Operations Research, Systems Engineering and Industrial Engineering Commons^™

Forecasting Army Recruiting Mission Distribution, Maxwell A. Deihl

Theses and Dissertations

The Army’s recruiting landscape has changed markedly in recent years, raising questions about whether forecasting methods of Army contracts remain robust. This thesis recreates the presented models in Joshua McDonald’s 2015 thesis. It replicates and evaluates the models with updated data (2018–2023) to assess their current validity and compare them to novel alternative approaches, such as simpler regression models or neural networks. While the 2015 model remains a valuable baseline, results suggest that either refining its variables or adopting alternative methods can improve predictive accuracy and interpretability. Ultimately, the United States Army Recruiting Command has many options regarding how it …

Operational Energy Education: A Thematic Analysis Of Knowledge Area Needs And Educational Gaps, Nana Hene

Theses and Dissertations

Operational Energy (OE) education is vital for national security, military readiness, and fuel energy efficiency. This thesis analyzes the current landscape of OE education and identifies key gaps in awareness, energy knowledge, and curriculum structure. Through a reflexive thematic analysis of interviews with Subject Matter Experts (SMEs), the study underscores the necessity of integrating OE concepts into both educational and professional training programs. A framework is proposed to enhance OE education across various levels in the Air Force, aiming to cultivate a more energy-conscious and strategically prepared force. The findings highlight the critical need for targeted training, curriculum enhancements, and …

Designing Pull-Based Energy System Rules For Public Buildings: An Application Of Modularity, Lei Wang

Dissertations and Theses Collection (Open Access)

Building energy systems, particularly Heating, Ventilation, and Air Conditioning (HVAC) systems, play a pivotal role in global sustainability efforts. Yet, traditional centralized HVAC systems continue to face major challenges: high energy consumption, significant operational costs, and limited adaptability to dynamic energy demands. These inefficiencies are compounded by the difficulty of integrating renewable energy sources into outdated system designs. As a result, substantial energy waste persists, posing obstacles to cost-effective, environmentally sustainable building operations.

This dissertation proposes a modular, pull-based energy framework to address these critical challenges. By combining the principles of modularity theory with demand-driven energy distribution, the framework enables …

Accuracy Of Time Phasing Missile And Munition Using The Continuous Distribution Function, Joseph Zobler

Theses and Dissertations

Accurate cost and schedule estimates are crucial for maintaining the U.S. military’s technological and operational superiority, ensuring efficient resource allocation and timely development of advanced defense systems. This research examines S-curve models for time-phasing non-recurring Research, Development, Test, and Evaluation (RDT&E) expenditures in missile and munition acquisition programs. This research evaluates the commonly used 60/40 rule, which assumes 60% of expenditures occur by 50% of the schedule, for its accuracy using Cost Assessment Data Enterprise (CADE) and Earned Value Management Central Repository (EVM-CR) data from 21 missile and munition development programs.

Are Emojis The New Words? A Sentiment Analysis Of Social Media Brand Conversations, Yashodhan Karulkar, Dev T. Vora, Siddharth Vaddepalli, Yash Thakur

Journal of International Technology and Information Management

Emojis have become an increasingly important aspect of consumer-brand interactions in the Indian subcontinent. However, the impact of emoji use on brand image and mental health remains underexplored, particularly in emerging economies like India, where structured research on this topic is limited. To address this gap, the present study analyzes over 4,600 consumer tweets related to 19 prominent brands across eleven industries. Using VADER sentiment analysis, the research develops a metric to assess consumer sentiment and brand engagement in relation to emoji usage. The findings indicate that effective integration of emojis contributes to positive consumer sentiment and enhanced brand engagement. …

Analysis Of An Army Recruiter Allocation Model (Ram), Erik J. Wittendorf

Theses and Dissertations

The United States Army Recruiting Command’s mission to recruit America’s best and brightest volunteers that can deploy, fight, and win requires an effective distribution of its recruiting force to serve as local community ambassadors. This research analyzes an Army recruiter allocation model (RAM) and assesses its underlying assumptions, objective function, and constraints. A detailed study of relative market potential and production rates for up to 1,319 Army recruiting stations and 18,789 ZIP codes enables RAM modification recommendations leveraging evolving recruiting concepts and identifies areas of future work to continue improving the Army’s understanding of the recruiting environment.

