorporating the resource factors in QFD planning. The impacts of the correlation among TA are also considered. In the model, the resources for achieving the design target for a certain TA are expressed in a non-linear formulation of its relationship, correlation as well as interdependence with other customer requirements or TA. The concepts of the achieved attainments and planned attainments for TA, and the corresponding primary costs, planned costs and actual costs are introduced. Solutions to the non-linear fuzzy model can be obtained using a parametric optimization method or a hybrid genetic algorithm. A case study is also given to illustrate how the proposed fuzzy model and the optimization routine can be applied to help decision-makers in a company deploy their design resources towards gaining better overall customer satisfaction.     

QFD optimization using linear physical programming

A new approach to quality function deployment (QFD) optimization is presented. The approach uses the linear physical programming (LPP) technique to maximize overall customer satisfaction in product design. QFD is a customer-focused product design method which translates customer requirements into product engineering characteristics. Because market competition is multidimensional, companies must maximize overall customer satisfaction by optimizing the design of their products. At the same time, all constraints (e.g. product development time, development cost, manufacturing cost, human resource in design and production, etc.) must be taken into consideration. LPP avoids the need to specify an importance weight for each objective in advance. This is an effective way of obtaining optimal results. Following a brief introduction to LPP in QFD, the proposed approach is described. A numerical example is given to illustrate its application and a sensitivity analysis is carried out. Using LPP in QFD optimization provides a new direction for optimizing the product design process.     

Understanding big consumer opinion data for market-driven product design

Big consumer data provide new opportunities for business administrators to explore the value to fulfil customer requirements (CRs). Generally, they are presented as purchase records, online behaviour, etc. However, distinctive characteristics of big data, Volume, Variety, Velocity and Value or ‘4Vs’, lead to many conventional methods for customer understanding potentially fail to handle such data. A visible research gap with practical significance is to develop a framework to deal with big consumer data for CRs understanding. Accordingly, a research study is conducted to exploit the value of these data in the perspective of product designers. It starts with the identification of product features and sentiment polarities from big consumer opinion data. A Kalman filter method is then employed to forecast the trends of CRs and a Bayesian method is proposed to compare products. The objective is to help designers to understand the changes of CRs and their competitive advantages. Finally, using opinion data in Amazon.com, a case study is presented to illustrate how the proposed techniques are applied. This research is argued to incorporate an interdisciplinary collaboration between computer science and engineering design. It aims to facilitate designers by exploiting valuable information from big consumer data for market-driven product design.     

An ontology for relating affective needs to design parameters: development process and content

Product manufacturers are looking for ways to design and refine their customisation offerings, based on knowledge of affective needs. One approach, CATER, has at its heart an ontology, which supports the exchange of data between the relevant sub-systems in a semantically meaningful manner. This article presents the ontology, specifically emphasising the role of Citarasa, product breakdown and customer selection history. It also presents the ontology development process, including a validation exercise, and discussion of the implications for affective customisation that emerge from the ontology and the development process. This includes issues of how generic an affective ontology can be, the importance of iteration in the customer selection and the functional component of affective requirements.