Browsing by Author "Shu-Cherng Fang, Chair"

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Optimal Component Layout for Minimal Vulnerability of Weapon Systems
(2021-10-28) Baik, Seungwon; Shu-Cherng Fang, Chair; Osman Ozaltin, Member; Michael Kay, Member; Min Liu, Graduate School Representative; Yahya Fathi, Minor
Simulation Optimization Using Soft Computing
(2001-01-25) Medaglia, Andres L.; Shu-Cherng Fang, Chair; Henry L. W. Nuttle, Co-Chair; Stephen D. Roberts, Advisor; James R. Wilson, Advisor
To date, most of the research in simulation optimization has been focused on single response optimization on the continuous space of input parameters. However, the optimization of more complex systems does not fit this framework. Decision makers often face the problem of optimizing multiple performance measures of systems with both continuous and discrete input parameters. Previously acquired knowledge of the system by experts is seldomincorporated into the simulation optimization engine. Furthermore, when the goals of the system design are stated in natural language or vague terms, current techniques are unable to deal with this situation. For these reasons, we define and study the fuzzy single response simulation optimization (FSO) and fuzzy multiple response simulation optimization (FMSO) problems. The primary objective of this research is to develop an efficient and robust method for simulation optimization of complex systems with multiple vague goals. This method uses a fuzzy controller to incorporate existing knowledge to generate high quality approximate Pareto optimal solutions in a minimum number of simulation runs. For comparison purposes, we also propose an evolutionary method for solving the FMSO problem. Extensive computational experiments on the design of a flow line manufacturing system (in terms of tandem queues with blocking) have been conducted. Both methods are able to generate high quality solutions in terms of Zitzlerand Thiele's 'dominated space' metric. Both methods are also able to generate an even sample of the Pareto front. However, the fuzzy controlled method is more efficient, requiring fewer simulation runs than the evolutionary method to achieve the same solution quality. To accommodate the complexity of natural language, this research also provides a new Bezier curve-based mechanism to elicit knowledge and express complex vague concepts. To date, this is perhaps the most flexible and efficient mechanism for both automatic and interactive generation of membership functions for convex fuzzy sets.
Stochastic Global Optimization Techniques
(2002-04-04) Birbil, Sevket Ilker; Shu-Cherng Fang, Chair; Henry L. W. Nuttle, Member; Yahya Fathi, Member; Elmor L. Peterson, Member; Xiuli Chao, Member
In this research, a novel population-based global optimization method has been studied. The method is called Electromagnetism-like Mechanism or in short EM. The proposed method mimicks the behavior of electrically charged particles. In other words, a set of points is sampled from the feasible region and these points imitate the role of the charged particles in basic electromagnetism. The underlying idea of the method is directing sample points toward local optimizers, which point out attractive regions of the feasible space.The proposed method has been applied to different test problems from the literature. Moreover, the viability of the method has been tested by comparing its results with other reported results from the literature. Without using the higher order information, EM has converged rapidly (in terms of the number of function evaluations) to the global optimum and produced highly efficient results for problems of varying degree of difficulty.After a systematic study of the underlying stochastic process, the proof of convergence to the global optimum has been given for the proposed method. The thrust of the proof has been to show that in the limit, at least one of the points in the population moves to the neighborhood of the global optimum with probability one.The structure of the proposed method is very flexible permitting the easy development of variations. Capitalizing on this, several variants of the proposed method has been developed and compared with the other methods from the literature. These variants of EM have been able to provide accurate answers to selected problems and in many cases have been able to outperform other well-known methods.
Strategic Vertical Integration and Decentralization.
(2019-03-21) An, Qi; Shu-Cherng Fang, Chair; Yunan Liu, Member; Russell King, Member; Thom Hodgson, Member; Jayashankar Swaminathan, External; Negash Medhin, Graduate School Representative
Sub-one lp Quasi-norm Minimization and Applications.
(2019-10-02) Jiang, Shan; Shu-Cherng Fang, Chair; Min Liu, Graduate School Representative; Yunan Liu, Minor; James Wilson, Member; Osman Ozaltin, Member