Browsing Industrial engineering by Subject "Industrial engineering"
Now showing items 1-20 of 54
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Approximate dynamic programming for weakly coupled Markov decision processes with perfect and imperfect information
A broad range of optimization problems in applications such as healthcare operations, revenue management, telecommunications, high-performance computing, logistics and transportation, business analytics, and defense, have ... -
Approximating Large-Scale Binary Integer Programs by Discrete Optimal Control
Optimal control theory has been introduced as a powerful tool for approximately solving binary integer programming problems. In previous studies, an approach using continuous optimal control theory was developed, where the ... -
Assessing Cognitive Workload of In-Vehicle Voice Control Systems
In-Vehicle Information Systems (IVIS) are becoming more accessible to drivers and contain more complex communication features. Voice control systems (VCS's) promise to be less distracting than visual-manual interfaces, but ... -
Complex Adaptive Food Supply Systems
(2014-02-24)The long-term sustainability of food supply chains (FSCs) is critical to human and environmental health. While the modern industrial FSC is capable of the production and global distribution of enormous quantities of food, ... -
Contemporaneous Health Monitoring and Biomarker Discovery by Integration of Patient Data and Disease Knowledge
Technological innovations have given rise to data-rich environments that support the use of heterogeneous sensor measurements to monitor complex healthcare systems. Despite these advancements, however, there remains little ... -
Convex and Dynamic Optimization with Learning for Adaptive Biologically Conformal Radiotherapy
The research objective of this dissertation is to apply convex and dynamic optimization methods to establish a rigorous mathematical framework called Adaptive Biologically Conformal Radiotherapy (ABCRT) for spatiotemporally ... -
Convex and Robust Optimization Methods for Modality Selection in External Beam Radiotherapy
The goal in external beam radiotherapy (EBRT) for cancer is to maximize damage to the tumor while limiting toxic effects of radiation dose on the organs-at-risk (OAR). EBRT can be delivered via different modalities such ... -
Data-Driven Polynomial Chaos Expansions for Uncertainty Quantification
Uncertainties exist in both physics-based and data-driven models of systems. Understanding how system inputs affect a system output's uncertainty is essential to improve system outputs such as quality and productivity. ... -
Depression Management Using Electronic Health Record: Individual Progression Prediction
Mitigating depression has become a national health priority and is the most common mental illness seen in primary care. Due to the complex dynamics of individual's depression trajectory, how to predict the progression of ... -
Developing Domain-specific Simulation Objects for Modeling Clinical Laboratory Operations
Clinical laboratories play a critical role in patient diagnosis, treatment planning and prevention of disease. The inherent complexity of clinical laboratories lies both in the volume and variety of specimen types, which ... -
Diagnostic Monitoring of High-dimensional Networked Systems (with Applications in Manufacturing and Healthcare System)
Rapid advances in sensor and information technology have resulted in both spatially and temporally data-rich environment, which creates a intensive need for us to develop novel statistical methods and computationally ... -
Discrete-event Simulation and Optimization to Improve the Performance of a Healthcare System
Healthcare systems have attracted the attention of management and analysis due to their high percentage of the gross domestic product (GDP) and increasing rate of growth of expenditures. Within the various types of healthcare ... -
Driver Behavioral Adaptation to In-Vehicle Technologies: Influence of Demands and Exposures
In-Vehicle Information Systems (IVIS) can assist drivers by increasing both safety and efficiency, but may also divert drivers' attention away from the road and cause distractions. The goal of this dissertation is to examine ... -
Dynamic Models under Uncertainty for Large-Scale Enterprise Systems
Many enterprise applications such as assortment planning, inventory control and supply chain management rely on forecasting, optimization and machine learning methodologies. While many methods have been developed for static ... -
Exact and Heuristic Approaches to Middle and Last Mile Logistics
Logistics is a well-studied field in operations research. Numerous authors have done extensive work in this area, especially in the domain of routing problems. However there are still aspects of routing to be better explored. ... -
Exploring the Impact of Virtual Medicine on the Patient-Centered Medical Home
The Patient-Centered Medical Home (PCMH) is a health care delivery model where a patient has an ongoing relationship with a personal physician who provides comprehensive and appropriate health care. Enhanced health care ... -
Feature Extraction Using Topological Data Analysis for Machine Learning and Network Science Applications
Many real-world data sets can be viewed as a noisy sampling of an unknown high-dimensional topological space. The emergence and development of topological data analysis (TDA) over the last fifteen years or so provides a ... -
Feedforward Control and Process Improvement for Some Time Series Disturbance Models
(2013-02-25)Process adjustment strategy is an important part of the process improvement methods, which is also called engineering process control (EPC), and it is often integrated with statistical process control (SPC) to improve the ... -
Improving Efficiency in Allocating Pediatric Ambulatory Care Clinics
Low utilized resources is a common problem in the health care sector. As health care costs and the need for more efficient operations increases, managers are looking for new methods to increase the utilization of their ... -
Information theoretic learning methods for Markov decision processes with parametric uncertainty
Markov decision processes (MDPs) model a class of stochastic sequential decision problems with applications in engineering, medicine, and business analytics. There is considerable interest in the literature in MDPs with ...