Understanding The Acceptance Of Digital Tools Within An Air Force Environment Using The Utaut2 Model, Britton J. Edwards

Theses and Dissertations

This research paper explores factors influencing digital tool adoption in a military context, using a modified UTAUT2 model with the inclusion of Military Status as a moderating factor. The study examines the moderating effects of Military Status on Social Influence towards Behavioral Intention and Behavioral Intention on Use Behavior. Data was collected through a Likert-scale survey from respondents across multiple Department of the Air Force (DAF) organizations. Findings revealed Social Influence had the potential to positively influence Behavioral Intention to use digital tools, but military experience did not significantly moderate this relationship. However, past experience with the legacy tool and …

Sustainability In Forex Trading: A Review In Search Of The Sarsa-Fis Hybrid Method As A Novelty, Joni Fat, Parwadi Moengin, Pudji Astuti, Sally Cahyati

Bulletin of Monetary Economics and Banking

This study employs meta-analysis, rich pictures, timeline analysis, and causal loop diagram to explore the sustainability impacts of the SARSA-FIS hybrid method in forex trading robots. It reviews 56 references (2018-2023), using rich pictures to map AI-driven interactions. Timeline analysis traces AI’s evolution in forex, while causal loop diagram clarifies its role in market dynamics. Responsible algorithms and SRI principles mitigate risks, promoting ethical trading. SARSA-FIS enhances strategies, leveraging AI for sustainable forex practices amidst global uncertainties. The research identifies gaps and positions SARSA-FIS as a novel approach, providing a foundation for advancing AI applications in finance, particularly in forex …

Utilizing Ai For Improved Credit Risk Assessment, Emel Baglarbasi

Harrisburg University Dissertations and Theses

As the finance sector continues to evolve, traditional risk assessment methods struggle to calculate default risk and identify nonlinear relationships accurately. This research examines an alternative risk assessment model designed to estimate credit risk more accurately and efficiently in the credit processes of individual customers, which are one of the primary sources of income for the banking sector. It presents the theoretical design of an AI-based model. The use of this AI model can reduce human error in processes, improve risk assessment accuracy, and expedite procedures. The study adopts a postpositivist worldview and employs a quantitative research design. Algorithms including …

Can An Llm Use Work System Axioms When Describing Work Systems For Requirements Analysis?, Steven Alter

Business Analytics and Information Systems

This research-in-progress paper presents part of an ongoing project related to using LLMs for describing, analyzing, and designing work systems (including information systems). General axioms that apply to any non-trivial WS or IS might provide a path toward new tools and methods. This paper identifies 24 work system axioms that extend earlier research. They are organized in five categories: 1) system in context, 2) system operation, 3) system goals and goal attainment, 4) system uncertainties, and 5) system-related change. The axioms potentially address the challenge of helping business and IS/IT professionals understand and collaborate around systems in organization. This preliminary …

Understanding The Determinants Of Blockchain Adoption: An Empirical Study, Amarpreet Kohli, Nihar Kumthekar, Piyush Shah, Rebecca Jauch

Journal of International Technology and Information Management

Blockchain technology (BT) has the potential to enhance security and robustness of transactions through a distributed ledger bookkeeping process. This study employs technology-organization-environment (TOE) framework and threat-rigidity theory (TRT) to examine whether perceived disruption caused by COVID-19 pandemic significantly impacted the adoption of BT, and inclination to adopt BT in the US. The COVID-19 pandemic provided a unique backdrop, as it affected businesses across all industries, sizes, and geographies. Results show a non-significant effect of perceived pandemic disruption on the current stage of BT adoption and intention to adopt BT. However, disruption readiness positively influences the current stage of BT …

Introducing Sustainable Development Goals In College Curricula: A Way Forward, Christy Ashley, Jason D. Oliver, Hillary Leonard

Markets, Globalization & Development Review

The commentary proposes a blueprint to help guide curriculum innovations, partnerships, and teaching interventions that incorporate the United Nations sustainable development goals (SDGs) into the curriculum. It suggests the utilization of AACSB’s Societal Impact Canvas with Ancona et al.’s (2007) Leadership Capabilities (Sensemaking, Relating, Visioning, Inventing) to provide a blueprint for mission-aligned SDG integration at a local level. It provides an illustrative example from the University of Rhode Island (USA), where the focus is on the Blue Economy. It aims to provide practical guidance for how a college or university can efficiently organize to gain stakeholder input that helps enhance …

