Quality Function Deployment (QFD) is a wellknown customer-oriented methodology, which is widely used to assist decision making in product design and development in various types of production. etermining how and to what extent certain characteristics or technical attributes (TAs) of products are to be met, with a view to gaining a higher level of overall customer satisfaction, is a key success factor in product design and development. An operational QFD planning problem with resource allocation is considered in this paper. The aim is to plan the attainment of TAs by allocating resources among the TAs with a view to achieving maximized overall customer satisfaction. Taking into account the technical and resource constraints, and the impact of the correlation among TAs, the operational QFD planning with resource allocation is formulated as a linear program and solved by a heuristics-combined Simplex Method. An overall procedure is presented to help a design team to implement this QFD design planning with resource allocation in practice. This model can bridge the gap and conflicts

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.

The current models and methods for PLP are usually restricted on some special types and usually the same type of possibilistic distribution. This paper focuses on linear programming problems with general possibilistic resources (GRPLP) and linear programming problems with general possibilistic objective coe cients (GOPLP). By introducing some new concepts of the largest most possible point, the smallest most possible point, the most optimistic point and the most possible decision, a new approach for formulating possibilistic constraints through general possibilistic resources and a satisfactory solution method will be discussed in this paper. A most possible decision method for GRPLP and a dual approach for GOPLP will be proposed for solving complex industrial decision problems.

ln this paper, a nonsymmetric model for a type of fuzzy nonlinear programming problems with penalty coefficients (FNLP-PC) is proposed. Based on a fuzzy optimal solution set and an optimal decision et, a satisfying solution method and a crisp optimal solution method with a GA for FNLP-PC is developed. Finally, the analysis of simulation results of an actual production problem example are also given.

As an extension of the hybridGenetic Algorithm-HGA proposedby Tang et al. (Comput. Math. Appl. 36 (1998) 11), this paper focuses on the critical techniques in the application of the GA to nonlinear programming (NLP) problems with equality andinequality constraints. Taking into account the equality constraints and embedding the information of infeasible points/chromosomes into the evaluation function, an extended fuzzy-basedmethod ology and three new evaluation functions are proposed to formulate and evaluate the infeasible chromosomes. The extended version of concepts of dominated semi-feasible direction (DSFD), feasibility degree (FD) of semi-feasible direction, feasibility degree (FD) of infeasible points &belonging to' feasible domain are introduced. Combining the new evaluation functions and weighted gradient direction search into the Genetic Algorithm, an extended hybrid Genetic Algorithm (EHGA) is developed to solve nonlinear programming (NLP) problems with equality andinequality constraints. Simulation shows that this new algorithm is effcient.