Operational Research Commons^™

Spatial Markov Equilibrium Models For Taxi Services: Driver Decision, Search Friction, And Locational Pricing, Yanchao Liu

Industrial and Systems Engineering Faculty Research Publications

This paper develops a modeling framework for stochastic multi-agent systems and applies it to equilibrium and pricing analysis in urban taxi markets. Travel demand is represented as a trip network and embedded in a Markov chain that captures both locational and in transit taxi states, with transition dynamics reflecting trip durations, search frictions, spatial competition, and drivers’ perceptions of long-term value. The framework features a parametric Markov chain with endogenous transition probabilities and a behavioral model in which agents’ decisions depend on anticipated long-term rewards. We establish equilibrium existence and examine two locational pricing schemes that align individual incentives with …

Optimization Of Gas Consumption, Cost And Production Rate For Computerized Numerical Control Oxy-Acetylene Flame Cutters, Eustace K. William, Simon I. Marandu, Enock W. Nshama

Tanzania Journal of Science

This study examined the impact of flame cutting parameters (i.e., cutting speed, plate thickness and nozzle diameter) on oxy-acetylene gas consumption, cost and production rate. A full factorial design of experiments was used to generate 27 experiments, which were conducted using a CNC flame cutter. The analysis of variance (ANOVA) method was used to determine significant process parameters, followed by regression analysis using the MINITAB ® software. The technique for order preference by similarity to ideal solutions (TOPSIS) was used to determine the optimal cutting parameters for minimizing the consumption and cost estimation of oxy- acetylene gas and maximizing the …

Innovative Controls For Combustible Particulate Dust In Industrial Refineries, Sadie Dickman

Discovery Day - Daytona Beach

This project entails an extensive analysis of the occupational combustible dust hazard that exists in industrial refineries, including food processing, manufacturing, and metalworking facilities. Based on prior accidents like the 2008 Imperial Sugar and 2017 Didion Milling explosions, combustible dust causes fire and explosion risks that can result in damages, injuries, and fatalities. Through comparison of OSHA regulations, NFPA standards, and accident data, a lack of effective standards regarding training, audits, ventilation, fire prevention, and adequate housekeeping measures were highlighted to be an existing safety gap. The purpose of this study is to propose administrative controls to mandate companies to …

Designing Under Pressure: A Comparative Study Of Ai And Manual Interface Development In A Naval Weapon System Scenario, Tsimur Babakhanau, Noah Clark, Colby Keller, Mary Grace Sorenson, Zoe Tiede

Discovery Day - Daytona Beach

This study implements a detailed naval scenario in which participants acted as operators on a Navy destroyer equipped with a Laser Weapon System (LaWS). Their task was to create a dashboard capable of stopping incoming suicide drone swarms while managing critical laser functions such as thermal constraints, threat prioritization, and adapting to attack dynamics. Poor management could leave the ship vulnerable. AI is increasingly integrated into design methods, fundamentally transforming the process of building user interfaces by compressing hours of work into minutes. Although AI design tools are becoming more common, little is known about how well beginners can use …

Shipboard Fire & Flooding: Evaluating Ai-Assisted Interface Design For Novice Designers, Rachel Phillips, Abigail Threat, Parneet Makkar, Jasmine Cruz, Matthew Skowronek

Discovery Day - Daytona Beach

In naval damage control scenarios, operators in Damage Control Central (DCC) must interpret information from numerous sensors at once to detect hazards such as fire, flooding, or smoke while also maintaining ship stability. Designing interfaces that effectively support these tasks typically requires significant experience in human factors and military design standards. This project investigates whether AI-based design tools can help novice users produce functional interface prototypes more efficiently. Participants were randomly divided into two groups: an experimental group that used an assigned AI tool (Figma Make, Visily, or Claude) to generate and refine interface layouts, and a control group that …

High Tempo Air Operations, Joseph Lipson, Brennan Flanagan, Trevor Sterbens, Brandon Godfrey, Jeremiah Sepich

Discovery Day - Daytona Beach

Aircraft carrier flight decks are one of the most dangerous work environments in the world, where dozens of aircraft must be moved, fueled, and armed within strict time limits. Currently, Flight Deck Handling Officers track aircraft positions using a physical board with wooden pucks that can be knocked out of place or become outdated during fast-moving operations. This study looks at whether using AI tools helps people design a better digital version of this tracking system. Participants with little design experience were randomly selected and then randomly assigned to one of two groups — one that could use AI tools …

Humans Vs. Ai: Comparing Approaches To Disaster Response Interface Design, Kelly Nguyen, Olivia Hartmann, Kailey Hrbek, Madeline Nees, Gabrielle Roth, Emily Silliman

