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 …

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 …

Challenges And Best Practices Of Regional Innovation Ecosystems, Mahnaz Asgari Sooran

Miners Solving for Tomorrow Research Conference

Many places around the world are developing regional innovation ecosystems to spur regional economic. Many innovation ecosystems fail or barely maintain their initial momentum after a few years. In this paper, we identified challenges and best practices facing innovation ecosystems interviews with the innovation ecosystem stakeholders. We employed the MIT REAP (Regional Entrepreneurship Acceleration Program) model to categorize the five main stakeholders of the innovation ecosystem: entrepreneurs, universities, industry, risk capital, and government. Our initial results indicated the following: (a) Access to capital and talented workforce; (b) Stakeholder discovery process is one of the critical areas to understand the basic …

Preventive Maintenance Scheduling Using Artificial Intelligence And Decision Support Agent, Samiksha Aryal

Miners Solving for Tomorrow Research Conference

Preventive Maintenance models have traditionally relied on a time-based maintenance model, which uses fixed statistical distribution to represent the time-to-failure (TTF). The requirement of data-driven models and automated decision-making systems have become essential for modern manufacturing systems. The use of fixed statistical distribution limits the ability of existing models in producing solutions in real-time. Our model overcomes these limitations by implementing an empirical distribution to represent TTF. The empirical distribution is generated using a neural network which eliminates noise from the raw maintenance log. Renewal Reward Theorem (RRT) is implemented to efficiently provide maintenance threshold in real time. The use …

Empirical Evaluation Of Policy-Based Reinforcement Learning For Dynamic Service Control In An M/M/1 Queue, Joseph Walton

Miners Solving for Tomorrow Research Conference

While reinforcement learning has been increasingly applied to stochastic control, limited work examines policy-based methods in queuing environments modeled as semi-Markov decision processes (SMDP). This study investigates how policy-based reinforcement learning (RL) algorithms perform when applied to service rate control in an M/M/1 queue, a common queuing model for manufacturing and service systems. The problem is formulated as an SMDP in which decisions occur at each new service, allowing an agent to select different service rates from a finite set of speeds, aiming to minimize an objective function that manages system congestion and energy costs. Three policy-based reinforcement learning algorithms, …

A Speed-Adjusted Centipawn Metric For Chess Cheating Detection, Benjamin Sullins, Benjamin Biehl

Miners Solving for Tomorrow Research Conference

The proliferation of chess engines has compromised the integrity of online play through both manual assistance and automated bots. This research proposes Si, a novel metric designed to quantify unnatural play by integrating move latency, the relative strength of the selected move, and the density of high-quality alternatives available in a given position. By fitting Si  values to theoretical probability distributions across specific Elo ratings and time controls, we establish a statistical baseline for human performance. Discrepancies between an individual's Si  profile and these established distributions provide a robust framework for identifying artificially inflated play, offering a potential method for …

Ai Adoption Tensions For Organ Procurement Organizations, Joely Grace Hall

Miners Solving for Tomorrow Research Conference

Artificial intelligence (AI) has the potential to improve efficiency in healthcare, yet its adoption remains limited, with only 22% of healthcare organizations having implemented domain-specific AI tools. Adoption may be especially complex in specialized domains such as organ transplantation, where ethical, legal, and operational challenges are dominant. This study examined factors influencing AI acceptance within Organ Procurement Organizations (OPOs), focusing on technological, organizational, and environmental contexts.

Semi-structured interviews with 16 OPO executives from 10 OPOs revealed key tensions shaping AI adoption. We identified five tensions that are holding back OPO leaders from AI adoption, (1) misconceptions, (2) training approach, (3) …

Visual Pattern Mining With Similarity Metrics For Model-Free Trading In The Korean Futures Market, Juhyeon Jang, Jaeyun Kim, David Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

The fractal market hypothesis highlights multi-scale dynamics in financial time series and provides a theoretical foundation for pattern-based analysis. This study proposes a model-free visual pattern mining framework that transforms high-frequency market data into image representations to support intelligent decision-making. By converting 1-minute KOSPI200 futures data into candlestick chart and Bollinger band images, the method effectively captures structural patterns and volatility dynamics. The framework applies similarity metrics and Intersection over Union (IoU)-based visual comparison to identify historically similar patterns and generate intelligent trading signals without model training or complex parameter tuning. Experimental results demonstrate that combining visual features of candlestick …

