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In this study, a novel model called Function Deployment Model (FDM) for decision making in new product development is introduced. To demonstrate its usefulness pragmatically, the model is applied to the design problem of development of the micro/nano injection molding machine. Based on the Quality Function Deployment (QFD) technique, this model translates the customer needs to engineering characteristics. An Analytic Hierarchy Process (AHP) is used to prioritize customer requirements while a Linear Programing (LP) optimization method is used to determine the feasible solution of the design variables within limited resources. Also, a program with a tailor-made graphical user interface (GUI) using MATLAB library and Microsoft Component Object Model (COM) technology is implemented to guide the design engineers to employ the proposed methodology for decision making.

This paper introduces a set of mathematical formulae for calculating the eigenvalue differential sensitivities of the closed-loop state matrix with respect to the open-loop state matrix, input matrix and state feedback matrix. It provides a computational procedure for a robust pole assignment problem. The algorithm is based on a gradient flow minimization of a differentiate objective function which measures the sensitivity for all closed-loop poles. Two numerical examples are employed to illustrate the technique. Comparisons to other existing methods are made as well.

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The paper provides a computational procedure for a type ofrobust regional pole assignment problem. It allows closed-loop poles to be settled at certain perturbation insensitive locations within some prespecified regions in the complex plane. The novelty ofour approach lies in the versatility of the proposed algorithm which provides a rich set of constrained subregions applicable for the assignment of individual or subsets of closedloop poles. in contrast to other conventional regional pole assignment methods. The algorithm is based on a gradient flow formulation on a differentiable potentialfunction which provides II minimizing solution for the Frobenius condition number of the closedloop eigenvector matrix. A numerical example is employed to illustrate the technique.The improvement on the eigenstructure robustness is compared with different kinds of constrained region.