Discovery Day - Daytona Beach

Amphibious emergency support operations involve rapidly changing information, high stress, and significant cognitive demands, which can make decision-making and situation awareness more difficult for operators. When interfaces are poorly designed, they can contribute to issues such as alarm flooding, confusion from incomplete information, and delayed responses, all of which increase operational risk during time-critical disaster situations. This study explores whether using generative AI to assist with interface design will improve performance (output quality and effort) and usability compared to a manual sketch mock-up. Participants were asked to design a dashboard interface to support disaster relief operations following a Category 5 …

C.S.A. Assessment - Tamp Family Health Center Portal, Delante Clark

Graduate Scholarship and Creative Works

This C.A.S. assessment evaluates how the Tampa Family Health Centers website influences userattention, cognitive processing, information accessibility, navigation efficiency, and digital user experience.The assessment examines whether the platform supports intentional engagement and informed decision-making while minimizing cognitive overload, distraction, confusion, and unnecessary attentional demands.

TFHC serves as a healthcare access portal providing appointment scheduling, patient resources, providerinformation, healthcare services, MyChart access, payment services, and community health resources.These functions make attention management and information clarity critical to successful user outcomes.

Prediction Of Satellite Temperature During An Orbit Of A Cubesat, Michael Reynolds

Honors Theses

This thesis develops a thermal-simulation strategy for predicting CubeSat component temperatures, applied to Jag-Sat-1, a CubeSat developed at the University of South Alabama and deployed from the International Space Station in 2022. The orbit was reconstructed from two-line element (TLE) data using simplified general perturbations (SGP4) propagation, and spacecraft attitude was recovered from onboard gyroscope measurements. Sunlight, penumbra, and umbra intervals were computed geometrically, and the external radiative environment — direct solar, Earth infrared, and albedo heat fluxes — was modeled using orientation-dependent view factors. These time-varying fluxes drove a transient finite-element thermal simulation of the full satellite geometry in …

Integrasi Metodologi Hazop Dalam Pengendalian Risiko Dan Keberlanjutan Operasional Pada Unit Pemulihan Urea: Studi Kasus Pada Industri Pupuk, Riny Yolandha Parapat, Arin Nur'aini Putri, Aryasatya Ramadhan Sukresno

National Journal of Occupational Health and Safety

The fertilizer industry is one of the chemical sectors with high-risk potential due to its operational processes involving hazardous materials as well as extreme pressure and temperature conditions. This study aims to identify and analyze hazards in the urea recovery unit using the Hazard and Operability Study (HAZOP) method, while also formulating effective risk control strategies to support the operational sustainability of the plant. The study was conducted directly at a commercial fertilizer plant in West Java using a semi-quantitative approach, which included field observations, review of technical documents such as Process Flow Diagrams (PFD), Piping and Instrumentation Diagrams (P&ID), …

Can Generative Ai Make Farming Decisions? Current Status And Future Pathways: A Case Study In Row Crop Production With Chatgpt, Nipuna Chamara, Yufeng Ge, Joe Luck, Yu Pan, Saleh Taghvaeian, Cory Walters, Christopher Proctor, Daran Rudnick, Daren Redfearn

Department of Agricultural and Biological Systems Engineering: Faculty Publications

The agricultural decision-making process is experience-based, knowledge-dependent, time-sensitive, complex, and driven by historical data. Planting, fertilization, irrigation, and chemigation are key categories in farm decision-making, and currently there is no one-shot decision-support tool that covers all these activities. Generative Artificial Intelligence (AI) models are more advanced than traditional machine learning and deep learning models. These models have been trained on vast amounts of data from the internet, allowing them to accept unstructured data in various forms and generate human-like text, solutions to problems, and scenario predictions. Given this capability, we became interested in exploring the potential of generative AI in …

Modeling Psychological And Demographic Predictors Of Analog Astronaut Mission Participation, Christian Yeara Herrero, Frányerson R. López Ochoa, Mackenzie Thomas, Phoebe Fleshman

Student Research Symposium (SRS)

As plans accelerate to send humans into orbit and to other celestial bodies, whether to lunar outposts, Mars bases, or commercial space stations, it becomes increasingly important to understand how to maintain healthy, cohesive, and productive crews in confined, isolated environments. A practical way to study human adaptation to these conditions is through analog astronaut missions on Earth. Although imperfect, these facilities provide the closest Earth-based simulation of space mission conditions. Currently, over ten analog research centers are operating worldwide, including NASA’s Human Exploration Research Analog (HERA) and the Crew Health and Performance Exploration Analog (CHAPEA) habitats. Selecting and recruiting …

