Operations Research, Systems Engineering and Industrial Engineering Commons^™

Evolving Ai Integration In Complex Medical Decision-Making And Multidisciplinary Transplant Care: A Systematic Review Of Human-Ai Collaboration, Rachel L. Dzieran, Cihan H. Dagli, Robert J. Marley

Engineering Management and Systems Engineering Faculty Research & Creative Works

Purpose of Review: Artificial intelligence (AI) in healthcare has evolved dramatically from early expert systems, which were initially considered replacements for clinical judgment, to today's collaborative frameworks that aim to augment physician decision-making. This evolution is particularly crucial in domains such as transplant surgery, where decisions carry irreversible consequences and require the integration of complex, often ambiguous data. Drawing on peer-reviewed literature from 2019 to 2025, we conducted a systematic review that analyzed key elements distinguishing successful human-AI partnerships from those that fail. Recent Findings: The ideal balance incorporates human expertise into AI systems through weighted integration approaches, rather than …

Modeling Preannouncement And Launch Timing Decisions For Product Line Extension Under Innovation Uncertainty And Resource-Sharing Dual-Sourcing Supply Chain, Adewole Adegbola, Venkat Allada

Engineering Management and Systems Engineering Faculty Research & Creative Works

This research seeks to develop a system dynamics (SD) model to address challenges associated with the product line extension (PLE) problem. In this work, we specifically consider the "Two-Generation Product Line Extension (TGPLE) Problem", where a firm introduces a product variant in the market and plans to extend the launch to include a newer generation product in a resource-sharing dual-sourcing (RSDS) environment. We begin by identifying the key factors that influence product line extension, and we developed a TGPLE-RSDS construct based on three (3) inter-related systems: Market System, Production System and Supply Chain System. The proposed TGPLE-RSDS construct also illustrates …

Retraction Notice To "Hydrogen Recovery, Cleaning, Compression, Storage, Dispensing, Distribution System And End-Uses On The University Campus From Combined Heat, Hydrogen And Power System" [International Journal Of Hydrogen Energy 39 (2014) 647-653], Tarek A. Hamad, Abdulhakim A. Agll, Yousif M. Hamad, Sushrut Bapat, Mathew Thomas, Kevin B. Martin, John W. Sheffield

Engineering Management and Systems Engineering Faculty Research & Creative Works

This article has been retracted: please see Elsevier policy on Article Correction, Retraction and Removal (https://www.elsevier.com/about/policies-and-standards/article-withdrawal). This article has been retracted at the request of the Editor.

Thermal Monitoring Of Solidification In Molded Casting Of Drone Landing Leg, Sadettin Cem Altıparmak, Maher Zayed, Samuel Hawk, Mohamed Baddi, Jesus Degollado, Ahmed Bendaouia, Jianzhi Li

Manufacturing & Industrial Engineering Faculty Publications

This paper presents the design, fabrication, and thermal characterization of an acrylonitrile butadiene styrene (ABS) mold produced by fused deposition modeling (FDM) extrusion-based additive manufacturing used for the wax casting of a drone landing leg. A thermal monitoring framework, consisting of four K-type thermocouples logged at 1 s intervals, was deployed across six casting trials to capture the complete solidification cycle, yielding 84,717 time-stamped measurements. The study focuses on thermal data analysis and materials science interpretation through descriptive statistics, normality diagnostics, inter-trial Kruskal–Wallis tests, phase-duration analysis, rolling-variance monitoring, and inter-channel Pearson correlation. Key findings include a persistent spatial temperature hierarchy …

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 …

Individualized Bayesian Inference Identifies Novel Genetic Variants For Parkinson's Disease, Jin Ren, Yasaman J. Soofi, Md Asad Rahman, Qing Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Parkinson's disease (PD) is a complex neurodegenerative disorder with a significant genetic component. While genome-wide association studies (GWAS) have been instrumental in identifying genetic variants associated with PD, the reliance on large sample sizes and population-level analyses may overlook variants with lower minor allele frequencies or individual-specific relevance. Individualized Bayesian Inference (IBI) offers a promising method to complement GWAS by identifying and prioritizing candidate genetic markers at both the individual and patients-like-me subgroup levels. This study evaluates the application of IBI to PD genetics, using GWAS as a baseline for comparison. We analyzed genetic data from the Fox Insight online …

Engineering For Compromise, Ron Ross

Other Faculty Materials

Modern systems cannot credibly depend on every component, account, service, person, supplier, and supporting dependency remaining trustworthy secure throughout the system life cycle. Prevention-centered cybersecurity remains essential, but for mission-critical systems it is not a sufficient engineering strategy. This paper introduces the concept of Engineering for Compromise (EfC), a systems security engineering strategy that deliberately assumes selected system elements may fail, become malicious, lose integrity, or fall under adversary control and then asks whether the system can continue to prevent unacceptable loss. EfC separates component compromise, system compromise, and mission loss as distinct events and uses architecture to place barriers …

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.

