Operational Research Commons^™

Inspection Program Effectiveness Key Performance Indicator For Pressurized Static Equipment Integrity At Offshore Platform, Teuku Ahmad Haekal, Johny Wahyuadi Soedarsono, Badrul Munir, Muhammad Yudi Masduky Sholihin

Journal of Materials Exploration and Findings

One of the key challenges in asset integrity management system at offshore platform is the lack of visibility regarding performance issues and program effectiveness. Without proper performance measurement systems, it becomes difficult to address positive or negative trends promptly and for management to stay informed about the status and the impact of the inspection program. Therefore, Key Performance Indicator (KPI) is needed to measure inspection program effectiveness to prevent undesirable equipment failures that could lead to Loss of Primary Containment (LOPC) or Process Safety Event (PSE). The developed KPI is the ratio of the number of non-leak inspection findings with …

Strategic Responses For Unplanned Events, Tidjan Simpson

Harrisburg University Dissertations and Theses

The paper addresses the question, “Can dynamic, effective response plans be made for stakeholders of a manufacturing line dealing with unplanned events at a manufacturing line, irrespective of an individual’s unique subject matter expertise? Prior research in manufacturing-related environments has indicated the existence of a high frequency of unplanned events. When not responded to efficiently, they can result in reduced financial efficiency and employee overwhelm. Through collection and analysis of interviews conducted with stakeholders in the manufacturing environment, a possible means of efficiently addressing unplanned events can be found or synthesized to help stakeholders navigate uncertainty in the manufacturing environment …

The Practical Adoption And Application Of Blockchain Technology Within The Beverage Industry, Alexander Adams Jr

Electronic Theses, Projects, and Dissertations

Abstract

The beverage industry is facing heightened scrutiny as the demand for transparency and accountability reaches new heights. In the age of information technology, companies must prioritize enhanced traceability to ensure product safety, comply with government regulations, maintain customer trust, and protect brand integrity. This thesis explores the potential of blockchain technology as a solution to these challenges, focusing on its ability to decentralize data, improve traceability, and expedite response times during safety recalls. The research provides an overview of the evolution of food safety regulations, beginning with the first establishment by Upland Sinclair, and examines current traceability practices and …

Integrating Risk And Vulnerability: Exploring A Unified Model For Supply Chain Resiliency, William G. Cook

USF Tampa Graduate Theses and Dissertations

The world has entered an era of retreating globalization, mounting geo-political tensions, rising protectionism, and increasing focus on the fragility of complex supply chains. The negative impacts of supply chain disruptions have been increasingly documented since the turn of the century. Given the global scale of recent disruptions, supply chain resiliency has become a national imperative. The Global Financial Crisis, the Covid-19 pandemic, and other major disruptive events demonstrate the active role of government in mitigating damage, the enduring effects of regulation, and the resultant re-evaluation of supply chain strategies by the private and public sectors. In this environment, supply …

Inexact Methods For Large-Scale Stochastic Programming, Niloofar Fadavi

Operations Research and Engineering Management Theses and Dissertations

This dissertation addresses the development of inexact methods for solving large-scale stochastic programming problems, with a focus on two-stage and multistage settings. Stochastic programming is a robust approach for managing uncertainty in decision-making, with applications across various domains like supply chain management, power systems, and logistics. However, solving large-scale stochastic programming problems, especially those with a nonlinear structure, is computationally challenging due to the high-dimensional nature of uncertainties and the need for efficient optimization techniques.

This work introduces novel inexact proximal bundle algorithms designed to solve two-stage stochastic quadratic programming problems. The proposed methods utilize dual-based and partition-based approaches to …

An Integrated Theoretical Socio-Technical Framework For Implementing Service Robots’ Integration In Healthcare, Sujatha Alla

Engineering Management & Systems Engineering Theses & Dissertations

Healthcare workers, either clinical or non-clinical, are obligated to serve patients. However, lack of a sufficient number of professionals leads to burnout, severe stress, and, consequently, decreased quality of services. In this context, very few countries have been successful in employing service robots to perform dull, dirty, and/or dangerous tasks related to patient wellbeing/healthcare, while most countries are still skeptical about it. As robotics advances, there is an opportunity for healthcare to take advantage of this technology to reduce personnel workload and to reduce the possibility of exposure to contagious pathogens. However, healthcare is a vulnerable environment and requires critical …

