A novel fuzzy linguistic model for prioritising engineering design requirements in quality function deployment under uncertainties

Quality function deployment (QFD) is a planning and problem-solving tool gaining wide acceptance for translating customer requirements (CRs) into the design requirements (DRs) of a product. Deriving the priority order of DRs from input variables is a crucial step in applying QFD. Due to the inherent vagueness or impreciseness in QFD, the use of fuzzy linguistic variables for prioritising DRs has become more and more important in QFD applications. Existing approaches make use of the associated fuzzy membership functions of linguistic labels based on the fuzzy extension principle. However, an inherent limitation of such fuzzy linguistic approaches is the information loss caused by approximation processes, which eventually implies a lack of precision in the final results. This paper proposes an alternative approach to prioritising engineering DRs in QFD based on the order-based semantics of linguistic information and fuzzy preference relations of linguistic profiles, under random interpretations of customers, design team and CRs. Ultimately, this approach enhances the fuzzy-computation-based models proposed in the previous studies by overcoming the mentioned limitations. A case study taken from the literature is used to illuminate the proposed technique and to compare with the previous techniques based on fuzzy computation.     

A fuzzy QFD program modelling approach using the method of imprecision

The use of quality function deployment (QFD) to aid decision making in product planning has gained extensive international attention, but current QFD approaches are unable to cope with complex product planning (CPP) characterized by involving multiple engineering characteristics (ECs) associated with significant uncertainty. To tackle this difficulty, in this paper, fuzzy set theory is embedded into a QFD framework and a novel fuzzy QFD program modelling approach to CPP is proposed to optimize the values of ECs by taking the design uncertainty and financial considerations into account. In the proposed methodology, fuzzy set theory is used to account for design uncertainty, and the method of imprecision (MoI) is employed to perform multiple-attribute synthesis to generate a family of synthesis strategies by varying the value of s, which indicates the different compensation levels among ECs. The proposed methodology will allow QFD practitioners to control the distribution of their development budget by presetting the value of s to determine the compensation levels among ECs. An illustrative example of the quality improvement of the design of a motor car is provided to demonstrate the application and performance of the modelling approach.     

Hesitant fuzzy integrated MCDM approach for quality function deployment: a case study in electric vehicle

Quality function deployment (QFD) is a product planning management instrument which has been used in a broad range of industries. However, the traditional QFD method has been criticised much for its deficiencies in acquiring experts’ opinions, weighting customer requirements (CRs) and ranking engineering characteristics (ECs). To overcome the limitations, an integrated analytical model is presented in this study for obtaining the importance ratings of ECs in QFD by integrating decision-making trial and evaluation laboratory (DEMATEL) technique and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method under hesitant fuzzy environment. In particular, the hesitant fuzzy DEMATEL is used to analyse the interrelationships among CRs and determine their weights, and the hesitant fuzzy VIKOR is utilised to prioritise ECs. Finally, the feasibility and practicality of the proposed method are verified by an example regarding the product development of electric vehicle.     

Quantification for the importance degree of engineering characteristics with a multi-level hierarchical structure in QFD

Quantification for the importance degree of engineering characteristics (ECs) is an essential problem in quality function deployment. In real-world scenario, it is sometimes difficult to directly evaluate the correlation degree between ECs and customer requirements (CRs) as ECs are too abstract. Thus, the target ECs have to be further decomposed into several more detailed basic ECs and organised by a multi-level hierarchical structure. The paper investigates the quantification problem for the importance degree of such target ECs and tackles two critical issues. The first issue is how to deal with the uncertainties including fuzziness and incompleteness involved during the evaluation process. A fuzzy evidential reasoning algorithm-based approach is proposed to tackle this issue and derive the correlation degree between each of the basic ECs and the whole CRs. The second issue is how to deal with the interactions among the basic ECs decomposed from the same target EC during the aggregation process. A λ-fuzzy measure and fuzzy discrete Choquet integral-based approach is proposed to tackle this issue and aggregate these basic ECs. Final importance degree of the target ECs can then be obtained. At the end of this paper, a case study is presented to verify the feasibility and effectiveness of the method we propose.     

A fuzzy expected value-based goal programing model for product planning using quality function deployment

Product planning is one of four important processes in new product development using quality function deployment (QFD), which is a widely used customer-driven approach. In this article, a hierarchical framework for product planning using QFD is developed. To tackle the fuzziness in functional relationships between customer requirements and engineering characteristics (ECs) in product planning, the least squares method is incorporated into fuzzy regression to investigate those functional relationships, by which a more central tendency can be obtained. Furthermore, a fuzzy expected value-based goal programing model is proposed to specify target values of ECs. Different from some fuzzy product planning approaches for QFD, the proposed programing model has unambiguous interpretations. An illustrated example of a quality improvement problem of emulsification dynamite-packing machine design is given to demonstrate the application and performance of the proposed approach.     

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.     