Balancing War And Peace In Taekwondo Athlete Development: A Multi-Criteria Coaching Decision Support Framework Using Leadership Scale For Sports And Fuzzy Ahp, King Harold A. Recto, Hazel Jade L. Antonio, Jhyrald Anthony P. Dalida

Electronics, Computer, and Communications Engineering Faculty Publications

Coaching effectiveness is frequently evaluated through competitive outcomes, with the assumption that coaching behaviors directly influence athlete performance. This study examined the relationship between perceived coaching behaviors and individual win percentage among collegiate Taekwondo athletes using the Leadership Scale for Sports (LSS). Descriptive statistics, correlation analysis, and regression analysis were conducted across five coaching dimensions: Training and Instruction, Democratic Behavior, Autocratic Behavior, Social Support, and Positive Feedback. Results showed that none of the coaching dimensions demonstrated a statistically significant relationship with win percentage. Although coaching behaviors did not significantly predict win percentage, the findings suggest that athlete development is shaped …

Constrained Assortment Optimization Under The Mixed-Logit Model, Hoang Giang Pham, Tien Mai

Research Collection School Of Computing and Information Systems

In this paper, we study the assortment optimization problem under the mixed-logit customer choice model. While assortment optimization has been a central topic in revenue management for decades, the mixed-logit model is widely regarded as one of the most general and flexible frameworks for modeling and predicting customer purchasing behavior. The assortment optimization problem is known to be NP-hard to be approximated to any constant factor, even in the unconstrained case. To address this challenge, we first explore the submodularity properties of a simplified version of the objective function to derive novel semi-constant factor approximation solutions for assortment problems under …

A System Dynamics Model For Analyzing Customer Satisfaction Drivers In A Manufacturing Context, Mahnaz Asgari Sooran, Venkat Allada, Adewole Adegbola

Engineering Management and Systems Engineering Faculty Research & Creative Works

Customer satisfaction offers economic benefits and competitive advantage to manufacturing enterprises. However, a gap exists in understanding how to achieve customer satisfaction due to the complexities involved in coordinating various subsystems of an enterprise in an efficient manner. In this paper, we identified four internal and external-to-the-firm customer satisfaction drivers which include: government policies, supply chain reliability, knowledge management, and manufacturing performance criteria such as on-time delivery, quality and cost. We then developed a conceptual framework to define the non-linear mathematical relationships between drivers using system dynamics modeling. We conducted "what-if "scenario analyses for a base case, optimistic case and …

Robust Generator Maintenance Schedule For Frequency-Secure Power Systems, Yang Yang, Qiuzhuang Sun, Jimmy Chih-Hsien Peng, Loon Ching Tang, Zhisheng Ye

Research Collection College of Integrative Studies

Problem definition: Normal operations of a power system require that alternating current frequency be maintained at a nominal value, for example, 50 Hz, whereas severe deviation from this value due to power deficiencies can cause cascading generator trips. Maintaining the frequency requires adequate inertia and frequency regulation reserve, which are primarily provided by online generators. In daily operations, generators due for preventive maintenance must be taken offline, and thus an improper maintenance schedule could jeopardize frequency security, as exemplified by the recent Texas power blackout. However, this natural nexus between frequency security and maintenance has been over-looked largely in the …

Online Planning Of Power Flows For Power Systems Against Bushfires Using Spatial Context, Jianyu Xu, Qiuzhuang Sun, Yang Yang, Huadong Mo, Daoyi Dong

Research Collection College of Integrative Studies

A power station or transmission line can be affected due to bushfires, increasing operation costs. We study a fundamental but challenging problem of planning the optimal power flow (OPF) for power systems under bushfires. We develop a model to capture the stochastic nature of bushfire spread based on Moore’s neighborhood model and propose an online optimization modeling framework to sequentially plan power flows in the electricity network. Our framework assumes that bushfire spread is non-stationary over time and that the spread and containment probabilities are unknown. To address these challenges, we develop a contextual online learning algorithm that treats the …

