Robust Optimization in Discrete Design Space for Constrained Problems

Abstract

Robust design in discrete design space is defined as a discrete design that is insensitive to external uncertainties or variations. The application of robust discrete design is not prevalent yet due to high computational cost. A relatively simple method is proposed to select discrete and robust optimum. At first, the discrete design is achieved as the postprocess of conventional optimization. An orthogonal array is established around a conventional optimum, and the characteristic functions are evaluated. The characteristic function is defined by considering the robustness of the objective and constraints. The parameter design of the Taguchi method is introduced to obtain the robust solution in discrete space. The present method has insensitive performance to variations of the design variables within the selected discrete values enhancing the feasibility of constraints. To enhance feasibility, ranking the estimators of the characteristic function is developed. Several structural problems are solved to show the usefulness of the present method.