Title of Dissertation: ROBUST OPTIMIZATION AND SENSITIVITY ANALYSIS WITH MULTI-OBJECTIVE GENETIC ALGORITHMS: SINGLEAND MULTIDISCIPLINARY APPLICATIONS Mian Li, Doctor of Philosophy, 2007 Dissertation directed by: Shapour Azarm, Professor Department of Mechanical Engineering Uncertainty is inevitable in engineering design optimization and can significantly degrade the performance of an optimized ...

The coupling of finite element simulations to mathematical optimization techniques has contributed significantly to product improvements and cost reductions in the metal forming industries. The next challenge is to bridge the gap between deterministic optimization techniques and the industrial need for robustness. This paper introduces a generally applicable strategy for modeling and efficientl...

Structural design and optimization in engineering address increasingly non-standard optimization problems (NSP). These problems are characterized by complex topology conditions of the optimization space (w.r.t. nonlinearity, multimodality, discontinuity etc.). By that, NSP can only be solved by means of computer simulations. Hereby, the corresponding numerical approaches applied often tend to b...

efficient and on-time maintenance plays a crucial role inreducing cost and increasing the market share of an industrial unit. preventivemaintenance is a broad term that encompasses a set of activitiesaimed at improving the overall reliability and availability of a systembefore machinery breakdown. the previous studies have addressed thescheduling of preventive maintenance. these studies have co...

A dynamic robust optimization model is established based on the objective of profit maximization to deal with the uncertainty of the problem through formulating a robust linear programming model. An equivalent robust optimization model using duality theory and auxiliary problem principle is obtained which allows the solutions to be derived more efficiently. Following the numerical simulation an...

  An optimal desirability function method is proposed to optimize multiple responses in multiple production scenarios, simultaneously. In dynamic environments, changes in production requirements in each condition create different production scenarios. Therefore, in multiple production scenarios like producing in several production lines with different technologies in a factory, various fitted r...

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 ...