Development Of A Putting Green Manufacturing Process, Tabitha R. Webster

Honors Theses

Our Capstone project investigates the end to end design, development, and production of a 6‑foot long portable putting green marketed for individuals seeking a high quality, competitively priced golf product for home or office use. The capstone project examines the full lifecycle of product creation applying manufacturing principles learned through the center of manufacturing’s coursework. From the initial concept through engineering design, market research, prototyping, manufacturing optimization, and final production the project emphasizes cross‑functional collaboration across engineering, business, and accountancy majors. Methods used to gather data included marketing surveys, CAD drawings, time studies during production runs, value stream mapping, and …

Largest 2-Regular Subgraphs In Complete S-Partite Graphs, Yiyang Jiang

McKelvey School of Engineering Graduate Student Theses & Dissertations

In this thesis, we focus on the class of complete $S$-partite graphs, for $S$ an undirected graph possibly with self-loops, and address the problem of finding largest $2$-regular subgraphs of these graphs, which can be formulated as an integer linear program. Roughly speaking, a complete $S$-partite graph is obtained by replacing every single node of $S$ with a number of nodes, preserving the edge/non-edge relations of $S$. Our motivation in studying largest $2$-regular subgraphs is rooted in the structural systems theory, particularly in the problem of finding largest subnetworks that can sustain controllability or asymptotic stability of the corresponding subsystems. …

A Multi-Objective Optimization Framework For Equitable Stormwater Management In Urbanizing Rural Communities, Harry L. Wilson

Industrial Engineering Undergraduate Honors Theses

Due to limited technical and financial resources, urbanizing rural communities often face growing stormwater management challenges while undergoing rapid development. This thesis proposes a mixed-integer linear programming (MILP) framework that integrates topography-driven stormwater flow behavior, infrastructure placement constraints, and multiple planning objectives to support cost-effective stormwater infrastructure decisions. Our model accounts for budget constraints, gravity-driven surface water flow, infiltration capacity, and spatial contiguity requirements to determine optimal pond placements that balance flood reduction and implementation costs. A synthetic discretized grid representing a small municipality is used to demonstrate model behavior under varying rainfall and topographic conditions. Results demonstrate the framework's …

Multi-City Travel Routing Tool: Reducing Travel Costs And Time Spent Planning Using Apis, Trey R. Merreighn

Industrial Engineering Undergraduate Honors Theses

Travel planning is a time-consuming and ever-changing problem that can diminish the travel experience and greatly increase expenditure, if not done correctly. It is important to have an easy travel planning experience so you can enjoy the travel experience more and not waste time where it is not needed. This thesis aims to minimize the costs and time spent on travel planning using APIs and simple optimization models, creating a travel planning tool. This travel planning tool was developed in Java with the main API being Amadeus, this was combined with a greedy best-permutation heuristic to create the main route …

A Forecasting Framework For Distribution Center Capacity Utilization: An Applied Industry Study, Jordan J. Shortt

Data Science Undergraduate Honors Theses

This project develops and evaluates a predictive modeling framework for forecasting distribution center capacity utilization at Company Y, with monthly forecast horizons up to one year. Motivated by the operational challenges of seasonal demand volatility, promotional cycles, and the absence of a formally defined capacity metric, the study first constructs a historical capacity utilization measure from raw warehouse management system data — reconciling item volumes, location dimensions, and utilization factors across all DCs — which serves as the target variable for all modeling work. Four models are developed and evaluated against a naïve seasonal baseline: SARIMA, LightGBM, LSTM, and a …

Optimizing College Food Pantry Locker Systems Through Simulation Techniques, Jacob W. Holmes

Industrial Engineering Undergraduate Honors Theses

Food insecurity affects 10.5% of households in the United States. Among those affected, college students are a group with increasing food insecurity. Through novel food pantry order methods, such as locker order systems, some effects of college food insecurity can be alleviated. The Jane B. Gearhart Full Circle Food Pantry (FCFP) at the University of Arkansas implemented a locker system, beginning in the Fall of 2020. There is a lack of investigation into locker management policies for pantries, such as how much time clients should be allowed to pick up orders after they are placed in lockers, how many lockers …

Decision Making For Large-Scale Problems Under Uncertainty And Conflict, Benjamin J. Hamlin

All Dissertations

Large-scale decision-making problems appear in many areas including long-range forecasting such as energy generation forecasting. Many such problems are subject to conflicting objectives and uncertain data, and can be modeled as linear optimization problems. We study novel theoretical results and algorithms for large-scale linear decision problems under conflict and uncertainty. First, we propose a parametric Benders decomposition algorithm for solving large-scale linear optimization problems with multiple objectives or deterministically uncertain objectives. Second, we extend the parametric Benders decomposition to a multi-stage setting, developing a parametric stochastic dual dynamic programming algorithm, which enables decision-making when conflicts and uncertainty have planning impacts …

