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Articles 1 - 30 of 122
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Optimal Network Maintenance And Restoration: Applications And Algorithms, Nayan Chakrabarty
Optimal Network Maintenance And Restoration: Applications And Algorithms, Nayan Chakrabarty
Graduate Theses and Dissertations
In this dissertation, we consider three types of network optimization problems. In Chapter 1, we consider a network maintenance problem which focuses on time-based redeployment of multi-class nodes for reliable wireless sensor network coverage. Whereas previous research on time-based node redeployment assumes nodes are identical with respect to time to failure, we use multiple classes of sensor nodes to represent a scenario where nodes’ times to failure are dependent on positioning in the network. We propose a partial survival signature (PSS) approach for estimating area coverage reliability under a given time-based redeployment policy, where the PSS is estimated by Monte …
Survival Signature Estimation Using Optimization And Monte-Carlo Simulation For K ≥ 3 Classes Of Nodes On Two-Terminal Networks, Md Sazid Rahman
Survival Signature Estimation Using Optimization And Monte-Carlo Simulation For K ≥ 3 Classes Of Nodes On Two-Terminal Networks, Md Sazid Rahman
Graduate Theses and Dissertations
This research develops an efficient approach to estimating survival signatures for two-terminal networks with more than two classes of components. Recently, the survival signature has gained substantial attention in the literature on network reliability estimation due to its unique separability property, which enables passing the network topology information independent of the failure distribution of the components. Following recent results from the literature, estimating the two-terminal survival signature by Monte Carlo simulation entails solving a multi-objective maximum capacity path problem on a two-terminal network in each replication. We adapt a multi-objective Dijkstra’s algorithm from the literature to construct the set of …
Survival Signature Estimation For All-Terminal Networks By Solving The Multi-Objective Bottleneck Spanning Tree Problem, Dewan Maisha Zaman
Survival Signature Estimation For All-Terminal Networks By Solving The Multi-Objective Bottleneck Spanning Tree Problem, Dewan Maisha Zaman
Graduate Theses and Dissertations
This research examines the problem of estimating the survival signature of all-terminal networks using Monte Carlo (MC) simulation. Following a recent similar result for twoterminal networks, we show that the work required within each MC replication corresponds to solving a multi-objective bottleneck spanning tree (MOBST) problem. We implement the resulting MC procedure using a “Blocks” algorithm from the literature to solve the MOBST in each replication by identifying its minimal set of non-dominated points. We compare this implementation against intuitive benchmark procedures for completing the work within an MC replication. We conduct numerical experiments to assess the efficacy of multi-objective …
Interaction-Sensitive Tree-Based Statistical Models, Xiaotong Sun
Interaction-Sensitive Tree-Based Statistical Models, Xiaotong Sun
Graduate Theses and Dissertations
This dissertation introduces a tree-based framework to improve the interpretability and modeling of interaction effects among variables, essential in fields like biostatistics, healthcare, science and engineering. Traditional regression methods often fail to clearly capture complex interactions, while tree-based approaches, despite their interpretability, face performance limitations and overfitting concerns. Our proposed interaction-sensitive tree-based method, designed for seamless integration, combines various statistical techniques tailored to different data types, leveraging ensemble learning methods to enhance accuracy and mitigate overfitting. We present methods for regression, survival analysis, and classification, validated with case studies and benchmarked against traditional models using metrics like BIC and R-squared. …
Learning To Accelerate Globally Optimal Solutions: Applications In The Ac Optimal Power Flow Problem, Muhammet Fatih Cengil
Learning To Accelerate Globally Optimal Solutions: Applications In The Ac Optimal Power Flow Problem, Muhammet Fatih Cengil
Graduate Theses and Dissertations
The Alternating Current Optimal Power Flow (AC-OPF) problem is a fundamental optimization challenge critical to ensuring the economical and reliable operation of power grids. While fast heuristic methods provide upper-bound solutions, assessing their quality requires lower bounds obtained from relaxations of the AC-OPF problem. This dissertation focuses on finding globally optimal solutions to the AC-OPF problem by enhancing the effectiveness and efficiency of Quadratic Convex (QC) relaxations. Leveraging machine learning techniques, we aim to achieve tighter relaxations faster and improve computational performance, enabling practical scalability for real-time applications.