An evaluation framework for product planning using FANP,QFD and multi-choice goal programming

Technological innovation and satisfaction of customer needs are the keys to survival and success for firms, especially in global competitive high-tech industries. Since new products are usually a source of new sales and profits, the success of new product development (NPD) is essential to maintain a competitive edge and to make a decent profit in a longer term. Therefore, how to develop products that deliver the quality and functionality customers demand while generating the desired profits becomes an important task for the manufacturers. In this paper, a framework with two phases is constructed for facilitating the selection of engineering characteristics (ECs) for product design. In the first phase, quality function deployment (QFD) is incorporated with the supermatrix approach of analytic network process (ANP) and the fuzzy set theory to calculate the priorities of ECs with the consideration of the interrelationship among factors and the impreciseness and vagueness in human judgments and information. In the second phase, multi-choice goal programming model is constructed by considering the outcome from the first phase and other additional goals, such as NPD cost and manufacturability, in the attempt to select the most suitable ECs. A case study of the product design process of backlight unit (BLU) in thin film transistor liquid crystal display (TFT-LCD) industry in Taiwan is carried out to verify the practicality of the proposed framework.     

Handling uncertainties in modelling manufacturing processes with hybrid swarm intelligence

Seldom has research regarding manufacturing process modelling considered the two common types of uncertainties which are caused by randomness as in material properties and by fuzziness as in the inexact knowledge in manufacturing processes. Accuracies of process models can be downgraded if these uncertainties are ignored in the development of process models. In this paper, a hybrid swarm intelligence algorithm for developing process models which intends to achieve significant accuracies for manufacturing process modelling by addressing these two uncertainties is proposed. The hybrid swarm intelligence algorithm first applies the mechanism of particle swarm optimisation to generate structures of process models in polynomial forms, and then it applies the mechanism of fuzzy least square regression algorithm to determine fuzzy coefficients on polynomials so as to address the two uncertainties, fuzziness and randomness. Apart from addressing the two uncertainties, the common feature in manufacturing processes, nonlinearities between process parameters, which are not inevitable in manufacturing processes, can also be addressed. The effectiveness of the hybrid swarm algorithm is demonstrated by modelling of the solder paste dispensing process.     

含模糊-区间变量的结构非概率可靠性优化设计

工程结构中存在着诸多不确定性因素,可靠性方法是处理不确定因素的有效途径之一。针对大量工程结构中存在混合不确定信息的问题,提出了一种适用于工程的混合不确定性可靠度计算方法。将模糊变量转化为区间变量,基于体积比的区间可靠性模型,建立了含模糊-区间混合变量的结构非概率可靠性模型。由于该可靠性模型意义明确,对模糊信息的处理也比较合理,可作为混合可靠性计算方法的一种补充。应用此可靠性模型对某飞机机翼结构进行了优化设计,实例计算说明了该文的方法是有效和可行的。     

Global sensitivity analysis for fuzzy inputs based on the decomposition of fuzzy output entropy

To analyse the component of fuzzy output entropy, a decomposition method of fuzzy output entropy is first presented. After the decomposition of fuzzy output entropy, the total fuzzy output entropy can be expressed as the sum of the component fuzzy entropy contributed by fuzzy inputs. Based on the decomposition of fuzzy output entropy, a new global sensitivity analysis model is established for measuring the effects of uncertainties of fuzzy inputs on the output. The global sensitivity analysis model can not only tell the importance of fuzzy inputs but also simultaneously reflect the structural composition of the response function to a certain degree. Several examples illustrate the validity of the proposed global sensitivity analysis, which is a significant reference in engineering design and optimization of structural systems.     

基于控制图的模糊资料判别研究

与一般处理如合格或不合格等布尔型数据的质量控制图不同,针对不适合采用布尔逻辑的产品质量数据,应用模糊逻辑构建质量控制图,以加权平均法来解模糊化,权重采用模糊中位数作为不同语言类别的表示值.通过实例分析,这种方法可以比较满意地进行工序控制工作.     

建筑结构基于模糊自适应调节趋近律的滑模控制研究

抖振问题是阻碍滑模控制应用的主要障碍之一.基于模糊控制的优点,采用一种模糊自适应调节趋近律的滑模控制方法对地震作用下建筑结构的振动控制问题进行了研究.给出了确定切换面的方法,推导了控制律的表达式.另外,考虑到实际中在大型的建筑结构中安装过多的传感器来进行主动控制研究是不切合实际的,还研究了有限状态输出反馈下该方法的适用性.以一个多层剪切型建筑结构模型为例来验证所提出控制方法的有效性,算例分析结果表明,所提出的控制方法能够有效地减小结构的地震峰值响应,同时达到了削弱控制系统抖振的目的.     

