Systematical decision-making approach for quality function deployment based on uncertain linguistic term sets

A systematical decision-making approach is constructed for quality function deployment (QFD) in uncertain linguistic situations. The mathematical expression and operation of linguistic terms play important roles in the proposed approach in terms of customer requirements (CRs) and design requirements (DRs) in QFD. First, hesitant fuzzy linguistic term sets are designed to conveniently express uncertain linguistic terms and compute with words after the data derived from customers are pretreated and integrated in the decision-making process. Second, the tolerance deviation is defined to restrict innovatively the deviation range of fuzzy linguistic terms in the assessment stage of relative importance for CRs. Third, information entropy is originally designed to determine the final importance of DRs. Moreover, an empirical study on the research project called vortex recoil hydraulic retarder is conducted to demonstrate the performance of the systematical decision-making approach. The proposed approach can be applied to a wide variety of new product development problems in uncertainty settings.     

An order allocation model in multi-period logistics service supply chain based on cumulative prospect theory and capacity matching constraint

By considering a two-echelon supply chain that consists of a logistics service integrator (LSI) and several functional logistics service provider (FLSP), this study builds a comprehensive subjective utility (CSU) function of FLSP in multi-period based on cumulative prospect theory and establishes an order allocation model in multi-period logistics service supply chain. This model considers three goals: to minimise the cost of LSI, to maximise the satisfaction of the FLSP and to maximise the capacity matching degree among different logistics capacities. This study uses an improved technique for order performance by similarity to ideal solution to obtain the optimal solution. A numerical example is given by software MATLAB 7.8. The effects of the behavioural parameters of FLSPs on order allocation are also discussed. When the LSI allocates orders, the FLSP, who has larger ratio of loss to gain range by the sensitivity decreasing rate, should be selected to obtain a large value of CSU. No obvious rule is found for the influence of the loss aversion coefficient of FLSPs on the CSU of FLSPs in a multi-period.     

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.     

Technical attribute prioritisation in QFD based on cloud model and grey relational analysis

Promptly development of new products can be achieved through quality function deployment (QFD) process, which is critical to companies’ survival. Since the multi-criteria decision-making problem involved in QFD, a novel method integrating cloud model and grey relational analysis is put forward in this paper. Taking into account the subjectivity and ambiguity in linguistic evaluations, some scholars utilise fuzzy theory, rough theory, interval-valued fuzzy-rough sets and MCDM methods to improve traditional QFD. However, much priori information requirements, inability to handle subjectivity and randomness, and lack of mechanism to overcome small sample size problem are some inevitable drawbacks in these methods. To solve these deficiencies, a hybrid methodology is proposed in this paper, integrating the fortes of cloud model in processing ambiguity and randomness, and the merits of grey relational analysis in overcoming small sample size error as well as revealing the inner correlations. The comparative analysis of different approaches as well as the sensitivity analysis of criteria weights is implemented to prove the stability of the novel method. The results obtained in this paper shows that the proposed method can be a practical tool for improving the efficiency and accuracy of traditional QFD in reality management.     

Consensus building in linguistic failure mode and effect analysis: A perspective based on prospect theory

As one of many scientific and efficient risk assessment approaches, failure mode and effect analysis (FMEA) has been widely applied across various fields. There are two core issues in the FMEA approach: identifying the latent failure modes of the systems, products, processes and services and the risk assessment and the prioritization of those failure modes. Then, corrective measures must be taken in a timely and accessible manner to prevent the occurrence of failure modes with higher risk levels. In practice, several FMEA members from different fields are usually involved in the FMEA implementation process; the risk assessment information given by them may vary greatly. Therefore, it is necessary to integrate a consensus-building process into FMEA. Meanwhile, the psychological behaviours of FMEA members have had a great impact on the final prioritization of failure modes. Prospect theory is an effective approach for describing individual psychological behaviours. Therefore, this paper presents a novel linguistic FMEA approach to address the consensus issue from the perspective of prospect theory. In the proposed linguistic FMEA approach, a consensus measurement approach based on prospect theory is constructed. Then, a novel feedback adjustment mechanism is designed in which FMEA members can adjust not only their assessment information but also their reference points to achieve an acceptable consensus degree. Eventually, a practical application is used to show the validity and applicability of the proposed linguistic FMEA approach.     

基于QFD和AHP法的适老浴室柜设计研究

目的 设计出符合老年人实际使用需求的浴室柜产品,更好地帮助他们完成日常起居的生活。方法 以质量功能展开(QFD)理论和层次分析法(AHP)作为理论支撑,构建适老浴室柜质量屋(HOQ)设计理论模型,并以此为依据对适老浴室柜进行设计实践。首先通过实地访谈和观察法等方式,获取老年人的行为状况和需求信息,利用KJ法对需求进行聚类和精炼,得到用户需求层次化结构;然后运用AHP法计算出各层需求的权重值;最后利用HOQ理论将用户各项需求转化为适老浴室柜设计特征。结果 依据设计特征得到一款最为符合老年人需求的适老浴室柜设计方案。结论 该方法能够挖掘出用户最为迫切的需求,使设计出的产品更为科学与合理,更好地帮助老年人应对衰老所带来的困扰,同时对同类产品的设计研究也具有重要的参考意义。     

Robot selection using an integrated approach based on quality function deployment and fuzzy regression

The decision-makers have been experiencing difficulties in determining the most suitable robot alternative due to the increase in number of robots and the diversity in their application areas. A robust decision framework for robot selection should consider multiple and conflicting criteria and the dependencies among them. This paper introduces a decision model for robot selection based on quality function deployment (QFD) and fuzzy linear regression. The proposed approach benefits from the fact that QFD focuses on delivering value by taking into account the customer requirements and then by deploying this information throughout the development process, and applies this perspective to robot selection. Fuzzy linear regression is considered as an alternative decision aid for robot selection problems where imprecise relationships among system parameters exist. An example robot selection problem is presented to illustrate the proposed decision-making approach.     

An integrated module partition approach for complex products and systems based on weighted complex networks

The modularisation strategy can dramatically shorten the lead time and reduce the cost of complex products and systems (CoPS) development. Module partition is the basis of the modularisation. Because of the high technical content and multi-dimensional complexity of CoPS, the existing module partition approach cannot identify the module of CoPS effectively and efficiently. The complex network is introduced to visually represent the CoPS as their topological structures are very similar. The weights of the edges in the weighted complex network (WCN) are brought to illustrate the correlation strengths among components. An integrated approach is proposed in this article by modelling the CoPS module partition problem as the community detection problem of WCN. Firstly, the CoPS component-WCN is constructed and transformed to a multi-graph. Secondly, the modified GN algorithm is adopted to establish the community detection, namely module partition of CoPS. Finally, a module partition case example of a large tonnage crawler crane expounds that the proposed approach is effective and systematic to solve the CoPS module partition problem.