Late-Night And Early-Morning Train Scheduling With Non-Traffic Hour Maintenance Window In Urban Rail Transit Systems, Yaochen Ma, Hai Yang, Hai Wang

Research Collection School Of Computing and Information Systems

Regular maintenance during non-traffic hours (NTH) is vital for the resilience of urban rail transit (URT) systems, yet an insufficient NTH maintenance window poses a challenge for URT systems in various cities. For instance, the Hong Kong MTR Corporation has noted that the required NTH maintenance time often exceeds the available window, prompting service adjustments such as earlier late-night closures and/or later early-morning starts. To address this challenge, this study develops an optimal scheduling framework that links late-night and early-morning URT services through the NTH maintenance window requirement to maximize public welfare. A Decoupled Optimization Model (DOM) first derives closed-form …

Integrating Production And Repair Operations Through Data Driven Defect Flow Analytics For Yield And Resource Optimization In Pcba Manufacturing, Ali Tahmasebi, Yingge Zhou, Daryl Santos, Sai Srikanth Reddy Kolli, Edward Wong

Systems Science and Industrial Engineering Student Scholarship

In Printed Circuit Board Assembly (PCBA) manufacturing, maintaining a closed loop connection between production and repair operations is critical to improving product quality and operational efficiency. However, in the manufacturing environment that motivated this work, production and repair data are often managed separately, and repair findings are not systematically fed back to production teams, limiting visibility into defect routing patterns and repair workload drivers. This study developed a data-driven framework that integrates production and repair data streams to analyze defect flow and support repair resource planning. The framework applies descriptive statistics, cross tabulations of repair inflow by product/project and repair …

Hydpn: A Hybrid Deep Reinforcement Learning, Programming, And Neighborhood Operations Framework For Integrated Scheduling On Parallel Batch Processing Machines, Yuqi Wang, He Luo, Guoqiang Wang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Batch processing machines (BPMs) are widely used in industries such as semiconductors, metal processing, and healthcare, where jobs are processed in batches. As production, inventory, and distribution become increasingly integrated to improve efficiency, research on their joint scheduling in parallel BPM environments remains scarce. This paper addresses the integrated scheduling problem in parallel BPMs, involving production, inventory, and distribution stages, with the objective of minimizing total costs. A unified cost-based model is first formulated, applicable to both in-facility and external distribution scenarios. A hybrid algorithm framework, HyDPN, combining deep reinforcement learning, dynamic programming, and neighborhood operations is proposed. Extensive experiments …

A Novel Hexagonal-Zigzag Cellular Infill Structure For Additive Manufacturing, Md. Saidur R Roney, Amm Nazmul Ahsan, Prosenjit Barua

Manufacturing & Industrial Engineering Faculty Publications

The rigidity of the Additively Manufactured objects can be tailored by manipulating the infill lattice type and density. In this research, an island type novel infill structure termed as Hexagonal-Zigzag pattern is introduced, and its mechanical performance is investigated. In this pattern, the zigzag raster reflects the repeating hexagonal shaped cell constituting the parallel-oriented islands and 90° rotation of the pattern in each layer distributes the island span along both transverse and longitudinal directions of the printing contour. A mathematical model is established to illustrate the effect of the infill parameters on hexagon unit cell size and relative infill density. …

Extensive And Intensive Margin Labor Supply On Ride-Sourcing Platforms, Hao Sun, Hai Wang, Zhixi Wan

Research Collection School Of Computing and Information Systems

The rapid expansion of ride-sourcing platforms has enabled freelance drivers to flexibly determine both their participation and working hours. Understanding this flexible labor supply behavior is essential for managing platform capacity and evaluating the impacts of pricing and incentive policies on driver welfare. This study develops a labor supply model in which drivers optimally choose whether to participate (extensive margin) and how long to work (intensive margin) to maximize their utility from consumption and leisure. The model incorporates heterogeneity in drivers’ other income, idle time, and participation costs, allowing us to analytically characterize equilibrium labor supply decisions. The results show …

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 …

Investigation Of Process Parameters To Fabricate Tiwmo Refractory Medium Entropy Alloy Via Laser Powder Bed Fusion, Abdullah Al Masum Jabir, Lindsey A. Salazar, Jianzhi Li