Work-In-Progress: Evaluating Feasibility Of Band Matrix Solvers For Scaling Up Extreme Learning Machine Method, Anton Akusok, Kaj Mikael Björk, Amaury Lendasse, Leonardo Espinosa Leal

Engineering Management and Systems Engineering Faculty Research & Creative Works

This work presents the results of the potential of band linear system solvers for improving the scalability of the Extreme Learning Machine (ELM) method at large model sizes. The model is tested on the standard MNIST dataset with a range of solvers provided by the SciPy Python library. The results are analyzed taking into consideration the overall performance and the performance impact of band solvers across different matrix bandwidths, as well as the performance versus runtime analysis. The findings show potential in applying the proposed method to very large ELM models with narrow band matrices.

Optimal Slotting In Hybrid Warehousing For Industry 4.0, Teng Yang

Masters Theses

In the era of Industry 4.0, the warehouse management system (WMS) employed by many firms prescribes hybrid storage, i.e., products with high turnover, called fast movers, are kept in random storage for a short time duration before being shifted to a dedicated storage area, while products with low turnover, called slow movers, remain in random storage. From dedicated storage, the products are dispatched to the customer. The challenge for managers is selecting the slot in dedicated storage to assign to each product while demand data change because of fluctuating market conditions; this problem is referred to as slotting in the …

Developing Discharge Estimation Algorithm Using Low-Cost Velocity Sensor And Machine Learning, Barkha Gautam

Masters Theses

Accurate river discharge estimation is essential for flood forecasting, water resources management, and hydraulic decision-making; however, continuous discharge records are unavailable at many river locations. Traditional stage-discharge rating curves are widely used but their reliability may decrease when channel conditions change or flow conditions vary rapidly. This study develops and evaluates Long Short-Term Memory (LSTM) models for discharge prediction using 15-minute time-series data from river monitoring stations in Missouri. Two model configurations, a baseline stage-only model and an enhanced stage-plus-velocity model, are developed and evaluated independently at two river sites to determine whether the inclusion of surface velocity improves discharge …

Long Short-Term Memory (Lstm) -Based Neural Network Model For Optimizing Composite Manufacturing Process Using Autoclave, Sourav Bolar, Steven Corns, Nayan Pundhir, Kumbla Chandrashekhara

Engineering Management and Systems Engineering Faculty Research & Creative Works

Producing high-quality fiber-reinforced composites requires precise temperature control during autoclave curing, as even small variations can lead to defects that compromise strength and reliability. At the same time, manufacturers aim to reduce energy use and shorten curing cycles without sacrificing material performance. To address these challenges, this study develops a data-driven Long Short-Term Memory (LSTM) neural network model capable of forecasting temperature evolution inside the autoclave throughout the curing cycle. The model is trained on time-series temperature data collected from multiple sensing locations, enabling it to learn the spatial and temporal trends that govern heat flow during curing. Data augmentation …

Five Tensions Of Artificial Intelligence Adoption For Organ Allocation: Applying The Technology–Organization–Environment Framework, Amaneh Babaee, Daniel Burton Shank, Casey I. Canfield, Joely Grace Hall, Krista L. Lentine, Henry Randall, Mark Schnitzler

Psychological Science Faculty Research & Creative Works

Background: The US organ transplantation system is pursuing modernization of the allocation process through the integration of new technologies such as artificial intelligence (AI). However, the legal and ethical issues within the transplantation industry are still of concern. Objective: We explore the opportunities and challenges for Organ Procurement Organizations (OPOs) to adopt AI. The US organ transplant system is a highly regulated industry yet open to innovation. Methods: Ten structured interviews were conducted with OPO representatives using the Extended Technology, Organization, Environment (TOE) framework. Results: Overall, we identified five core tensions in AI adoption: (1) misconceptions, (2) approach to training, …