In Chapter 2, we propose a machine learning-based method to accelerate the …
Exploring Telehealth Utilization Through Data Analytics, Statistical Analyses, And Machine Learning Techniques, Aysenur Betul Cengil
Exploring Telehealth Utilization Through Data Analytics, Statistical Analyses, And Machine Learning Techniques, Aysenur Betul Cengil
Graduate Theses and Dissertations
This dissertation investigates the utilization of telehealth services, initially focusing on the Arkansas healthcare system and then extending the analysis nationwide. It aims to understand the factors influencing telehealth adoption and its impact on healthcare delivery. After examining telehealth utilization in Arkansas from 2018 to 2022, the research utilizes a comprehensive dataset from Epic Cosmos, which includes a wide range of patient and visit data from multiple healthcare facilities across the United States from 2018 to 2023. This timeframe allows for a detailed analysis of telehealth trends before, during, and after the COVID-19 pandemic. In Chapter 2, we analyze key …
Advancing Prediction And Decision Analytics Techniques To Improve Treatment Of Tuberculosis, Maryam Kheirandish Borujeni
Advancing Prediction And Decision Analytics Techniques To Improve Treatment Of Tuberculosis, Maryam Kheirandish Borujeni
Graduate Theses and Dissertations
Tuberculosis (TB) remains a global health challenge, significantly impacting morbidity and mortality rates worldwide. Despite advancements in diagnosis and treatment, TB continues to pose substantial challenges, particularly in low-resource settings. This dissertation aims to develop a robust treatment monitoring framework for TB patients to ensure personalized and effective treatment using demographic and clinical information. The current standard TB treatment framework, recommended by the World Health Organization (WHO), involves monitoring patients through laboratory tests such as smear and culture sputum tests at specific time points during treatment. These tests, however, are not fast and accurate enough to determine the severity of …
Feasibility Assessment And Container Traffic Forecasting Of Inland Waterway Container On Barge Transportation, Fan Bu
Graduate Theses and Dissertations
Container on Barge (COB) transportation is an intermodal freight transport mode that moves shipping containers via barges on navigable inland and intracoastal waterways. During the past twenty years, COB has been a growing mode of container shipping globally due to its low-cost, eco-friendly, and congestion-reducing characteristics. Europe and China are currently leading global COB transportation, and the United States (U.S.) may have the potential to achieve economic benefits through the implementation of COB within its intermodal transportation system. To explore this potential, this dissertation investigates the implementation feasibility of COB transportation within the U.S. intermodal freight transportation system. Three contributions …
Leveraging Machine Learning And Stochastic Programming To Address Vaccine Hesitancy In Public Health Resource Allocation, Hieu Trung Bui
Leveraging Machine Learning And Stochastic Programming To Address Vaccine Hesitancy In Public Health Resource Allocation, Hieu Trung Bui
Graduate Theses and Dissertations
Infectious disease outbreaks highlight the urgent need for effective strategies to distribute vaccines and allocate critical healthcare resources to contain the disease and reduce its negative impacts on the population. Managing these allocations is a significant challenge, especially in marginalized communities facing uncertainty in healthcare demand and logistical constraints. This dissertation addresses these challenges by investigating factors that influence dynamic changes in vaccine hesitancy (VH) and its implications for disease spread and healthcare resource demand. It develops optimization models for vaccine distribution and resource allocation under uncertainty, validated with data from the COVID-19 pandemic in the U.S. The first study …
Reliability Modeling And Improvement Of Critical Infrastructures: Theory, Simulation, And Computational Methods, José Carlos Hernández Azucena
Reliability Modeling And Improvement Of Critical Infrastructures: Theory, Simulation, And Computational Methods, José Carlos Hernández Azucena
Graduate Theses and Dissertations
This dissertation presents a framework for developing data-driven tools to model and improve the performance of Interconnected Critical Infrastructures (ICIs) in multiple contexts. The importance of ICIs for daily human activities and the large volumes of data in continuous generation in modern industries grant relevance to research efforts in this direction. Chapter 2 focuses on the impact of disruptions in Multimodal Transportation Networks, which I explored from an application perspective. The outlined research directions propose exploring the combination of simulation for decision-making with data-driven optimization paradigms to create tools that may provide stakeholders with optimal policies for a wide array …