Manufacturing & Industrial Engineering Faculty Publications

This paper presents an experimental study on the fabrication of a TiWMo refractory medium-entropy alloy (RMEA) using laser powder bed fusion (PBF-LB/M, commonly known as selective laser melting) from elemental powders as well as successful alloy formation on titanium substrates. The effects of tungsten particle size and process parameters on successful TiWMo RMEA fabrication have been explored using scanning electron microscopy (SEM), x-ray diffraction, hardness measurement and microstructural analysis. Scanning electron microscope (SEM) analysis revealed that the lowest percentage (0.01%) of unmelted tungsten particles was observed at a laser power of 350 W and scanning speed of 250 mm/s, particularly …

Long-Term Mine Planning: A Survey Of Classical, Hybrid And Artificial Intelligence-Based Methods, Nurul Asyikeen Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi

Research Collection School Of Computing and Information Systems

The aim of long-term mine planning (LTMP) is two-fold: to maximize the net present value of profits (NPV) and determine how ores are sequentially processed over the lifetime. This scheduling task is computationally complex as it is rife with variables, constraints, periods, uncertainties, and unique operations. In this paper, we present trends in the literature in the recent decade. One trend is the shift from deterministic toward stochastic problems as they reflect real-world complexities. A complexity of growing concern is also in sustainable mine planning. Another trend is the shift from traditional operational research solutions — relying on exact or …

To Wait Or To Transfer? A Three-Level Optimization Framework For Intermodal Transfer Coordination In First Train Timetabling And Bus Bridging Services Management, Hao Li, Liujiang Kang, Norman Weik, Huijun Sun, Qingying Lai, Zhiguang Cao

Research Collection School Of Computing and Information Systems

This study addresses the integrated optimization of the first train timetabling and bus bridging service design (FTT-BBSD) for morning transfer challenges, two critical but interdependent passenger services in the public transit system. In contrast to most existing studies and conventional approaches, this study explicitly models the influence of passenger path choices and transfer mode selections on FTT-BBSD. Through a novel dual-level network representation that integrates subway and bus systems, we formulate the FTT-BBSD problem as a mixed-integer nonlinear programming model. The model simultaneously determines subway and bus timetables and bridging line deployment to minimize total travel time for all first …

Bridging Images And Language In Radiology: A Comprehensive Prisma Systematic Review Of Transformer Vision-Language Models And Clinical Readiness, Mohammad T. Khasawneh Dr., Sadaf Tabatabaee

Systems Science and Industrial Engineering Student Scholarship

Transformer vision-language models (VLMs) promise end-to-end automation of radiology reporting and related multimodal tasks. However, evidence remains fragmented across datasets, architectures, evaluation practices, and levels of clinical validation, limiting fair comparison and safe translation into practice. Following PRISMA 2020/PRISMA-S, search engines including PubMed, IEEE Xplore, Web of Science, and Google Scholar were systematically searched for peer-reviewed, English-language studies published between 2019 and 2025 that used paired radiology images and free-text reports. Dual reviewers screened records and extracted data using a locked schema covering datasets, modalities, architectures, training objectives, evaluation metrics, and indicators of clinical readiness. Free-text model descriptions were normalized …

Data Driven Estimation Of Pore Size Using 1d Light Emissions For Laser Powder Bed Fusion Additive Manufacturing, Jose Galarza, Jorge Barron, Luis Jimenez, Tamer Oraby, Jianzhi Li, Farid Ahmed

Manufacturing & Industrial Engineering Faculty Publications

The quality assurance of the Laser Powder Bed Fusion Process (LPBF) has been extensively investigated over the last decade for in-situ monitoring of metal additive manufacturing. The process inherently generates voids within the bulk of the part, which can detrimentally affect the quality of the printed part. The characterization of these voids by estimating their size and identifying their geometrical features remains a challenge. This study introduces a Machine Learning (ML) based framework for estimating void sizes of varying geometries using layer-wise one-dimensional (1D) average light intensity signal obtained from the optical tomography system during the 3D printing of metallic …

Beyond Accuracy: Machine Learning Models For Predicting Presence Of Permanent Molar Caries In U.S. Children And Adolescents With Fairness Consideration, Pritam Deb, Lin Li, Christina R. Scherrer

Faculty Articles

Background

Although predictors of dental caries have been previously explored, a comprehensive understanding of factors influencing permanent‐molar decay in U.S. children and adolescents, especially with respect to racial and ethnic biases remains limited. This study aims to develop and evaluate machine‐learning (ML) models incorporating algorithmic fairness to predict caries in permanent molars.