Optimal Control Of Queueing Systems With Error-Prone Servers, Junqi Hu, Sigrún Andradóttir, Hayriye Ayhan

Information Technology & Decision Sciences Faculty Publications

Consider a Markovian tandem line with finite intermediate buffers and an equal number of stations and servers. Servers are flexible but noncollaborative, so that a job can be processed by at most one server at any time. When a job is being processed, it can be damaged and wasted depending on the proficiency of the server. We identify the dynamic server assignment policy that maximizes the long-run average throughput of the system with two stations and two servers. We find that the optimal policy is either a single or a double threshold policy on the number of jobs in the …

A Systematic Literature Review On Resilient Digital Transformation, Examining How Organizations Sustain Digital Capabilities, Thira Chavarnakul, Li Da Xu, Zhuming Bi, Achyut Shankar, Gaurav Dhiman, Wattana Viriyasitavat, Danupol Hoonsopon

Information Technology & Decision Sciences Faculty Publications

In an era marked by relentless technological shifts and market volatility, digital transformation (DT) alone is insufficient. Organizations must develop Resilient Digital Transformation (RDT)—the organizational capabilities required to sustain DT over a medium-term horizon—to navigate these challenges effectively. This study primarily aims to propose a guideline for fostering RDT. Drawing on the PRISMA guidelines and a systematic review of 77 peer-reviewed papers, this study identifies and synthesizes key targets and drivers across three core pillars: Technology, Organization, and External Environment. These elements collectively foster organizational resilience. Specifically, this study highlights how adaptability, innovation, and scalability form the technological underpinnings of …

A Comparative Analysis Of Preprocessing Filters For Deep Learning-Based Equipment Power Efficiency Classification And Prediction Models, Sang-Ha Sung, Chang-Sung Seo, Michael Pokojovy, Sangjin Kim

Mathematics & Statistics Faculty Publications

The quality of input data is critical to the performance of time-series classification models, particularly in the domain for industrial sensor data where noise and anomalies are frequent. This study investigates how various filtering-based preprocessing techniques impact the accuracy and robustness of a Transformer model that predicts power efficiency states (Normal, Caution, Warning) from minute-level IIoT sensor data. We evaluated five techniques: a baseline, Simple Moving Average, Median filter, Hampel filter, and Kalman filter. For each technique, we conducted systematic experiments across time windows (360 and 720 min) that reflect real-world industrial inspection cycles, along with five prediction offsets (up …

A Comprehensive Academic And Industrial Survey Of Blockchain Technology For The Energy Sector Using Fuzzy Einstein Decision-Making, Umit Cali, Annabelle Lee, Barry Hayes, Claudio Lima, D. Jonathan Sebastian-Cardenas, David Flynn, Emre Kantar, Farrokh Rahimi, Kaung Si Thu, Marco Pasetti, Marthe Fogstad Dynge, Merlinda Andoni, Muhammet Deveci, Murat Kuzlu, Raquel Alanso, Kim-Kwang Raymond Choo, Sambeet Mishra, Shammya Shananda Saha, Sonam Norbu, Srinikhil Gourisetti, Ugur Halden, Vahid Hosseinezhad, Valentin Robu

Engineering Technology Faculty Publications

The global energy sector is undergoing a significant transformation driven by decarbonization and digitalization, leading to the emergence of Distributed Ledger Technology (DLT) — particularly blockchain — as a promising tool for enhancing transparency, security, and efficiency in modern power systems. This study aims to provide a comprehensive academic and industrial survey of blockchain applications in the energy sector and develop a robust decision-making framework to identify and prioritize the most promising real-world use cases based on multidisciplinary criteria. A three-stage methodology was adopted: (i) a literature and market review encompassing over 300 academic publications and commercial blockchain initiatives in …

Predicting Crises On The African Frontier Stock Markets With Investor Sentiment Indicators: A Machine Learning Approach, David Korsah, Lord Mensah