Toward Adaptive And Modular Joint Multi-Domain Operational Planning, Kyle S. Wilkinson

Theses and Dissertations

This research develops a multiparametric optimization framework for modeling joint multi-domain operational planning under uncertainty. We address the application of our framework to model the doctrine of adaptive planning. We apply set-based design, which is a program management practice of maintaining maximal design options through time as a response to epistemic uncertainty. We couple this with a multiparametric optimization method yielding both sets of solutions and sensitivity profiles. We use the sensitivity profiles to quantify risk associated with changes during adaptive planning. This research also models features of military operational planning via the mathematics of category theory. We formalize intuitive …

Improving Military Medical Evacuation System Performance Via Stochastic Optimization, Virbon B. Frial

Theses and Dissertations

This research highlights the importance of improving the performance of military medical evacuation systems to reduce the risk of permanent disability or death among service members in deployed environments. We employ a range of stochastic optimization techniques relating to integer programming, Markov decision process, approximate dynamic programming, and machine learning, as appropriate, to gain insights into factors that contribute to improving system performance.

Data Driven Decision Making For Sustainable Planning And Operations Of Large Scale Networks, Bahareh Kargar

Dissertations

This dissertation explores data-driven decision-making networks, focusing on sustainable planning and operations for large-scale systems such as healthcare supply chains and power systems. One significant application in healthcare is the optimization of vaccine supply chains. An agent-based simulation-optimization modeling framework is developed to enhance the efficiency and sustainability of vaccine distribution. First, an agent-based epidemiological model of COVID-19 is extended to capture disease transmission dynamics and forecast the number of susceptible individuals and infections. Then, a sustainable vaccine supply chain considering the impacts of greenhouse gases is developed and integrated with the simulation model to minimize total costs and environmental …

Predictive And Prescriptive Analytics For Minimizing The Cost Of Recidivism, Adreana Julander

Operations Research and Engineering Management Theses and Dissertations

A problem faced by the United States is the ever increasing prison population. There are inmates serving long sentences, new inmates being sentenced for the first time, and those who have previously served prison sentences that reoffend. The third group and the reduction of recidivism are the focus this dissertation. It is estimated that over 80% of inmates released from prison will reoffend within the next ten years. Is there an optimal sentence length that reduces that chance of an ex-convict reoffending? Are there programs or opportunities that some inmates have while incarcerated that reduce the probability they will return …

An Emergency Response System To Assist The Movement Of Vehicles During Emergency Operations In Urban Transportation Networks, Jamal Nahofti Kohneh

All Dissertations

Emergency responders need to arrive at the emergency scene as soon as possible, but operating vehicles under emergency conditions can pose a risk to both the responders and other road users, potentially resulting in crashes or delays in emergency operations. In this research, an emergency response system is proposed to assist emergency and non-emergency response vehicles (ERVs and non-ERVs) during emergency operations in a connected vehicle environment. This system collects the information from connected ERVs and non-ERVs, utilizes this information as inputs in the proposed models, and sends instruction messages back to vehicles. The proposed models provide the fastest ERV …

Authenticated Diagnosing Of Covid-19 Using Deep Learning-Based Ct Image Encryption Approach, Mohamed Attia Abdelgwad, Amira Hassan Abed, Mahmoud Bahloul

Future Computing and Informatics Journal

Researchers are motivated to use artificial intelligence in biometrics, medical imaging encryption, as well as cybersecurity due to its rapid progress. An encryption method for CT scans—which are used to diagnose COVID-19 disease—is proposed in this study. The suggested encryption method creates a connection among an individual's face picture and CT image to increase confidentiality. The simple CT picture is first enhanced with a host image. An encryption key is multiplied by the final result. This key is produced by applying a Convolutional Neural Network (CNN) to recognize characteristics from people's face photographs. Additionally, a straightforward CNN with three convolutional …

