Operational Research Commons^™

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. …

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 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 …

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 …

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 …

Towards Developing A Career Technology Fit Framework And Analyzing Its Influence On Work-Related Outcomes Among It Professionals, Gunjan Tomer

Journal of International Technology and Information Management

With growing attrition rate and significant demand for skilled IT professionals, the importance of studying their behaviour has become important for both academia and industry. Despite ample amount of research, there is still a gap between theory and practice. Based on our qualitative study conducted on Indian IT professionals we propose that technology allocation might contribute in understanding the behaviour of IT professionals. We found that IT professionals evaluate the technology allocated to them based on their individual career motives. This evaluation, either positive or negative, influences their job outcomes. Further, we explored the factors that make a technology preferable …

High Technology And The Developing State: The Arrival Of Supercomputers In India, Ramesh Subramanian

Journal of International Technology and Information Management

While India has made vast strides in information technology in the last few decades, its success is mainly attributed to its software, rather than its hardware sector. In fact, India’s attempts at developing computer hardware that can match international standards have largely been unsuccessful. A notable exception is its development of a series of supercomputers that match and exceed many international standards. This paper looks at an interesting period in India’s computing history – namely the 1980s and 1990s – focusing on its development of an indigenous supercomputer. During that period, supercomputers were thought to be the sole privy of …

Responsible People Analytics For Remote-Work Decisions: A Machine-Learning Benchmark For Classifying Perceived Productivity, Ruth Menjivar, Nima Molavi, Narges Mashhadi Nejad

Journal of International Technology and Information Management

This study examines whether employee-perception survey data can support responsible people-analytics decisions about remote-work productivity. Using the public New South Wales (NSW) Remote Working Survey 2021 (N=1,512), the study benchmarks statistical and machine-learning classifiers for self-reported perceived productivity classes (same, less, or more productive when working remotely relative to onsite work), not objective output, under default, class-weighted, and resampling protocols. Main evidence comes from 5×5 repeated stratified cross-validation using macro F1 and balanced accuracy with fixed model specifications. Class-balanced separability is modest. Random Forest, CatBoost, and LightGBM form a leading cluster with overlapping confidence intervals (macro F1 ≈0.51–0.52). Affective/well-being items, …