This thesis studies three approaches for solving linear programs with complementarity constraints (LPCC). The focus of Chapter 2 lies on difference-of-convex (DC) penalty formulations and the associated difference-of-convex algorithm (DCA) for computing stationary solutions of LPCCs. We concentrate on three such formulations and establish connections between their stationary solutions and...
One of the main drivers of complexity in a service system is the dependence between different random variables describing the system. For example, the queue lengths at different time points and the waiting times of different items (jobs, customers) in queue are strongly dependent. To reduce dependence-related complexities, it is...
Marathons are long distance running events with many participants, often organized in heavily populated cities. A key component in marathon planning operations is the design of the marathon course to be followed during the race. In addition to the technical requirements regarding length and incline change, marathon courses must satisfy...
This dissertation explores the relationship between how teams form and what they need to perform. It adopts the perspective that technology is fundamental to organizing in modern workplaces and examines how technology may both enhance and constrain teamwork. By adopting this perspective, two questions naturally follow. First, how do teams...
Unstructured data like text is plentiful and possibly contains valuable insights leading to a better decision-making process. Manually obtaining these insights can be costly and time-consuming. Text mining, also known as Text analytics, is developed to derive meaningful information from textual data. It is widely applied in various domains such...
The thesis contains all four chapters of my Ph.D. research on deep learning and text mining. The first chapter, "Temporal Topic Analysis with Endogenous and Exogenous Processes'', proposes a topic model which mines temporal economy-related documents with an exogenous economic indicator, and finds the relationship between document topics and the...
This work is a collection of articles featuring applications of operations research primarily on solid organ transplantation. At the time of writing, 111,434 Americans were waiting for a liver or kidney transplant. Only 26,901 transplants were performed last year – a consequence of the scarcity of organ donors and the...
Machine learning has been widely applied to solve intricate problems in finance. Yet in options theory, machine learning methods are less visited due to the structural complexity of the derivatives market. This dissertation focuses on using machine learning algorithms to obtain optimal decisions for three distinct option-related problems. In the...
Risk measurement involves estimating some functional of a loss distribution. This calls for nested simulation, in which risk factors are sampled at an outer level of simulation, while the inner level of simulation provides estimates of loss given each realization of the risk factors. Assessing the statistical uncertainty of estimates...
Stochastic programming models static or dynamic optimal decision making under uncertainty. In contrast to deterministic mathematical programming, stochastic programming generally uses expectation functionals in objective or constraints over known or partially known distributions of the problem data. Its features many decision variables under complicated constraints over discrete time periods. Its...
Funding risky Research and Development (R&D) and New Product Development (NPD) projects is crucial for the long term health of any large company. Such funding decisions are multiobjective and are undertaken with sparse data. Therefore prescriptive or algorithmic solutions are not adopted by practitioners. Descriptive solutions that provide managerial insights...
Operations Management (OM) is concerned with the processes involved in delivering goods and services to customers (Hopp and Spearman 2000, Shim and Siegel 1999). While recent surge in service and professional white collar work has greatly changed the arena of OM practice, OM research has not yet well address the...
This dissertation studies the management of operations that match surplus inventory of one party to meet the need of another. The first part is concerned with the efficient and robust design of transshipment networks in a commercial environment. The second part is concerned with a sequential resource allocation problem in...
Both individual and institutional investors face a number of constraints in their consumption and investment decisions. We look at well-motivated constraints on the consumption process as well as liquidity constraints and study their impact on optimal consumption and investment policies under a dynamic discrete time setting.
The most important managerial criteria in supply chains are how to manage product, information and cash flows, and how to maximize profits by either increasing the revenue or decreasing the costs. Although the maximum benefits can be achieved if everyone follows the central planner's suggestions; unfortunately, the individual maximum profits...
In this dissertation, we explore modeling and solution methods for intermodal drayage operations. This research is motivated by the need to provide operational choices in drayage operations to increase efficiency; however, as shown in our work, the introduction of this flexibility in modeling and solution methods is challenging.
Intermodal freight...
In this thesis we discuss the issue of solving stochastic optimization problems using sampling methods. Numerical results have shown that using variance reduction techniques from statistics can result in significant improvements over Monte Carlo sampling in terms of the number of samples needed for convergence of the optimal objective value...
Markov models are widely employed in cost-effectiveness analysis of healthcare interventions. Although such models are usually formulated at the individual level, it is also useful to examine outcomes at the population level. Analysts may wish to know the impact of a health intervention on a whole population instead of an...