An Integrated Space Test Lexicon: A Taxonomy For The Integrated Test And Evaluation Of Space Systems, Stephen K. Tullino, Andrew S. Keys, Robert A. Bettinger, Amy M. Cox, David R. Jacques

Faculty Publications

The proposed Integrated Space Test Lexicon is intended to amalgamate the numerous definitions of integrated (IT or IT&E), development test (DT or DT&E), and operational test (OT or OT&E) into unified, service-wide definitions, aligned with the Space Test Enterprise Vision. Refining such definitions will help distill the core characteristics of these fundamental test types to first identify space system activities composing what is traditionally known as DT and OT, then to provide a means of how these activities fit into the IT paradigm and support space system development. In forging a common understanding of how DT and OT support space …

Evaluating Climate Sentiment In Sec 10-K Filings: S&P 50 Companies, Ruby Chu

Dissertations, Theses, and Capstone Projects

Evaluating Climate Sentiment in SEC 10-K Filings: S&P 50 Companies investigates how climate sentiment is portrayed in corporate financial reporting, focusing on SEC 10-K filings from leading S&P 50 companies. These filings offer detailed insights into financial performance, risks, and management discussions, providing a rich dataset for analyzing corporate behaviors with sustainability and environmental concerns. This study aims to shed light on the extent to which companies address environmental issues and the implications for environmental stewardship by analyzing how these topics are portrayed in their SEC 10-K filings. Drawing inspiration from established greenwashing indicator frameworks, the study develops a climate …

Connection-Saving Gate Assignment: A Computational Approach, Rob Mailley

Computer Science Senior Theses

The growth of the commercial aviation industry has yielded many interesting problems in the field of Operations Research, many of which are now able to be solved as both technology and mathematical optimization improve. A particularly interesting problem in airport operations re- search is the Aircraft Gate Assignment Problem (AGAP), which seeks to create a feasible match- ing between planes and flights at an airport. This problem is well-suited to modeling with Integer Programming, and has attracted research since the 1970s. Researchers of the AGAP have considered many different objectives, ranging from airline-focused objectives to more passenger-focused objective functions. In …

Design Of Small-Scale Milk Processing Facility, Luke Bond, Robert Stenzel, Ry Steffen, John Van Nieuwenhuyse, Rossana Villa-Rojas, Terry Howell Jr., Tami M. Brown-Brandl, Forrest Kievit

Department of Agricultural and Biological Systems Engineering: Masters Project Reports

This project was intended to investigate the feasibility of a proposed modular dairy processing facility collocated with a small dairy on the University of Nebraska–Lincoln’s Innovation Campus. Priority was placed upon cost savings for equipment to be used in processing. Process flow diagrams, floor plans, production schedules, risk assessments, and plans for wastewater treatment were all developed to address the needs of the plant should it be built. A Monte Carlo simulation was conducted to evaluate the timeline for breaking even financially, resulting in the potential for a payback period of 4.5 years. The 4.5-year payback period was based on …

Reduction Of The Length Of Stay At The Emergency Department Of The Audie L. Murphy Hospital, Arwa Al Shikrian

Theses

We implemented the DMAIC framework (Define, Measure, Analyze, Improve, and Control) and Lean Six Sigma methods to reduce the length of stay (LOS) in the Emergency Department (ED) at Audie L. Murphy VA Hospital, a crucial issue affecting operational efficiency and patient care quality. This project was conducted in conjunction with an internship at the Hospital; our efforts were an integral part of a Green Belt project being conducted by Mr. Roarke Verkaik-Bushby, Hospital Administration Service, to

whom this author reported.