Optimal Allocation Of Flexible Servers In Healthcare Systems, Tong Zhang

All Dissertations

This dissertation investigates the optimization of worker allocation in healthcare systems, focusing on flexible staffing models and the strategic prioritization of healthcare tasks. This research explores the dynamics among pre-operative, operative, and post-operative care units under various cost and service-rate constraints, using a series of models that represent realistic healthcare scenarios within a comprehensive framework for improving patient flow and reducing waiting costs.

In Chapter 2, we model and analyze cross-trained nurse allocation policies within an inpatient surgical system. We model the surgical system as a tandem clearing queueing system and formulate Markov decision processes under different business rules governing …

Improving Transfer Portal Decision-Making Through A Microsoft Excel Optimization Model, Peyton Steffes

Honors Projects

The creation of the transfer portal has increased the mobility of collegiate athletes, giving players the autonomy to switch teams throughout their career. As a result, the roster development process has become more complex, as coaches are tasked with the challenge of recruiting from the transfer portal, which involves an extensive decision-making process. Coaches not only have to evaluate a substantial pool of players, but they also must consider the following constraints they are under: scholarship budget, roster spots available, and positional needs. To simplify this decision-making process, this analysis includes the results of a Microsoft Excel data optimization model …

Optimization Of Vehicle Mix For Three-And Four-Wheel Passenger Transportation System In Nigeria, Omotunde A. Muyiwa, Hamid A. Jimoh, Kolawole T. Oriolowo

Tanzania Journal of Engineering and Technology (TJET)

Efficient transportation systems are essential for reducing passengers waiting times in densely populated areas. However, there is a dearth of information on the optimization of vehicle mix for three-and four-wheel transportation systems in Nigeria. The present study was designed to optimize the vehicle mix for three-and four-wheel passenger transportation to improve transportation efficiency. Twenty purposively selected vehicles were observed, while a queuing model (M/M/s) was used to determine passenger arrival rates during different periods (morning, afternoon, and evening). An integer linear programming model was developed to minimize passenger waiting times. Data on vehicle types, capacities, travel cycles, and average passenger …

Ai-Scm Cmm: A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines, Omar F. El-Gayar, Patti Brooks, Insu Park

Annual Research Symposium

Artificial intelligence is increasingly deployed in supply chain management, yet many organizations struggle to align adoption efforts with process readiness, data quality, governance, and workforce capabilities, and they still lack validated supply chain specific roadmap for assessing readiness, sequencing investments, and reducing implementation risk. This study develops and evaluates a Capability Maturity Model for Artificial Intelligence Integration in Supply Chain Management to address that gap. Using a design science research approach, the study synthesizes prior literature and practitioner knowledge to define maturity dimensions, capability indicators, and staged progression levels for AI integration in supply chain contexts. The artifact and assessment …

Analogy2kg: An Automatic Pipeline For Deriving Knowledge Graphs From Long-Text Analogies, Kara Combs, Lance E. Champagne, Bruce A. Cox, Christine M. Schubert Kabban, Trevor Bihl, Grace Lemming

Faculty Publications

Analogical reasoning is an increasingly popular, lightweight solution to enable large language model (LLM)-level reasoning without computational complexity. Still, it has yet to be adopted due to its reliance on strictly hand-formatted data. Therefore, we propose Analogy2KG (“Analogy to Knowledge Graph”), as an automatic pipeline that transforms text into a KG format via a fine-tuned version of information extraction (IE) algorithms for long-text analogies. The need to verify that the complex underlying analogical structure of the data is maintained was done via paired samples tests in the creation and validation of this pipeline. Graph density was used to evaluate the …

Multi-Objective Optimization Of Waste Incineration For Minimal Carbon Monoxide And Sulfur Dioxide Emission And Rate Maximization: A Case Study Of Mbezi, Mkuranga-Pwani, Tanzania, Grangay M. Nyanghura, Enock W. Nshama

Tanzania Journal of Science

Incineration is widely employed for hazardous waste disposal, but results in harmful flue gas emissions. This study optimizes a double-chamber incineration process to reduce sulfur dioxide (SO2) and carbon monoxide (CO) emissions while maximizing the incineration rate. The effects of waste mass, primary chamber temperature (PT), and secondary chamber temperature (ST) were analyzed using a full factorial design of 27 experiments. ANOVA revealed that mass had the greatest impact on emissions and incineration time, ST had a moderate effect, and PT had little influence. Regression analysis provided models for incineration time, CO, and SO2 emissions. Single-objective optimization using sequential quadratic …

