Quality function deployment (QFD) is becoming a widely used customer-driven approach and tool in product design. The inherent fuzziness in QFD modelling makes fuzzy regression more appealing than classical statistical tools. A new fuzzy regression-based mathematical programming approach for QFD product planning is presented. First, fuzzy regression theories with symmetric and nonsymmetric triangular fuzzy coefficients are discussed to identify the relational functions between engineering characteristics and customer requirements and among engineering characteristics. By embedding the relational functions obtained by fuzzy regression, a mathematical programming model is developed to determine targets of engineering characteristics, taking into consideration the fuzziness, financial factors and customer expectations among the competitors in product development process. The proposed modelling approach can help design team assess relational functions in QFD effectively and reconcile tradeoffs among the various degree of customer satisfaction and determine a set of the level of attainment of engineering characteristics for the new/improved product towards a higher customer expectation within design budget. The comparison results under symmetric and non-symmetric cases and the simulation analysis are made when the approach is applied to a quality improvement problem for an emulsification dynamite packing machine.

This paper reports on a collaboratively developed user interface. The user interface has been coded as a Microsoft Windows-based and user-friendly computer software system using the Quality Function Deployment (QFD) method and linear programming technique. By using the user interface, a product development team can rapidly specify a product to meet customer requirements and needs, and optimally allocate the team members'time to achieve the various technical features of the defined product so that maximum overall customer satisfaction is achieved. This paper uses an industrial case to illustrate the basic principles behind the user interface and the industrial implementation results are discussed.

Quality Function Deployment (QFD) is a well-known customer-oriented methodology that is widely used to assist decision-making in product design and development in various types of production including highly customized One-of-a-Kind Production (OKP), batch production as well as continuous/mass production. Determining how and to what extent (degree) certain characteristics/technical attributes (TA) of products are to be met with a view to gaining a higher level of overall customer satisfaction is a key to successful product design and development. Most of the existing approaches and models for QFD planning seldom consider the resource constraints in product design, nor do they normally take into account the impacts of the correlation among various TA. In other words, most of the existing QFD applications assume that the resources committed fully to attaining the design target for one TA have no impacts on those for other TA. Hence, the costs/resources required are usually worked out individually by linear formulation. In practice, design resource requirements should be expressed in fuzzy terms to accommodate the imprecision and uncertainties innate in the design process, such as ill-de

A tvpe of model of fuzzy quadratic programming problems(FQP)iS proposed. It describes the fuzzy objective and resource constraints with different types of membership functions according to different types of fuzzy objective and fuzzy resource constraints in actual production problems. This article develops an inexact approach to solve this tyDe of model of quadratic programming problems with fuzzy objective and resource constraints. Instead of finding an exact optimal solution, we use a Genetic Algorithm (GA) with mutation along the weighted gradient direction to find a family of solutions with acceptable membership degrees. Then by means of the human-computer interaction, the solutions preferred by the DM under different criteria can be achieved.