Nelson and Staum derived ranking-and-selection procedures that employ control-variate (CV) estimators instead of sample means to obtain greater statistical efficiency. However, control-variate estimators require more computational effort than sample means, and effective controls must be identified. In this dissertation, we present a new CV screening procedure to avoid much of...
Cluster Analysis deals with classifying a sample of multivariate measurements into different categories. In this dissertation we study the effect of the correlation structure of the data on the performance of a clustering method. We begin with the analysis of two-component normal mixture models and then proceed to cluster analysis...
In this thesis, we study routing and resource allocation problems which have probabilistic objective functions. This class of problems has received limited attention in literature despite its promising applications. A probabilistic objective function is capable of incorporating business targets into the problem modeling and representing the risk attitude of a...
Portfolio optimization problems with transaction costs have been widely studied by both financial economists and financial engineers through various approaches. In this paper, we propose the following approach. In analogy to American option pricing, we study the problem through the Finite Element Method (FEM) combined with an optimization method: We...
The classic error bounds for quasi-Monte Carlo approximation follow the Koksma-Hlawka inequality based on the assumption that the integrand has finite variation. Unfortunately, not all functions have this property. In particular, integrands for common applications in finance, such as option pricing, do not typically have bounded variation. In contrast to...
This thesis concerns the development of robust algorithms for large-scale nonlinear programming. Despite recent advancements in high-performance computing power, classes of problems exist that continue to challenge the practical limits of contemporary optimization methods. The focus of this dissertation is the design and analysis of algorithms intended to achieve economy...
This dissertation examines the impact of product returns on effective supply chain management. Within this area of research, known as Closed-Loop Supply Chain Management, we consider both strategic and tactical level reverse logistics and inventory management problems from the perspective of a firm which must efficiently process returned items. More...
At the heart of nonlinear optimization methods lies the solution of linear systems of equations. As the size of the problem increases, it is imperative to use iterative methods, such as the conjugate gradient algorithm, to solve these linear systems. In the context of constrained optimization, it has proved to...
Flexibility can be created in manufacturing and service operations by using multipurpose production sources such as cross-trained labor and flexible machines/factories. We focus on control and design issues in systems with flexible resources. In Chapter 2, we consider optimal scheduling of a fully cross-trained server in a finite-population queueing system...
In financial risk management, coherent risk measures have been proposed as a way to avoid undesirable properties of measures such as value at risk that discourage diversification and do not account for the magnitude of the largest, and therefore most serious, losses. A coherent risk measure equals the maximum expected...
The goal of this thesis is to design practical algorithms for nonlinear optimization in the case when the objective function is stochastic or nonsmooth. The thesis is divided into three chapters. Chapter 1 describes an active-set method for the minimization of an objective function that is structurally nonsmooth, viz., it...
Ross (2015) proposed a recovery theorem which uses prices of contingent claims to recover market’s expectations about underlying asset returns. His work relies on two assumptions. He assumes all uncertainty of the economy follows a finite state irreducible Markov chain and that the pricing kernel is transition independent. We first...
Simulation analytics treats stochastic simulation as data analytics for systems that do not yet exist, and extends traditional performance estimation and system optimization to uncovering underlying patterns and the key drivers and dynamics of system behavior by retaining the sample paths generated throughout simulation runs. My dissertation addresses two research...
It is well documented that an individual’s ability to know who knows whom in their network has positive benefits in various facets of professional life. But people vary in their network acuity - that is, their ability to accurately assess who knows whom in their network. This poster seeks to...
Traditionally, simulation analysis has focused on designing a computationally efficient algorithm assuming a correct simulation model is given. As computation becomes cheaper, we are now able to perform more sophisticated simulation analyses involving extensive computation and consider all sources of errors in the simulation model and their effects to the...
As the title suggests, this dissertation is composed of three major topics. The first two are optimization problems focusing on developing effective solution methodologies, while for the last topic we present a large-scale information retrieval system in the domain of sports. With the algorithms and frameworks developed in this dissertation,...
We introduce and advocate a new paradigm in simulation experiment design and analysis, called ``green simulation,'' for the setting in which experiments are performed repeatedly with the same simulation model but different input parameters. In this dissertation three classes of green simulation estimators are proposed: the likelihood-ratio-based estimators, the metamodeling-based...