A hierarchical approach to robust design optimization considering the needs of robust performance from multiple disciplines is proposed in this paper. The approach extends the framework of Collaborative Optimization to perform robust design with multiple objectives from different disciplines under uncertainty. The proposed approach is demonstrated by examples and compared with the all-in-one ap...

1. Abstract In practical engineering applications, there exist two different types of uncertainties: aleatory and epistemic uncertainties. Aleatory uncertainty is classified as objective and irreducible uncertainty with sufficient information on input uncertainty data, whereas epistemic uncertainty is a subjective and reducible uncertainty that stems from lack of knowledge on input uncertainty ...

A probabilistic approach which exploits the domain and distribution of the uncertain model parameters has been developed for the design of robust input shapers. A Polynomial Chaos based expansion approach is used to approximate uncertain system states and cost functions in terms of finite-dimensional series expansion in the stochastic space. Residual energy of the system is used as cost functio...

Robust optimization is an important technique to immunize optimization problems against data uncertainty. In the case of a linear program and an ellipsoidal uncertainty set, the robust counterpart turns into a second-order cone program. In this work, we investigate the efficiency of linearizing the second-order cone constraints of the latter. This is done using the optimal linear outer-approxim...

Background and Objectives: A sudden jump in blood demand during natural disasters may have strong negative impact on the performance of blood supply chain. Appropriate response to emergency situations requires predictive approach to determining the optimal allocation of blood supply chain resources for various disaster scenarios. The present study, thus, presents an optimization model aimed at ...

Wireless sensor network a powerful network contains many wireless sensors with limited power resource, data processing, and transmission abilities. Wireless sensor capabilities including computational capacity, radio power, and memory capabilities are much limited. Moreover, to design a hierarchy topology, in addition to energy optimization, find an optimum clusters number and best location of ...

Performance aspects such as travel time, punctuality, and robustness are conflicting goals of utmost importance for railway transports. To successfully plan traffic, it is therefore important to strike a balance between planned times expected delays. In operations research, lot attention has been given construct models methods generate robust timetables—that is, timetables with the potential wi...

This letter presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of Neural Contraction Metric (NCM). The NCM uses long short-term memory recurrent neural network global approximation an optimal contraction metric, existence which is necessary sufficient condition exponential stability systems. optimality stems from fact that metrics sampled ...