Outline
- Abstract
- Introduction
- Proposed Methodology
- Numerical Example
- Discussion of Results
- Conclusions
- Notes
- References
رئوس مطالب
- چکیده
- کلیدواژه ها
- مقدمه
- بکارگیری تابع کیفیت
- فرآیند سلسله مراتبی تحلیلی مبهم
- برنامه ریزی فیزیکی خطی
- اعمال برنامه ریزی فیزیکی خطی با QFD
- برنامه ریزی خطی مبهم
- روش شناسی پیشنهای
- مثال عددی
- بحث نتایج
- نتیجه گیری ها
Abstract
Quality function deployment (QFD) is a customer-driven approach, widely used to develop or process new product to maximize customer satisfaction. Last researches used linear physical programming (LPP) procedure to optimize QFD; however, QFD issue involved uncertainties, or fuzziness, which requires taking them into account for more realistic study. In this paper, a set of fuzzy data is used to address linguistic values parameterized by triangular fuzzy numbers. Proposed integrated approach including analytic hierarchy process (AHP), QFD, and LPP to maximize overall customer satisfaction under uncertain conditions and apply them in the supplier development problem. The fuzzy AHP approach is adopted as a powerful method to obtain the relationship between the customer requirements and engineering characteristics (ECs) to construct house of quality in QFD method. LPP is used to obtain the optimal achievement level of the ECs and subsequently the customer satisfaction level under different degrees of uncertainty. The effectiveness of proposed method will be illustrated by an example.
Conclusions
In this paper, we proposed a simple and useful methodology by integrating AHP, QFD, and LPP for supplier development problems under uncertainty conditions. We used fuzzy AHP to determine the relationships between customer’s requirements and engineering characteristics for building the relation matrix in the QFD method. Then, applying LPP, we formulated the mathematical model to optimize QFD. Proposed methodology helps decision makers to deal with the vagueness and imprecise involved in the real problems. In addition, it helps them to maximize overall customer satisfaction in supplier development. In addition, the proposed methodology can be used in the product design, product development, process development, and other decision-making problems.
For the future work, we suggest to consider the correlation between engineering characteristics to increase the reliability of the obtained solutions or use the other type of fuzzy programming to obtain optimal achievement level of engineering characteristics and customer satisfaction level.