Operations Research, Systems Engineering and Industrial Engineering Commons^™

Assessing Readiness For Implementation Of Prognostics And Health Management In Small And Medium Enterprises, Sara C. Fuller

Theses and Dissertations

Prognostics and Health Management (PHM) refers to using robust sensing, monitoring, and control to detect, assess, and track system health degradation and failure modes, allowing for enhanced management and operational decisions. The need for PHM within a manufacturing facility has increased due to a variety of reasons, such as the increasing complexity of manufacturing equipment.

A lack of readiness for digital implementations is linked to failure. The literature highlights certain barriers and enablers that can signal whether a technology implementation will be successful, such as management and maintenance employees’ desire to change the existing process, an understanding and willingness to …

Bayesian Network Development And Validation For Siting Selection, Abdullah Hassan Battawi

Theses and Dissertations

In this study, increasing electricity demand requires considerable attention to increasing the diversity of power generation. Alternative energy can produce heating and power systems and thermal storage. Our objective and every organization’s objectives are to minimize its energy consumption cost under electricity demand uncertainty. In rural areas, heat and power availability and stability are also crucial. Combined heat and power have proven their effectiveness as a subsequent to Electricity. This paper identified four criteria and eleven sub-criteria to determine the most appropriate structure location for combined heat and power in the rural community. The Bayesian Network technology has been applied …

Bayesian Network Development For Depots Location Selection With Biomass Supply System Excellence, Alaa Ashraf Abulhamail

Theses and Dissertations

The renewable energy of the wood pellet market has taken great attention over the last few periods. However, the returns from the pellet business depend largely on how well the quality of biomass. The objective is to economically harvest pellets matching pellet standards set forward by the U.S. markets. The single-mindedness of this study is to develop a Bayesian network model to ensure a high-quality flow through the supply chain of the pallet industry in the top ten counties in Mississippi state. Multiple critical decisions (harvesting, storage, transportation, and quality control) of a biomass-to-pellet supply system could potentially affect the …

Developing Novel Optimization And Machine Learning Frameworks To Improve And Assess The Safety Of Workplaces, Amin Aghalari

Theses and Dissertations

This study proposes several decision-making tools utilizing optimization and machine learning frameworks to assess and improve the safety of the workplaces. The first chapter of this study presents a novel mathematical model to optimally locate a set of detectors to minimize the expected number of casualties in a given threat area. The problem is formulated as a nonlinear binary integer programming model and then solved as a linearized branch-and-bound algorithm. Several sensitivity analyses illustrate the model's robustness and draw key managerial insights. One of the prevailing threats in the last decades, Active Shooting (AS) violence, poses a serious threat to …

Optimization Of Drive Time And Competitiveness In Sports League Design, Zhuo Chen

Operations Research and Engineering Management Theses and Dissertations

Club sports, also known as recreational team sports, are prevalent in the metropolitan areas of United States nowadays. However, there is a key concern for organizers, which is how to reduce the time that players spend driving to and from matches while keeping league divisions competitive. We adopt a three-step approach to solve this problem. Initially, we analyze the drive time data between clubs’ locations to determine the geographic regions for the league. And then, clubs are assigned to divisions based on their rankings within in the league as well as their home facilities’ geographic regions. Finally, divisions are further …

Manufacturing & Service Systems, Robert S. Keyser

KSU Distinguished Course Repository

An analysis of decision making in the current production environment and the tools and optimization models needed for finding solutions to problems relating to production planning and scheduling, inventory, and warehouse design.

Lasso Based State Transition Modeling With Interactions In Adaptive Interdisciplinary Pain Management, Amith Viswanatha

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

The Eugene McDermott Center for Pain Management at the University of Texas Southwestern Medical Center has an interdisciplinary pain management program for chronic pain. This program treats patients with a holistic view of reducing chronic pain and improving their physical, mental, and social well-being through treatment interventions. The development of an adaptive treatment decision tool is main goal of the research project. This program is modeled as a two-stage adaptive treatment decision problem, with state transition models representing the transition of patient state, treatment, and outcome variables from stage 1 to stage 2. Interactions between the patient state and treatments …

A Hybrid Systems Model For Emergency Department Boarding Management, Eniola Oluwasola Suley

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

The purpose of this research is to examine methods for minimizing the influence of boarding on emergency department (ED) crowding outcomes. To accomplish this purpose, this research uses a hybrid systems model framework by combining agent-based simulation, predictive and optimization models to improve ED outcomes such as length-of-stay and left-without-being-seen rates. For the research, different types of simulation models were examined (discrete event and agent-based/discrete event combination) to identify the most parsimonious for studying ED boarding. Predictive models using simulation output were developed to understand the factors that influence future boarding levels as well as generate predictions. Research has previously …

Efficient Approaches To Steady State Detection In Multivariate Systems, Honglun Xu

Open Access Theses & Dissertations

Steady state detection is critically important in many engineering fields such as fault detection and diagnosis, process monitoring and control. However, most of the existing methods are designed for univariate signals. In this dissertation, we proposed an efficient online steady state detection method for multivariate systems through a sequential Bayesian partitioning approach. The signal is modeled by a Bayesian piecewise constant mean and covariance model, and a recursive updating method is developed to calculate the posterior distributions analytically. The duration of the current segment is utilized to test the steady state. Insightful guidance is provided for hyperparameter selection. The effectiveness …

Development Of Flood Prediction Models Using Machine Learning Techniques, Bhanu Kanwar