Journal of International Technology and Information Management

This study examined the predictive ability of machine learning algorithms in identifying crises within African stock markets. The study employed seven distinct machine-learning models, analyzing historical stock prices from eight stock markets, three major sentiment indicators, and the exchange rates of local currencies against the US dollar, with each data spanning from May 1, 2007, to April 1, 2023. Extreme Gradient Boosting (XGBoost) emerged as the most effective algorithm for predicting crises. Historical stock prices and exchange rates were identified as the most critical features for prediction. On the sentiment side, investors’ perceptions of potential volatility on the S&P 500, …

Pedagogy In The Age Of Ai: Exploring Generative Ai For Higher Education, Alison Munsch Phd

Journal of International Technology and Information Management

Generative Artificial Intelligence (AI) presents transformative opportunities for higher education, enabling personalized learning, enhanced student engagement, and efficient pedagogical practices. This tutorial-style article guides educators in integrating generative AI into their classrooms through hands-on activities, practical strategies, and reflective exercises. It explores the capabilities of AI tools such as ChatGPT, their applications across disciplines, and the ethical considerations for their use. By cultivating critical thinking and fostering student readiness for AI-driven futures, this article underscores the transformative potential of generative AI in higher education with an emphasis on the academic areas of business analytics, information systems, and computer science.

Surviving And Thriving In The Hybrid Cloud: A Review Of The Current Cloud Computing Landscape, Peter Munsch, Alison Munsch

Journal of International Technology and Information Management

Background and Purpose

Both academic and industry institutions have increasingly migrated essential services to public cloud providers (e.g., Microsoft, AWS, Google) with mixed outcomes. Some industry leaders attempted to fully replace their on-premises data centers with public cloud services, a move not advised without thorough performance and cost analyses (Potel, 2023). Despite some organizations pulling back from the “Cloud First” strategy, the public cloud services market continued to grow, with revenue increasing by approximately 20% year-over-year since 2020 and surpassing half a trillion dollars in 2022 (IDC Worldwide Semiannual Public Cloud Services Tracker, 2H 2022). Cloud technologists suggested that hybrid …

Predicting Global Healthcare Supply Chain Delays: A Machine Learning Approach Leveraging Country-Level Logistics Metrics, Jeevan Sai Gali, Nima Molavi, Sepideh Alavi

Journal of International Technology and Information Management

In global healthcare logistics, ensuring the timely delivery of medical commodities is critical, particularly in low- and middle-income countries characterized by infrastructural limitations and operational uncertainties. This research introduces an advanced, data-driven predictive framework designed to forecast delivery delays by synthesizing granular, internal shipment-level data from the USAID Global Health Supply Chain Program (GHSC-PSM) with external country-level logistics capabilities indicators derived from the World Bank’s Logistics Performance Index (LPI). Rather than relying on retrospective trend analyses, this study employs machine learning algorithms such as Random Forest, XGBoost, Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP) to detect …

Insider Threat Agent: A Behavioral Based Zero Trust Access Control Using Machine Learning Agent, Michael Fojude

College of Graduate Studies: Theses & Dissertations

Hybrid work, cloud adoption, and freely available AI‑enabled attack tools have exposed critical weaknesses in perimeter‑centric security. Current breach reports attribute more than one‑third of incidents to insider misuse or credential compromise, yet many organizations still depend on static Role‑ or Attribute‑Based Access Control that neither verifies intent continuously nor adapts to subtle behavioral change. This research addresses that gap by designing and validating a behavioral based Zero Trust Access Control (ZTAC) Agent. A five‑year enterprise log Dataset was extracted and cleansed to establish a high‑fidelity baseline of normal user behavior. Feature engineering captured temporal regularity (login sequence, session duration), …

Suas Agricultural Aerial Application Operational Field Test, David Thirtyacre, Joseph Cerreta, Scott S. Burgess

International Journal of Aviation, Aeronautics, and Aerospace

One of the most promising uses of aerial applications by a sUAS is on small farms where traditional crewed aerial applicators were not practical due to the limited size of the operations. This controlled field test aimed to assess the feasibility and cost-effectiveness of using a sUAS spreading system compared to the traditional manual application of weed killer and fertilizer on cranberry bogs in Western Washington that were less than 10 acres. The aerial application took place over two separate days as scheduled by the farmer for maximum product efficiency. A total of sixty-three flights were necessary to apply the …