Methods

Data from the National Health and Nutrition Examination Survey (NHANES) were analyzed, using the 2011–2014 cycles for training and validation and the 2015–2016 cycle for testing. The primary outcome was decayed, missing, and filled teeth (DMFT) in at least one permanent molar, dichotomized to represent the presence …

Modeling Individual Self-Protective Behavior During Epidemics, Geonsik Yu, Michael J. Garee, Mario Ventresca, Yuehwern Yih

Faculty Publications

Protecting public health from infectious diseases requires collective action, as individual behaviors—such as vaccination and mask-wearing—directly influence disease dynamics. During the COVID-19 pandemic, unexpected public responses often undermined the effectiveness of interventions, highlighting the need to understand collective behavioral patterns and motivations to design more effective mitigation strategies. This study presents an agent-based simulation model that captures how individuals adjust self-protective behaviors based on evolving opinions about disease risk and examines how these decisions interact with external factors, such as public health interventions, to shape collective outcomes. To improve the representativeness of the simulated population, multiple datasets were integrated to …

Blockchain For Large Language Models (Llms): Applications, Challenges, And Framework Implementation, Ahmad Musamih, Ibrar Yaqoob, Khaled Salah, Raja Jayaraman, Mohammed Omar

Industrial Engineering: Faculty & Staff Publications

Large Language Models (LLMs) are increasingly embedded in intelligent systems across domains such as healthcare, finance, and smart infrastructure. However, their reliance on centralized data pipelines raises unresolved challenges concerning provenance, accountability, and verifiable trust. As the demand for transparent and regulation-aligned AI grows, these challenges have become central to the responsible deployment of intelligent systems. This review examines how blockchain technology can address them by introducing decentralized integrity, immutable audit trails, and cryptographic verification into the LLM lifecycle. Through a structured synthesis of current research, we identify conceptual and architectural gaps that limit trustworthy data management, inference authentication, and …

Factors Affecting Pedestrian-Vehicle Conflicts: An Empirical Analysis, Christo D. Jamo

Honors Scholar Theses

The number of pedestrian deaths increased by 78% between 2009 and 2023, while other motor vehicle crash deaths increased by 13% in the same period [1]. To identify potential pedestrian safety measures, this study analyzed the effects of location-based demographics, pedestrian phasing type, and other physical infrastructure and behavior variables on the probability of pedestrian-vehicle conflicts at signalized intersections, which is a surrogate measure of crash risk. Data were collected from 55 intersections in Connecticut, and the pedestrian-vehicle interactions were classified by severity based on the Swedish Traffic Conflict Technique: undisturbed passage, potential conflict, minor conflict, or serious conflict. Because …

Technology Based Assessment Of Workplace Biomechanical Risk: A Roadmap For Process Improvement, Tamer Adel Mohamed, Noha Mostafa, Ahmed Elsayed

Mechanical Engineering

The main objective of this paper is to develop a methodology for assessing the biomechanical risk in the workplace and design and apply improvements to reduce this risk by redesigning the working process. The risks are measured using Ergonomics tools to assess body posture and lifting index. Kinect sensor and video recordings are utilized to evaluate the joints' positions as an input for body posture data. Then, the specialized CATIA V5 software [26] is used to simulate the workers' bodies in the form of 3D models. Risk factors were assessed, and a new design was developed to be implemented in …

Dynamic Cushioning Performance And Sustainability Implications Of Recycled-Content Expanded Polyethylene Foams, Jay Singh, Pratish Patel, Emily Recinos, Paulina Goncharov

Industrial Technology and Packaging

The transition toward circular materials in protective packaging requires empirical evidence that recycled-content foams can satisfy stringent shock-attenuation requirements. This study evaluates the dynamic cushioning performance of expanded polyethylene (EPE) foams formulated with 0%, 30% and 40% recycled content under controlled impact conditions following ASTM D1596. A total of 1100 drop tests were conducted across combinations of density, thickness, static load and drop height representative of distribution hazards in parcel and freight supply chains. Peak transmitted acceleration (G) was measured as the primary performance response variable.

Across the configurations evaluated, recycled-content EPE foams exhibited cushioning performance comparable to virgin materials. …