Doctoral Dissertations

"Flooding and flash flooding events damage infrastructure elements and pose a significant threat to the safety of the people residing in susceptible regions. There are some methods that government authorities rely on to assist in predicting these events in advance to provide warning, but such methodologies have not kept pace with modern machine learning. To leverage these algorithms, new models must be developed to efficiently capture the relationships among the variables that influence these events in a given region. These models can be used by emergency management personnel to develop more robust flood management plans for susceptible areas. The research …

Evaluating Barriers To And Impacts Of Rural Broadband Access, Javier Valentín-Sívico

Doctoral Dissertations

"The lack of adequate broadband infrastructure persists in many rural communities. Beyond funding, additional barriers persist, such as digital literacy and community-level self-efficacy. As a result, the first contribution articulates barriers at the organizational level. This work proposes a framework based on the Theory of Planned Behavior to highlight stakeholder dynamics that have constrained Regional Planning Commissions from advancing broadband infrastructure in rural areas. One approach to address these barriers is to provide stakeholders with analytical tools to evaluate the benefits and costs of various broadband options for their community since there is not a one-size-fits-all solution. To this end, …

Design And Operations Of A New Facility In A Next Generation Logistic System Based On Horizontal Collaboration, Dilhani Marasinghe

All Dissertations

The growing amount of freight combined with systemic inefficiencies are stressing current logistic systems. Including horizontal collaboration in the future could provide significant help but this strategy will require several new types of facilities. The most flexible type of horizontal collaboration, however, required freight routing decisions to be made dynamically, in real time, and based on last minute information. This research explores one new facility type operating in this environment that handles a high throughput of pallets or pallet-like containers. A key design feature is how much storage these facilities should have to better coordinate outbound loads. The approach taken …

Generating Sex Trafficking Networks From Text Documents, Maria Diaz

All Theses

Qualitative coding is a long and strenuous process that requires a well-skilled investigator. Natural language processing techniques have made leaps and bounds as far as usability and application domain, although it does not work for every task. In this work, we have created a natural language processing framework to help qualitative coders automatically obtain the nodes and node arcs from federal case files, dockets, and indictments within a sex trafficking network. The produced nodes and arcs allows us to perform network modeling by providing us with the information needed to create network structures that can then be used for interdiction …

Smart Manufacturing—Theories, Methods, And Applications, Zhuming Bi, Lida Xu, Puren Ouyang

Information Technology & Decision Sciences Faculty Publications

(First paragraph) Smart manufacturing (SM) distinguishes itself from other system paradigms by introducing ‘smartness’ as a measure to a manufacturing system; however, researchers in different domains have different expectations of system smartness from their own perspectives. In this Special Issue (SI), SM refers to a system paradigm where digital technologies are deployed to enhance system smartness by (1) empowering physical resources in production, (2) utilizing virtual and dynamic assets over the internet to expand system capabilities, (3) supporting data-driven decision making at all domains and levels of businesses, or (4) reconfiguring systems to adapt changes and uncertainties in dynamic environments. …

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 …

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 …

Joint Chance-Constrained Staffing Optimization In Multi-Skill Call Centers, Tien Thanh Dam, Thuy Anh Ta, Tien Mai

Research Collection School Of Computing and Information Systems

This paper concerns the staffing optimization problem in multi-skill call centers. The objective is to find a minimal cost staffing solution while meeting a target level for the quality of service (QoS) to customers. We consider a staffing problem in which joint chance constraints are imposed on the QoS of the day. Our joint chance-constrained formulation is more rational capturing the correlation between different call types, as compared to separate chance-constrained versions considered in previous studies. We show that, in general, the probability functions in the joint-chance constraints display S-shaped curves, and the optimal solutions should belong to the concave …

Optimal Global Supply Chain And Warehouse Planning Under Uncertainty, Avnish Kishor Malde

All Dissertations

A manufacturing company's inbound supply chain consists of various processes such as procurement, consolidation, and warehousing. Each of these processes is the focus of a different chapter in this dissertation.

The manufacturer depends on its suppliers to provide the raw materials and parts required to manufacture a finished product. These suppliers can be located locally or overseas with respect to the manufacturer's geographic location. The ordering and transportation lead times are shorter if the supplier is located locally. Just In Time (JIT) or Just In Sequence (JIS) inventory management methods could be practiced by the manufacturer to procure the raw …

Improving Patient Safety, Patient Flow And Physician Well-Being In Emergency Departments, Vishnunarayan Girishan Prabhu

All Dissertations

Over 151 million people visit US Emergency Departments (EDs) annually. The diverse nature and overwhelming volume of patient visits make the ED one of the most complicated settings in healthcare to study. ED overcrowding is a recognized worldwide public health problem, and its negative impacts include patient safety concerns, increased patient length of stay, medical errors, patients left without being seen, ambulance diversions, and increased health system expenditure. Additionally, ED crowding has been identified as a leading contributor to patient morbidity and mortality. Furthermore, this chaotic working environment affects the well-being of all ED staff through increased frustration, workload, stress, …

Development Of New Space Systems Architecture In Sysml Using Model-Based Pattern Language, Bhushan Lohar

Graduate Theses and Dissertations (2019 - present)

This manuscript presents an approach to the application of the Model-Based Systems Engineering (MBSE) and Model-Based Systems Architecting (MBSA) principles to develop a Model-Based Pattern Language (MBPL). It takes considerable time for systems engineers and mission architects to develop a new system from scratch, particularly new space-based systems derived from the existing space system architectures. The use of a pattern language which is a holistic view of reusable logical model artifacts, can improve the process.

The main benefit of the pattern language is to reduce the time and validation required to generate a new space-based system architecture; this approach will …