Digital Twin And Cybersecurity In Additive Manufacturing, Lidong Wang

Journal of Cybersecurity Education, Research and Practice

Additive manufacturing (AM) has been applied to automotive, aerospace, medical sectors, etc., but there are still challenges such as parts’ porosity, cracks, surface roughness, intrinsic anisotropy, and residual stress because of the high level of thermal gradient. It is significant to conduct the modeling and simulation of the AM process and achieve quality products. Digital Twin (DT) can help AM with forecasting defects/errors through simulation and real-time process monitoring. DT is a concept of Industry 4.0, and its digital structure reflects the real-time behaviors of a cyber-physical or physical system. This paper introduces the progress of DT applications in AM, …

The Boris Experience: Evaluating Omnichannel Returns And Repurchase Intention, Jianliang Hao, Robert G. Richey Jr., Tyler R. Morgan, Ian M. Slazinik

Faculty Publications

Researchers have examined the influence of the factors on reducing return rates in retailing over the years. However, the returns experience is often an overlooked way to drive customer engagement and repeat sales in the now ubiquitous omnichannel setting. The focus on returns prevention in existing research overshadows management’s need to understand better the comprehensive mechanics linking the customer in-store return experience with their repurchase actions. Recognizing the need to bridge different stages of the returns management process, this research aims to explore the facilitators and barriers of in-store return activities.

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 …

Prompt Engineering Principles For Generative Ai Use In Extension, Paul A. Hill, Lendel K. Narine, Aubree L. Miller

Journal of Extension

The prevalence of Generative AI (GenAI) and Large Language Models (LLMs) is increasing rapidly. For Extension professionals, the utilization of prompt engineering is key to leveraging GenAI and LLMs effectively. Prompt engineering involves crafting prompts that elicit desired LLM responses. This article discusses prompt engineering principles, providing examples and guidance. The application of prompt engineering in Extension is explored, showcasing its potential to enhance programs, deliver personalized advice, engage audiences, and disseminate research-based information. By learning prompt engineering skills, Extension professionals can harness the power of GenAI and LLMs, enhancing their ability to address complex challenges in the 21st century.

An Exponential Cone Programming Approach For Managing Electric Vehicle Charging, Li Chen, Long He, Yangfang (Helen) Zhou

Research Collection Lee Kong Chian School Of Business

To support the rapid growth in global electric vehicle adoption, public charging of electric vehicles is crucial. We study the problem of an electric vehicle charging service provider, which faces (1) stochastic arrival of customers with distinctive arrival and departure times, and energy requirements as well as (2) a total electricity cost including demand charges, costs related to the highest per-period electricity used in a finite horizon. We formulate its problem of scheduling vehicle charging to minimize the expected total cost as a stochastic program (SP). As this SP is large-scale, we solve it using exponential cone program (ECP) approximations. …

Appointment Scheduling With Delay Tolerance Heterogeneity, Shuming Wang, Jun Li, Marcus Ang, Tsan Sheng Ng

Research Collection Lee Kong Chian School Of Business

In this study, we investigate an appointment sequencing and scheduling problem with heterogeneous user delay tolerances under service-time uncertainty. We aim to capture the delay-tolerance effect with heterogeneity, in an operationally effective and computationally tractable fashion, for the appointment scheduling problem. To this end, we first propose a Tolerance-Aware Delay (TAD) index that incorporates explicitly the user-tolerance information in delay evaluation. We show that the TAD index enjoys decision-theoretical rationale in terms of Tolerance Sensitivity, Monotonicity, Convexity and Positive Homogeneity, which enables it to incorporate the frequency and intensity of delays over the tolerance in a coherent manner. Specifically, the …

Order-Picking Strategies And Efficiency Models For The Fulfillment Of Multi-Line E-Commerce Grocery Orders, Zijia Wang

Dissertations

The fulfillment process for Buy Online Pickup from Store grocery orders (BOPS-Grocery) is particularly challenging, given the large item count per order and the low-profit margins. This research investigates the BOPS-Grocery model and pursues the following objectives: (i) Defining and specifying the BOPS-Grocery fulfillment process. Differentiating the process from classical warehouse order picking., identifying performance objectives, and characterizing design options and facility layout. (ii) Modelling and developing order picking algorithms specific to the straight aisle section. Formulated as an order hatching problem with picker movement minimization. No order splitting, and (iii) modeling and developing order picking algorithms specific to the …