We employed quantitative research design, meticulously observing, measuring, and analyzing ED processes. We tracked patient flow, identified bottlenecks, and …

Open-Source Optimization For Green Last Mile Delivery And Other Applications, John Sooter

Industrial Engineering Undergraduate Honors Theses

Solving combinatorial optimization problems at scale and of sufficiently interesting context has historically required commercial solvers and access to proprietary company data. The development of performant open-source mathematical programming software and crowdsourced datasets has created an opportunity for individuals and enterprises alike to consider alternative solutions to problems with social and personal implications. This honors thesis represents a summary of my undergraduate research work, an application of optimization to three distinct problems connected to these developments. First, we present an optimization study of a last mile delivery system that shows optimization for energy consumption can generate vehicleindependent fuel savings at …

Modeling Of Multi-Period Disaster Logistics Planning In The State Of South Carolina, Emma Simon

All Theses

South Carolina is one of the most vulnerable states in the United States to the impact of hurricanes. Currently, when threatened with a natural disaster such as a hurricane, the state government makes many vital decisions based on knowledge and experience. In this study, the distribution of disaster relief commodities to meet immediate needs is analyzed through two models for the case of South Carolina to generate an optimal logistics strategy that considers the social vulnerability of affected populations. The first model is a multi-objective pre-disaster logistics model that uses a four-index formulation for the multiple trip vehicle routing problem. …

The Importance Of Data Preparation In A Data Science Problem, Sophia Beard

Data Science Undergraduate Honors Theses

This study is going to be based on an inventory outlier automation data science problem that is being solved to identify and prescribe inventory level outliers to help keep shelves stocked in terms of beverages. The objective of this paper will address why it is so important to understand the data that is involved in a particular data science problem and how planning ahead ensures a successful outcome in the data science world. In this data science project, Spatiotemporal Outlier Analysis for Inventory Intervention Automation, it was crucial for the team to understand, research, and visualize the data we were …

The Importance Of Text Representation For Neural Networks Through Natural Language Processing Techniques, William Parsley

Data Science Undergraduate Honors Theses

Text representation is a fundamental aspect of natural language processing (NLP) when it comes to the performance of neural networks. Free-form text fields are being utilized in more and more industries. Anything from a description of an item on a web store to tracking service events to military-grade aircraft is being collected in free-form text. The goal of the thesis is to highlight best practices and discuss trends in data to prepare text for a neural network. It will demonstrate various techniques for representing free-form text in the context of neural networks, focusing on data preparation decisions, embedding techniques, and …

Examining The Impact Of Customer Rfp Characteristics On Award Compliance, Laasya Ravipati

Data Science Undergraduate Honors Theses

In the context of intermodal transportation, understanding the dynamics of award compliance holds significant importance for operational efficiency and strategic decision-making. Award compliance refers to the percentage of awarded freight volume that is realized, indicating the extent to which contractual agreements are fulfilled. This analysis delves into the intricate relationship between customer characteristics and award compliance, aiming to provide valuable insights into the variability and predictability of compliance rates. By analyzing Request for Pricing (RFP) data and primary awarded freight volumes, the study seeks to address the need for more accurate volume estimations, crucial for sales planning, revenue projections, and …

Examining Award Compliance To Inform Resource Allocation, Jacob Haarala

Data Science Undergraduate Honors Theses

This project focuses on JB Hunt Transport Inc's intermodal business unit (JBI) by focusing on the challenges associated with Published Pricing and Contractual Pricing. The primary issue revolves around the variance between the awarded freight volumes in Requests for Pricing (RFPs) and the actual volumes realized when the freight is shipped. This discrepancy poses challenges for effective sales planning, revenue goals, and optimal freight network management within JBI. Reporting tools, such as PowerBI, are currently used by JBI to provide insights into award compliance on a weekly basis. However, our goal with this project was to provide a deeper understanding …

Cost-Risk Analysis Of The Ercot Region Using Modern Portfolio Theory, Megan Sickinger

Master's Theses

In this work, we study the use of modern portfolio theory in a cost-risk analysis of the Electric Reliability Council of Texas (ERCOT). Based upon the risk-return concepts of modern portfolio theory, we develop an n-asset minimization problem to create a risk-cost frontier of portfolios of technologies within the ERCOT electricity region. The levelized cost of electricity for each technology in the region is a step in evaluating the expected cost of the portfolio, and the historical data of cost factors estimate the variance of cost for each technology. In addition, there are several constraints in our minimization problem to …