Characterizing Logistics Operations Within A Federal Staging Area For Hurricane Response: A Qualitative Analysis Of Federal, State And Local Perspectives, Jannatul Shefa
Graduate Theses and Dissertations
A successful deployment of logistics operations following a disaster is a collective contribution of federal, state, and local entities to ascertain an efficient and effective response. This research analyzes data from interviews with disaster response logistics experts from these entities. The objective is to investigate the information sources and planning processes used in these organizations to plan vehicle routes for critical resource deliveries to impacted areas. Special attention is directed to the impacts of incomplete knowledge of infrastructure status, such as road disruptions due to debris or flooding. Supported by both qualitative and quantitative evidence, the study finds that incomplete …
Automation Of Life Cycle Assessment, Jacob Hickman
Automation Of Life Cycle Assessment, Jacob Hickman
Graduate Theses and Dissertations
An automation program, named Jacob LCA, was created to help perform life cycle assessment (LCA). The program uses a template file to perform controlled and consistently ordered actions with the LCA program, SimaPro, and effectively removes the need for manual user input. It can be set to run using data from one or more life cycle inventory (LCI) files, which allows for rapid iteration and testing of data. It also partially addresses some of the limitations of LCA by establishing a procedure through which temporal and spatial variations in data can be integrated into LCI files and then passed to …
Efficient Routing For Disaster Scenarios In Uncertain Networks: A Computational Study Of Adaptive Algorithms For The Stochastic Canadian Traveler Problem With Multiple Agents And Destinations, Neel Chanchad
Graduate Theses and Dissertations
The primary objective of this research is to develop adaptive online algorithms for solving the Canadian Traveler Problem (CTP), which is a well-studied problem in the literature that has important applications in disaster scenarios. To this end, we propose two novel approaches, namely Maximum Likely Node (MLN) and Maximum Likely Path (MLP), to address the single-agent single-destination variant of the CTP. Our computational experiments demonstrate that the MLN and MLP algorithms together achieve new best-known solutions for 10,715 instances. In the context of disaster scenarios, the CTP can be extended to the multiple-agent multiple-destination variant, which we refer to as …
Demand Prediction And Inventory Management Of Surgical Supplies, Rajon Paul Pantha
Demand Prediction And Inventory Management Of Surgical Supplies, Rajon Paul Pantha
Graduate Theses and Dissertations
Effective supply chain management is critical to operations in various industries, including healthcare. Demand prediction and inventory management are essential parts of healthcare supply chain management for ensuring optimal patient outcomes, controlling costs, and minimizing waste. The advances in data analytics and technology have enabled many sophisticated approaches to demand forecasting and inventory control. This study aims to leverage these advancements to accurately predict demand and manage the inventory of surgical supplies to reduce costs and provide better services to patients. In order to achieve this objective, a Long Short-Term Memory (LSTM) model is developed to predict the demand for …
Using Reinforcement Learning To Improve Network Reliability Through Optimal Resource Allocation, Henley Wells
Using Reinforcement Learning To Improve Network Reliability Through Optimal Resource Allocation, Henley Wells
Graduate Theses and Dissertations
Networks provide a variety of critical services to society (e.g. power grid, telecommunication, water, transportation) but are prone to disruption. With this motivation, we study a sequential decision problem in which an initial network is improved over time (e.g., by adding or increasing the reliability of edges) and rewards are gained over time as a function of the network’s all-terminal reliability. The actions during each time period are limited due to availability of resources such as time, money, or labor. To solve this problem, we utilized a Deep Reinforcement Learning (DRL) approach implemented within OpenAI-Gym using Stable Baselines. A Proximal …
A Multi-Criteria Ranking System For Prioritizing Maintenance Of Levee Systems In Arkansas, Nguyen Danh Phan
