Recently, machine learning and deep learning, which have made many theoretical and empir- ical breakthroughs and is widely applied in various fields, attract a great number of researchers and practitioners. They have become one of the most popular research directions and plays a sig- nificant role in many fields, such...
Existing nonlinear optimization methods have proven reliable over the past few decades for a wide range of applications but have critically relied on accurate function and gradient evaluations. Modern nonlinear optimization problems arising from machine learning and scientific computing applications are increasingly complex and large scale, which make accurate evaluations...
In this dissertation we consider how simple operational levers affect a firm's revenue and consumer surplus. In particular, we focus on information disclosure as an useful control for omnichannel services.In the first chapter we consider a revenue-maximizing service firm that caters to price and delay-sensitive customers. The firm offers a...
This dissertation focuses on applications of statistical methods in nancial markets and isdivided into three parts. The rst part proposes an accurate variable-order cumulant approximation
method for Black-style shadow interest rate models respecting the zero lower
bound and estimates the interest rates models on historical bond yield data using the...
This dissertation is motivated by the decision process of the supply chain team of a major furniture retailer that delivers its products at the customer's home. In retail supply chains for companies offering home delivery services, demand surges are observed at the store level, which translate to a high volume...
The overall theme in this thesis is about eliciting and leveraging information to improve algorithms in Operations Management, with an emphasis on tractable and integrated approach of estimation and optimization. The main tool that I will be using is Robust Optimization, and the applications in my work vary from online...
This dissertation investigates a number of models and algorithms in the context of optimization under uncertainty. The focus is on risk-averse optimization, where risk aversion is modeled via stochastic dominance, utility theory, and distributionally robust optimization. We first investigate an investment model with fixed contribution rates, optimize asset-allocation decisions in...
We consider the statistical study of partially observed queueing systems arising in application areas such as hospital networks, data centers and cloud computing systems. Since these services operate under strict performance requirements, a statistical understanding of their performance is of great practical interest. A key challenge in these settings is...
This dissertation contains three essays that study the operational challenges and business innovations in service industries. We investigate how service providers could use wisely designed business models and service systems to induce preferable customer behaviors, and therefore manage demand and optimize revenue. Using a stylized model, we study how customers...
Multistage optimization is a prominent modeling tool to solve a broad range of dynamic decision-making problems in the presence of uncertainty. However, computing optimal policies is intractable, since they are obtained by considering all possible realizations of uncertainties and subsequent future decisions over time. To overcome these challenges, we present...