A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines

Dissertations

Artificial Intelligence (AI) is transforming Supply Chain Management (SCM), yet many organizations struggle to assess their readiness for AI adoption and to understand how AI capabilities develop across maturity stages. This dissertation addresses this gap by developing a Capability Maturity Model (CMM) for AI integration in SCM, grounded in Organizational Information Processing Theory (OIPT), the Resource-Based View, and related capability frameworks. The model provides a structured approach for evaluating an organization's information-processing requirements, resource configurations, and alignment needed for effective AI-enabled supply chain operations.

Using a design science research approach, the AI-SCM CMM and its associated assessment instrument were derived …

Optimal Placement Of Electric Vehicle Chargers: A Mixed-Integer Linear Programming Model, Joubin Zahiri Khameneh, Emmanuel Fagbenle

Faculty Publications

Electric vehicle adoption is growing, but New Hampshire lags in public charging infrastructure, especially in rural areas. This gap increases range anxiety and economic inefficiencies. In this study, we developed a mixed-integer linear programming (MILP) model to optimally locate new electric vehicle chargers statewide, maximizing coverage and equity under budget constraints. The model includes geographic coverage requirements, population-weighted equity, capacity limits, and a $28 million budget. Moreover, the model recommends 855 Level 2 chargers and 149 Direct Current Fast Chargers (DCFCs) across 247 ZIP Codes, nearly doubling public charging capacity and achieving 98.8% coverage within defined service radii. The plan …

Bridging Mission And Execution: Integrating Participatory Design In Early-Phase Mission Engineering For Stakeholder Alignment And Mission Clarity, Rafi Soule

Knowledge and Creativity Expo

This research examines mission framing during the early phase of Mission Engineering. Stakeholder interpretations diverge under ambiguity. Interoperability constraints are often not surfaced early. These conditions reduce mission clarity and weaken mission-to-system mapping readiness. The study integrates a participatory design-inspired, artifact-first workflow with RAG-enabled retrieval from a closed corpus to support evidence-grounded reasoning and traceable citations.

Phase 1 uses an online survey to establish baseline patterns in practice (N = 86). Shared understanding is positively associated with mission clarity (r = 0.60, p < 0.001). Phase 2 uses a time-bounded comparative workshop with two conditions. Expert reviewers rate mission statement quality higher for the participatory design condition (mean 3.5) than the traditional condition (mean 2.8). Technical feasibility ratings are similar across conditions. Phase 3 demonstrates RAG-enabled, closed-corpus, retrieval-supported traceability using the Referencer tool. It is reported as a proof-of-concept for evidence-grounded rationale and auditability, and as a pathway …

Gpu-Accelerated Biased Random-Key Genetic Algorithms: Framework Optimization And Llm-Driven Configuration, Fnu Harishjitu Saseendran

Open Access Master's Theses

This thesis presents two complementary contributions to the field of GPU-accelerated evolutionary metaheuristics for combinatorial optimization, organized in manuscript format.

The first manuscript, “BrkgaCuda 3.0: A Redesigned Multi-GPU Framework for Biased Random-Key Genetic Algorithms,” presents a ground-up architectural redesign of BrkgaCuda 2.0 that enables a true multi-GPU island model for Biased Random-Key Genetic Algorithms (BRKGA). The BRKGA island model evolves multiple semi-independent populations that periodically exchange elite solutions, a structure that maps naturally to multi-GPU parallelism; however, BrkgaCuda 2.0 is confined to a single GPU. BrkgaCuda 3.0 introduces an IslandManager that distributes populations across any number of GPUs, with multiple …

Business Process Redesign For Reducing Undelivered Product Return Losses In E-Commerce – An Explainable Ai Approach, Venkataraghavan Krishnaswamy, Deepa R, Himanshu Sharma

Journal of International Technology and Information Management

Product returns in e-commerce affect the profitability of the e-tailer. We adopt a two-stage approach to reduce undelivered product returns in an e-commerce firm. First, we develop and compare machine learning techniques—logistic regression, decision trees, Naïve Bayes, random forest, adaptive boosting, gradient boosting, stochastic gradient boosting, and deep neural networks—on their ability to predict undelivered returns. Next, we use explainable methods, such as relative importance and Shapley values, to develop insights from the best-performing machine learning model. Finally, we use these insights and the predictive model to redesign the firm’s order fulfillment and return processes. A Post-implementation evaluation of the …