Denoising Diffusion Probabilistic Models Based Accelerated Mri, Alexander Francis Bugielski

Theses and Dissertations

Magnetic Resonance Imaging (MRI) is a cornerstone in obtaining intricate visualizations of anatomy and physiological processes within the human body. However, its extensive scan duration not only causes patient discomfort but also increases the likelihood of motion-induced artifacts in the images. To address such a challenge, this study investigates deep neural network models for reconstructing high-resolution MRI images from noisy and significantly undersampled data in a supervised learning manner. Specifically, it compares three models: a conventional U-Net, a self-attentive U-Net, and an innovative probabilistic diffusion model that builds upon the self-attentive U-Net architecture. These models are evaluated on their ability …

Adopting Information System Technologies In Construction Project Management, Hadi Haikal

Harrisburg University Dissertations and Theses

The construction sector has remained late in integrated information systems' adopting process to perform the system improving processes. Lack of adoption of technology has been a root cause of the early issues they faced, such as budgetary and scheduling overshoots, quality defects, safety occurrences, and generally poor communication and coordination among the stakeholders. This research looks into the benefits and challenges, the key success factors, and the implications of using an array of information systems, including building information modeling (BIM), artificial intelligence (AI), Internet of Things (IoT) sensing, and enterprise systems, to comprehensively address issues in project management in construction …

Analyzing The Impact Of Socioeconomic Factors On Cancer Clinical Trials Accessibility In The U.S. Using Machine Learning, Krysta L. Ray, Hiromi Honda

ATU Scholars Symposium

While cancer impacts all segments of the United States population, specific groups experience a disproportionate burden of the disease due to social, environmental, and economic disadvantages. This research examines the correlation between socioeconomic factors and the accessibility of cancer clinical trials across U.S. counties, employing a comprehensive dataset, County-Level Socioeconomic and Cancer Clinical Trial Data from Noah Ripper, and advanced machine-learning techniques. Our findings, derived from regression analysis and machine learning models like gradient boosting, highlight significant disparities in trial availability linked to socioeconomic indicators, including poverty rates, population estimates, median income, incidence rates, and mortality rates. Many regression models …

Optimization Of Human Interactions In The College Campus Model Via Simio Integration, Benjamin E. Chaback

Doctoral Dissertations and Master's Theses

College campuses are a significant part of life in some cities. Many students each year attend university, pursuing additional knowledge from faculty members. Both staff and faculty members rely on these students to have successful jobs and to ensure the university functions. Yet recently, more and more students are attending, leading to overcrowding, lower admission rates, and difficulty getting into good programs. Previous work exists on qualitative student affairs and quantitative retention data, yet little on using simulations to model this problem. This work aimed to (a) Determine the ability to successfully model human interactions/people flow on a college campus, …

Understanding The Impact Of Emergent Conflict On Communication And Team Cognition: A Multilevel Study In Engineering Teams, Francisco Cima

Engineering Management & Systems Engineering Theses & Dissertations

The development of team cognition is crucial for fostering high-performing teams. In cognitive-intensive fields like engineering, effective communication serves as a primary precursor to team knowledge development, enabling group members to effectively retrieve and utilize each other's expertise. Despite the critical role of communication, there is a lack of empirical research examining how conflict situations, which are critical emerging factors inherent to teamwork, interact with communication processes to constrain team knowledge development and utilization. This study, rooted in information processing theory, investigates how emerging conflict shapes multilevel team knowledge structures by interacting with communication processes in engineering project teams. Prior …

Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs

Theses and Dissertations

Leveraging the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) we investigate the use of reinforcement learning (RL) techniques for imbuing AUCAV agents with high-quality behaviors for the within-visual-range air combat maneuvering problem (ACMP). We formulate the 2v2 WVR ACMP as a Markov decision process wherein friendly AUCAVs are equipped with DEW capabilities and operate with 6 degrees of freedom. We utilize the Double Deep Q-Network RL algorithm, which centrally trains two friendly AUCAVs and employ a phased learning approach, initially exposing the AUCAVs to a dense reward environment for early training, followed by a sparse reward environment to encourage …