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...
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...
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...
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...
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...
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...
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...
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...
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...
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...