Constrained optimization problems are prevalent in all areas of science and engineering, and many well-known numerical methods have been developed to solve these problems. Yet, when there exists random quantities in the model under consideration, most of the deterministic methods are no longer effective. In this dissertation, we address parameter...
Optimization via simulation (OvS) is the practice of minimizing or maximizing the expected value of the output of a stochastic simulation model with respect to controllable decision variables. Stochastic simulation is a standard tool within operations research and is often required to model complex systems subject to uncertainty where it...
In machine learning, classification that assigns a label to a sample is a fundamental problem and serves a building block for various applications of artificial intelligence such as speech recognition, sentimental analysis, and image recognition. During the last years, deep learning rejuvenates artificial intelligence; in particular, it leads to tremendous...
In this dissertation, we study models and methods to address uncertainties that can vary in optimization problems. Robust optimization is a popular approach for optimization under uncertainty, especially if limited information is available about the distribution of the uncertainty. It models the uncertainty through sets and finds a robust optimal...
The goal of this thesis is to design practical algorithms for nonlinear optimization in the case where the objective function is deterministic or stochastic. Problems of this nature arise in many applications including machine learning and image processing. The thesis is divided into four main chapters. Chapters \ref{chap:Inexact}, \ref{chap:Adasample} and...
This dissertation develops a new framework and algorithms for statistical process control of stochastic textured surface data that have no distinct features other than stochastic characteristics that vary randomly (e.g., image data of textiles or material microstructures and surface metrology data of metal parts). All methods are general and nonparametric...
Cancer radiation therapy relies on optimized treatment plans whose quality assessment is judged by dosimetric planning aims. It is computationally prohibitive to incorporate the planning aims into the optimization models. Therefore, there exists a disconnect between the two steps of (1) optimizing a plan and (2) evaluating the optimized plan,...
The vast majority of interactions between customers and service providers are experiences that extend over time. Service systems that deliver excellent customer experience achieve greater customer satisfaction and therefore customer loyalty, and eventually raise revenue. The temporal aspects of service delivery have not yet been analyzed as carefully as its...
Computer simulation experiments are commonly used as an inexpensive alternative to real-world experiments to form a metamodel that approximates the input-output relationship of the real-world experiment. The metamodel can be useful for decision making and making predictions for inputs that have not been evaluated yet since it can be evaluated...