An approach of new product planning using quality function deployment and fuzzy linear programming model

Abstract

Quality function deployment (QFD) is a useful tool for maximising customer satisfaction in the new product planning (NPP) process. When applying QFD, recognising the importance levels of customer requirements (CRs) is a vital issue, since they have great influences on the values of decision variables, i.e. the fulfilment levels of design requirements (DRs), for NPP. Unlike existing researches, this study evaluates the importance scores of CRs from three different perspectives: the customer, competition and value perspectives. To cope with the vague and uncertain nature at the NPP stage, various fuzzy approaches are adopted for measuring the importance scores of CRs from different perspectives. The three measures are aggregated as the evaluated importance score of each CR. In addition, different to prior studies, this paper modifies Chen and Weng's model to develop a new normalised relationship between CRs and DRs to improve the existing models' drawbacks. A fuzzy linear programming model is established to determine the optimal fulfilment levels of DRs to maximally satisfy CRs. Based on an illustrative example, the proposed method achieves greater total customer satisfaction than previous methods. Furthermore, this paper also proposed a normalised relationship formulation with conventional way to reasonablely modify Wasserman's model.

A fuzzy- rough set approach to determine weights in maintenance quality function deployment

“…In such scenario, fuzzy numbers, instead of crisp data, can be used to represent the vague or imprecise data. Chen and Weng (2006), Ko and Chen (2014), Mechefske and Wang, (2003) and Verma et al (2007) proposed fuzzy models that consider the correlation between TRs. However, the fuzzy theory requires some additional information such as membership grade and predefined boundary intervals.…”

An Efficient Imperialist Competitive Algorithm for Solving the QFD Decision Problem

“…Firstly, in order to clarify those complex relationships, the problem is abstracted as a mathematical model. Then the model is calculated by an algorithm to offer a result for decision makers as a valuable reference [2][3][4][5][6][7][8][9][10][11][12][13].…”

Application of fuzzy linear programming for short-term planning and quality control in mine complexes

“…FLP has many applications in real-world problems, including production planning and scheduling, transportation, finance, engineering design, environmental management, and assignment (Rommelfanger, 1996;Sahinidis, 2004;Ko and Chen, 2014). This concept can be applied to optimize the mining schedule and production planning in open pit mines.…”

“…This concept can be applied to optimize the mining schedule and production planning in open pit mines. Consider the general form of LP models (Equation [1]) where the objective function's coefficient (c~), resources (b), and coefficients of the constraints (Ã) are all fuzzy numbers (Lie and Hwang, 1992;Wang, 1997;Sakawa, Yano, and Nishizaki, 2013;Luhandjula, 2014). These parameters could also be represented as triangular fuzzy numbers.…”

“…It provides a set of solutions to the decisionmaker, who can select his/her preferred solution. The optimum solution of the problem is a trade-off among the degree of memberships (Lie and Hwang 1992) [2]…”