Design Improvement for Complex Systems with Uncertainty

Yue Chen, Jian Shi and Xiao-Jian Yi
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Yue Chen: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Jian Shi: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100864, China
Xiao-Jian Yi: School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100811, China

Mathematics, 2021, vol. 9, issue 11, 1-20

Abstract: The uncertainty of the engineering system increases with its complexity, therefore, the tolerance to the uncertainty becomes important. Even under large variations of design parameters, the system performance should achieve the design goal in the design phase. Therefore, engineers are interested in how to turn a bad design into a good one with the least effort in the presence of uncertainty. To improve a bad design, we classify design parameters into key parameters and non-key parameters based on engineering knowledge, and then seek the maximum solution hyper-box which already includes non-key parameters of this bad design. The solution hyper-box on which all design points are good, that is, they achieve the design goal, provides target intervals for each parameter. The bad design can be turned into a good one by only moving its key parameters into their target intervals. In this paper, the PSO-Divide-Best method is proposed to seek the maximum solution hyper-box which is in compliance with the constraints. This proposed approach has a considerably high possibility to find the globally maximum solution hyper-box that satisfies the constraints and can be used in complex systems with black-box performance functions. Finally, case studies show that the proposed approach outperforms the EPCP and IA-CES methods in the literature.

Keywords: robustness; hyper-box; uncertainty; optimization; key parameters; constraints (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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