基于灰关联和模糊规划的产品设计改进方法

考虑质量屋模型中顾客需求与产品工程特性之间相关关系具有“少数据”、“贫信息”特点,将灰关联分析引入到质量屋模型中求解产品工程特性重要度。为了在产品设计过程中考虑产品开发成本、技术实现困难、产品市场竞争需求以及实际情况中产品开发成本约束存在的模糊性对产品最优设计的影响,构建一种模糊混合整数规划模型,实现产品工程特性最优设计水平。通过一个智能手机产品改进的案列来验证所构建模型的有效性和实用性。     

Constructing a new fuzzy classifier based on hierarchical fuzzy entropy

Abstract

In an earlier work, Lee et al. (Lee et al., 2001) presented a simple and fast fuzzy classifier that employed fuzzy entropy to evaluate pattern distribution information in a pattern space. In this paper, we extend his work to propose a new fuzzy classifier based on hierarchical fuzzy entropy (FC‐HFE). We retained the main parts of the original structure and modified some methods (e.g., methods for deciding the number of intervals in each dimension and for assigning class labels). In addition, the hierarchical fuzzy entropy is proposed for partitioning the decision region. The proposed FC‐HFE improves classification accuracy and overcomes some of the drawbacks in the Lee et al method (Lee et al., 2001). The simulation results show that the classification rate of the proposed FC‐HFE is better than earlier methods.     

A Hybrid GDM-SERVQUAL-QFD Approach for Service Quality Assessment in Hospitals

Quality is one of the most critical factors in health systems. To improve service quality in hospitals, we propose a hybrid approach incorporating Group decision-making (GDM), Service Quality measurement (SERVQUAL), and Quality function deployment (QFD). QFD is used to translate patient needs into quality characteristics (derived from SERVQUAL), while taking into account interrelationships between design requirements and quality characteristics. One of the most important steps in the QFD process is to prioritize quality characteristics from the patients’ viewpoint, which is inherently affected by two types of uncertainties: human mental perception and patient heterogeneity. In this research, a novel two-stage GDM approach is employed to cope with these uncertainties in an integrated manner. Furthermore, the proposed methodology is applied in a public hospital to effectively enhance quality characteristics, which will be considered in the QFD. Accordingly, the proposed methodology provides a framework for engineering professionals and managers in hospitals to assess and manage quality characteristics.     

A Fuzzy Quality Function Deployment (FQFD) model for deriving optimum targets

Quality Function Deployment (QFD) is a powerful tool that translates the Voice of the Customer (VoC) into the Engineering Characteristics (ECs), which are those that can be modified in order to meet the desires of the customer. A main objective of QFD is the determination of target values of ECs; however, the conventional QFD aims only empirically at finding these targets, which makes it difficult for the ECs to be optimum. This paper proposes a novel method for determining optimum targets in QFD. Fuzzy numbers are used to represent the imprecise nature of the judgements, and to define more appropriately the relationships between ECs and Customer Attributes (CAs). Constraints such as cost, technical difficulty and market position are considered. An example of a car door is presented to show the application of the method.     

基于直觉模糊张量的决策方法及其应用

对具有高维数据特征的直觉模糊决策问题进行了研究,定义了直觉模糊张量的一般形式和运算法则;建立了基于直觉模糊张量的广义直觉模糊加权平均算子,探索了广义直觉模糊加权平均算子的基本性质;证明了广义直觉模糊加权平均算子是直觉模糊加权平均算子的一般形式。提出了基于广义直觉模糊加权平均算子的决策方法,通过算例验证了该方法能够有效解决具有高维数据特征的直觉模糊决策问题。     

A novel intelligent fuzzy estimator for uniform load determination on a beam with varying boundary conditions

A novel intelligent fuzzy input estimation method that estimates the uniform input load on a beam structural system is presented in this article. The uniform input load acting on a beam structural system is estimated from the measured dynamic responses using the inverse method. The algorithm includes the fuzzy Kalman filter and the fuzzy-weighted recursive least square method. This study presents an efficient estimator accelerated and weighted using fuzzy-accelerating and -weighting factors proposed based on the fuzzy logic inference system. The capability of the proposed algorithm is demonstrated through several beam structural system examples with different types of boundary conditions. The simulation results are compared by alternating between the constant, adaptive, and fuzzy weighting factors. The results demonstrate that the presented method applied to beams with various structural system boundary conditions is successful.     

Development of Run-To-Run (R2R) controller for the multiple-input multiple-output (MIMO) system using fuzzy control theories

Run-to-Run (R2R) control has been extensively applied in semiconductor manufacturing. In particular, del Castillo, E. and Rajagopal, R., A multivariate double EWMA process adjustment scheme for drifting processes. IIE Trans., 2002, 34, 1055–1068, investigated double multivariate exponentially weighted moving average (MEWMA) controller for the multiple-input multiple-output (MIMO) system in an attempt to adjust and maintain the linear dynamic process outputs on target. Multivariate fuzzy control, inherently different from conventional MEWMA-based control, is another promising alternative that consists of fuzzy logic and set concept. Providing the fuzzy control can structure an appropriate membership function for the R2R MIMO system, thus it can be shown a practically useful control tool in comparison to MEWMA control. In this paper, fuzzy logic is utilized to design the multivariate fuzzy controller for the type of R2R applications based primarily on the min-max-gravity method advocated by Gupta, M.M., Kiszka, J.B. and Trojan, G.M., Multivariable structure of fuzzy control systems. IEEE Trans. Sys., Man Cybern., 1986, 16, 638–656. Under a variety of disturbance models, the proposed multivariate fuzzy controller can produce quite competitive control performance when compared to MEWMA control.