A Multi-Criteria Ranking System For Prioritizing Maintenance Of Levee Systems In Arkansas, Nguyen Danh Phan
Graduate Theses and Dissertations
There are 208,009 properties in Arkansas that have more than a 26% chance of being severely affected by flooding over the next 30 years, which represents 13% of all properties in the state. A levee system is designed to reduce the flooding risk for urban and rural communities; however, most of the state's levees have been significantly outdated or built with engineering standards less rigorous than current best practices. The Levee Safety Action Classification (LSAC), as recorded in the National Levee Database (NLD), communicates the risk associated with living behind a particular levee and assists local, state, and federal stakeholders …
Machine Learning For Early Detection Of Pediatric Sepsis, Glory Manson-Endeboh
Machine Learning For Early Detection Of Pediatric Sepsis, Glory Manson-Endeboh
Graduate Theses and Dissertations
Sepsis is a host response to infection in both adults and children. It contributes to approximately 1.7 million cases annually with nearly 270,000 annual deaths in the United States. In the United States, the financial burden of sepsis on survivors and their families including the hospitals is over $4.8 billion, at approximately $64,280 per hospitalization. The first goal of this thesis research is to develop efficient machine learning models to predict pediatric sepsis accurately for inpatients. The second objective is to develop machine learning methods to determine how early sepsis can be detected to mitigate mortality. We examine data collected …
Advancing Statistical Learning And Decision Modeling Using Irregularly-Sampled Multivariate Data For Managing Respiratory Diseases, Maryam Alimohammadi
Advancing Statistical Learning And Decision Modeling Using Irregularly-Sampled Multivariate Data For Managing Respiratory Diseases, Maryam Alimohammadi
Graduate Theses and Dissertations
Complex healthcare systems require efficient and effective data-driven decision making in various aspects. As patient data becomes more available, advanced statistical learning and machine learning techniques are applied to improve data-driven decision making. However, patient health data, including clinical trial data, medical records, and electronic health records, are associated with several challenges. Patient health data includes medical information of a patient that may includedemographics, information relating to their health or illness, medications and treatments, etc. They are a combination of static and time series variables, with many censoring and missingness in the data, and are irregularly sampled in most cases. …
Scheduling, Complexity, And Solution Methods For Space Robot On-Orbit Servicing, Susan E. Sorenson
Scheduling, Complexity, And Solution Methods For Space Robot On-Orbit Servicing, Susan E. Sorenson
Graduate Theses and Dissertations
This research proposes problems, models, and solutions for the scheduling of space robot on-orbit servicing. We present the Multi-Orbit Routing and Scheduling of Refuellable On-Orbit Servicing Space Robots problem which considers on-orbit servicing across multiple orbits with moving tasks and moving refuelling depots. We formulate a mixed integer linear program model to optimize the routing and scheduling of robot servicers to accomplish on-orbit servicing tasks. We develop and demonstrate flexible algorithms for the creation of the model parameters and associated data sets. Our first algorithm creates the network arcs using orbital mechanics. We have also created a novel way to …
Ensemble Tree-Based Machine Learning For Imaging Data, Reza Iranzad
Ensemble Tree-Based Machine Learning For Imaging Data, Reza Iranzad
Graduate Theses and Dissertations
In particular medical imaging data, such as positron emission tomography (PET), computed tomography (CT), and fluorescence intravital microscopy (IVM), have become prevalent for use in a wide variety of applications, from diagnostic purposes, tracking diseases' progress, and monitoring the effectiveness of treatments to decision-making processes. The detailed information generated by medical imaging has enabled physicians to provide more comprehensive care. Although numerous machine learning algorithms, especially those used for imaging data, have been developed, dealing with unique structures in imaging data remained a big challenge. In this dissertation, we are proposing novel statistical tree-based methods with more efficient and more …
Deep Learning Applications In Industrial And Systems Engineering, Winthrop Harvey
Deep Learning Applications In Industrial And Systems Engineering, Winthrop Harvey
Graduate Theses and Dissertations
