| Author: | Miao, Yunwen |
| Title: | Quality function deployment optimization from game-theoretic and fuzzy perspectives |
| Advisors: | Ji, Ping (ISE) |
| Degree: | Ph.D. |
| Year: | 2020 |
| Subject: | Quality function deployment Product management Customer relations Consumer satisfaction Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Industrial and Systems Engineering |
| Pages: | xv, 182 pages : color illustrations |
| Language: | English |
| Abstract: | In regard to the widely applied customer-centric product design nowadays, quality function deployment (QFD) can be viewed as one of the effective and efficient tools to interpret customer requirements (CRs) of a certain product to engineering characteristics (ECs) in the manufacturing aspect. Generally, the competitiveness and customer satisfaction of the product are expected to be enhanced after implementing the QFD optimization procedure. Based on the traditional QFD optimization framework, this research attempts to conduct product development from the perspectives of cooperative games and fuzzy uncertainty, respectively. For a certain manufacturing product, several CRs and ECs are selected in the QFD procedure for optimization. Commonly, the respective importance weights of CRs and ECs, and target values or target levels of ECs are significant research points, which aim at maximizing the overall customer satisfaction of the product under limited resources. Firstly, the angle of a two-stage cooperative game integrating a quantitative Kano's model in QFD is hardly considered in the previous literature. More specifically, Shapley value is utilized to obtain the CR relative importance weights, while Nash bargaining is applied to the objective function in a deterministic optimization model to attain target values of ECs. Secondly, as far as the QFD optimization under the fuzzy perspective is concerned, some novel derivations on calculations for expected values of different fuzzy events expressed by α-optimistic values of fuzzy variables are given. Therefore, the fuzzy importance of ECs can be measured, and the expected return of the fuzzy objective and several expected constraints of a fuzzy optimization model can be transformed into more simplified ones. On this basis, an improved hybrid intelligent algorithm (iHIA), which consists of a novel fuzzy simulation technique for the expected value of fuzzy events and a genetic algorithm, is proposed to solve the simplified model. Thirdly, in order to accomplish the novel fuzzy simulation procedure in the iHIA, a series of improved fuzzy simulation techniques are generated. At the beginning, a new operational law regarding membership functions of continuous and strictly monotone functions of regular fuzzy numbers or intervals is set forth. As a consequence, several novel fuzzy simulation techniques for the possibility and expected value of fuzzy events are successively raised as a theoretical basis. Another enhancement on the expected value simulation is based on the analytical expressions of α-optimistic values of fuzzy variables. On the whole, by applying the proposed QFD optimization methods from two perspectives, research outcomes of CRs and ECs provide useful guidelines, suggestions, and managerial implications for the decision-makers. The implementation of the methods to the case study of a notebook development in this thesis can also be extended to other manufacturing products. Meanwhile, the theoretical improvements on fuzzy simulation will also make contributions to the development of fuzzy theories. |
| Rights: | All rights reserved |
| Access: | open access |
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