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Modeling and Solving Arc Routing Problems in Street Sweeping and Snow Plowing
(2012)
In arc routing problems, the goal is to determine an optimal path, or set of paths, that traverse a required subset of arcs on a graph with respect to a set of constraints and objective function. The Chinese Postman Problem ...
An Agent-Based Modeling Approach to Reducing Pathogenic Transmission in Medical Facilities and Community Populations
(2012)
The spread of infectious diseases is a significant and ongoing problem in human populations. In hospitals, the cost of patients acquiring infections causes many downstream effects, including longer lengths of stay for ...
STATISTICAL AND OPTIMAL LEARNING WITH APPLICATIONS IN BUSINESS ANALYTICS
(2015)
Statistical learning is widely used in business analytics to discover structure or exploit patterns from historical data, and build models that capture relationships between an outcome of interest and a set of variables. ...
Simulation Optimization: New Methods and An Application
(2014)
Simulation models are commonly used to provide analysis and prediction of the behavior of complex stochastic systems. Simulation optimization integrates optimization techniques into simulation analysis to capture response ...
Stochastic Simulation: New Stochastic Approximation Methods and Sensitivity Analyses
(2015)
In this dissertation, we propose two new types of stochastic approximation (SA) methods and study the sensitivity of SA and of a stochastic gradient method to various input parameters. First, we summarize the most common ...
A DECISION MODEL FOR STUDENT-ATHLETE ENTRY INTO THE NBA DRAFT
(2014)
We develop a Markov Decision Process model using the framework of an optimal stopping problem to describe whether or not a student-athlete should enter the National Basketball Association (NBA) draft early. Our model uses ...
Optimal Learning with Non-Gaussian Rewards
(2014)
In this disseration, the author studies sequential Bayesian learning problems modeled under non-Gaussian distributions. We focus on a class of problems called the multi-armed bandit problem, and studies its optimal learning ...
STOCHASTIC OPTIMIZATION: APPROXIMATE BAYESIAN INFERENCE AND COMPLETE EXPECTED IMPROVEMENT
(2018)
Stochastic optimization includes modeling, computing and decision making. In practice, due to the limitation of mathematical tools or real budget, many practical solution methods are designed using approximation techniques ...
PROBLEMS ORIGINATING FROM THE PLANNING OF AIR TRAFFIC MANAGEMENT INITIATIVES
(2018)
When weather affects the ability of an airport to accommodate flights, a ground delay program is used to control the rate at which flights arrive at the airport. This prevents excessive congestion at the airport. In this ...
HYBRID ROUTING MODELS UTILIZING TRUCKS OR SHIPS TO LAUNCH DRONES
(2018)
Technological advances for unmanned aerial vehicles, commonly referred to as drones, have opened the door to a number of new and interesting applications in areas including military, healthcare, communications, cinematography, ...