Deep learning - the use of large neural networks to perform machine learning - has transformed the world. As the capabilities of deep models continue to grow, deep learning is becoming an increasingly valuable and practical tool for industrial engineering. With its wide applicability, deep learning can be turned to many industrial engineering tasks, including optimization, heuristic search, and functional approximation. In this dissertation, the major concepts and paradigms of deep learning are reviewed, and three industrial engineering projects applying these methods are described. The first applies a deep convolutional network to the task of absolute aerial geolocalization - the …
Modeling The Impact And Accelerating The Process Of Transitioning To A Sustainable Healthy Diet Through Decision Support Systems, Prince Agyemang
Modeling The Impact And Accelerating The Process Of Transitioning To A Sustainable Healthy Diet Through Decision Support Systems, Prince Agyemang
Graduate Theses and Dissertations
Food production and consumption are essential in human existence, yet they are implicated in the high occurrences of preventable chronic diseases and environmental degradation. Although healthy food may not necessarily be sustainable and vice versa, there is an opportunity to make our food both healthy and sustainable. Attempts have been made to conceptualize how sustainable healthy food may be produced and consumed; however, available data suggest a rise in the prevalence of health-related and negative environmental consequences of our food supply. Thus, the transition from conceptual frameworks to implementing these concepts has not always been effective. This paper explores the …
The Impact Of Reliability In Conceptual Design - An Integrated Trade-Off Analysis, Tevari James Barker
The Impact Of Reliability In Conceptual Design - An Integrated Trade-Off Analysis, Tevari James Barker
Graduate Theses and Dissertations
Research presented in this paper focuses on developing models to estimate the systemreliability of Unmanned Ground Vehicles using knowledge and data from similar systems. Traditional reliability approaches often require detailed knowledge of a system and are used in later design stages as well as development, operational test and evaluation, and operations. The critical role of reliability and its impact on acquisition program performance, cost, and schedule motivate the need for improved system reliability models in the early design stages. Reliability is often a stand-alone requirement and not fully included in performance and life cycle cost models. This research seeks to …
Predicting The Likelihood And Scale Of Wildfires In California Using Meteorological And Vegetation Data, Matthew Walters
Predicting The Likelihood And Scale Of Wildfires In California Using Meteorological And Vegetation Data, Matthew Walters
Graduate Theses and Dissertations
Wildfires have devastating ecological, environmental, economical, and public health impacts through the deterioration of water and air quality, CO2 emissions, property damage, and lung illnesses. The early detection and prevention of wildfires allow for the minimization of these risks. The use of Artificial Intelligence (AI) in wildfire detection and prediction has been highly researched as a tool to assist firefighters in stopping wildfires in its early stages. The three common wildfire prediction categories include image and video detection, behavior prediction, and susceptibility prediction. Data such as climate, weather, vegetation, satellite images, and historical wildfire data is most commonly used. Many …
Supervised Representation Learning For Improving Prediction Performance In Medical Decision Support Applications, Phawis Thammasorn
Supervised Representation Learning For Improving Prediction Performance In Medical Decision Support Applications, Phawis Thammasorn
Graduate Theses and Dissertations
Machine learning approaches for prediction play an integral role in modern-day decision supports system. An integral part of the process is extracting interest variables or features to describe the input data. Then, the variables are utilized for training machine-learning algorithms to map from the variables to the target output. After the training, the model is validated with either validation or testing data before making predictions with a new dataset. Despite the straightforward workflow, the process relies heavily on good feature representation of data. Engineering suitable representation eases the subsequent actions and copes with many practical issues that potentially prevent the …
Statistical Modeling, Learning And Computing For Stochastic Dynamics Of Complex Systems, Mohammadmahdi Hajiha
Statistical Modeling, Learning And Computing For Stochastic Dynamics Of Complex Systems, Mohammadmahdi Hajiha
Graduate Theses and Dissertations
With the recent advances in sensor technology, it is much easier to collect and store streams of system operational and environmental (SOE) data. These data can be used as input to model the underlying behavior of complex engineered systems and phenomenons if appropriate algorithms with well-defined assumptions are developed. This dissertation is comprised of the research work to show the applicability of SOE data when fed into proposed tailored algorithms. The first purposes of these algorithms are to estimate and analyze the reliability of a system as elaborated in Chapter 2. This chapter provides the derivation of closed-form expressions that …
Deployment Policies To Reliably Maintain And Maximize Expected Coverage In A Wireless Sensor Network, Nicholas T. Boardman
Deployment Policies To Reliably Maintain And Maximize Expected Coverage In A Wireless Sensor Network, Nicholas T. Boardman
Graduate Theses and Dissertations
The long-term operation of a wireless sensor network (WSN) requires the deployment of new sensors over time to restore any loss in network coverage and communication ability resulting from sensor failures. Over the course of several deployment actions it is important to consider the cost of maintaining the WSN in addition to any desired performance measures such as coverage, connectivity, or reliability. The resulting problem formulation is approached first through a time-based deployment model in which the network is restored to a fixed size at periodic time intervals. The network destruction spectrum (D-spectrum) has been introduced to estimate reliability and …
Optimization Of Vaccine Supply Chains In Low- And Middle-Income Countries Utilizing Drones, Maximilian Kolter
Optimization Of Vaccine Supply Chains In Low- And Middle-Income Countries Utilizing Drones, Maximilian Kolter
Graduate Theses and Dissertations
Despite tremendous efforts from governments and humanitarian organizations, millions of children in low- and low-middle-income countries (LICs and LMICs) are still excluded from the benefits of immunization. The vaccine distribution in LICs and LMICs is challenging for several reasons, such as limited cold chain capacities, vaccine wastage, uncertain demand, and lack of access to immunization services. A promising avenue to address these issues is the utilization of drones for vaccine delivery. Drones can fly at high speed on direct paths and could enable on-demand deliveries to mitigate limited storage capacities. Further, their independence of road networks could allow them reaching …
Scheduling Allocation And Inventory Replenishment Problems Under Uncertainty: Applications In Managing Electric Vehicle And Drone Battery Swap Stations, Amin Asadi
Graduate Theses and Dissertations
In this dissertation, motivated by electric vehicle (EV) and drone application growth, we propose novel optimization problems and solution techniques for managing the operations at EV and drone battery swap stations. In Chapter 2, we introduce a novel class of stochastic scheduling allocation and inventory replenishment problems (SAIRP), which determines the recharging, discharging, and replacement decisions at a swap station over time to maximize the expected total profit. We use Markov Decision Process (MDP) to model SAIRPs facing uncertain demands, varying costs, and battery degradation. Considering battery degradation is crucial as it relaxes the assumption that charging/discharging batteries do not …
Resilience-Driven Post-Disruption Restoration Of Interdependent Critical Infrastructure Systems Under Uncertainty: Modeling, Risk-Averse Optimization, And Solution Approaches, Basem A. Alkhaleel
Resilience-Driven Post-Disruption Restoration Of Interdependent Critical Infrastructure Systems Under Uncertainty: Modeling, Risk-Averse Optimization, And Solution Approaches, Basem A. Alkhaleel
Graduate Theses and Dissertations
Critical infrastructure networks (CINs) are the backbone of modern societies, which depend on their continuous and proper functioning. Such infrastructure networks are subjected to different types of inevitable disruptive events which could affect their performance unpredictably and have direct socioeconomic consequences. Therefore, planning for disruptions to CINs has recently shifted from emphasizing pre-disruption phases of prevention and protection to post-disruption studies investigating the ability of critical infrastructures (CIs) to withstand disruptions and recover timely from them. However, post-disruption restoration planning often faces uncertainties associated with the required repair tasks and the accessibility of the underlying transportation network. Such challenges are …