Sme Ai Outreach In Finland—A Case Study, Kaj Mikael Björk, Anton Akusok, Amaury Lendasse, Leonardo Espinosa-Leal

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper presents a project (work in progress) where entrepreneurship and higher education in AI (from Master level to postdoc level) are integrated in order to produce a dual effect; helping SMEs to gain insight in how AI can aid in the corporate environment and to expose AI researchers to the real-life situations in the company world. If successful, the companies are made ready for the AI revolution and the researchers more equipped for corporate settings. The project is ongoing, so this paper addresses a work-in-progress project. The paper reflects on the project as well on some aspects that need …

Application Of Natural Language Processing And Machine Learning For Analyzing Mining Accident Reports And Automating The Process Of Root Cause Analysis, Siddhartha Agarwal, Y. P. Chugh, Atul Singh, Vikram Sakinala, Ayan Mukherjee, Balbir Prasad, Cihan Dagli, Yuhao Zou

Engineering Management and Systems Engineering Faculty Research & Creative Works

Coal mining accidents are a major concern worldwide, necessitating effective safety measures and comprehensive analysis to prevent future accidents. Our proposed solution is the first attempt for Indian mines, inspired by the potential of Natural Language Processing (NLP) that can read and analyze vast repositories of accident records in seconds. In combination with machine learning (ML), NLP algorithms can extract unstructured text by eliminating manual data entry errors, reading poorly scanned reports, and understanding multiple versions of the event and cluster documents based on types that would otherwise take months to collate. In the case of accident records, it can …

Ibi-Dt: A Novel Approach Combining Individualized Bayesian Inference And Decision Tree For Identifying Cancer Drivers And Their Interactions, Md Asad Rahman, Gregory F. Cooper, Jinying Zhao, Xinghua Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Cancer is mainly caused by a relatively small portion of somatic genome alterations (SGAs), called cancer drivers. Despite success in identifying a good number of cancer drivers, many more remain to be discovered to explain various cancers. Moreover, limited tools are available to identify potential interactions among cancer drivers for a better understanding of oncogenesis. To tackle these challenges, we have developed a novel approach called individualized Bayesian inference using a decision tree (IBI-DT). IBI-DT recognizes the genetic heterogeneity among cancer patients, where different individuals or patient subgroups of distinct genomic makeup may have different drivers. IBI-DT works by constructing …

Understanding The Relationship Between Structural Failure And Fatalities In Tornadoes: A Quantitative Investigation Of The 2021 Midwest Tornado Outbreak, Yi Zhao, Ruwen Qin, John W. Van De Lindt, Justin Sharpe, Grace Yan

Engineering Management and Systems Engineering Faculty Research & Creative Works

During 1950-2011, the number of fatalities caused by tornadoes in the U.S. significantly exceeded the fatalities caused by both hurricanes and earthquakes. To reduce tornado induced fatalities, it is essential to understand how structures/building failures correlate with fatalities and who are more vulnerable to tornadoes. Insights from this study are intended to help provide information to decision-makers on where to allocate limited resources for enhancing tornado resilience. By examining both the fatality data and structural damage data in the 2021 Midwest Tornado Outbreak, the objective of this study is to examine the occurrence of fatalities during tornadoes across various types …

Supporting Academic Parents: The Effects Of Dependent Care Policies On Research Productivity Trends, Drake Van Egdom, Matthew M. Piszczek, Christiane Spitzmueller, Peggy Lindner, Aaron Clauset

Engineering Management and Systems Engineering Faculty Research & Creative Works

On average, women faculty take on more childcare responsibilities, posing barriers to career success. Work-family policies represent one solution for advancing gender equity in academia as they support parents after childbirth with benefits for children, employees, and organizations. We contribute to understanding how the availability and use of dependent care policies (paid parental leave and childcare benefits) relate to long-term research productivity trends. Based on the work-home resources model, we theorize that policy availability provides contextual resources and policy use provides personal resources, leading to improvements in an individual's research productivity after they have a child. We also examine potential …

Linear Mixed Model For The Surface Roughness Prediction In Hard Turning Operation, Prithbey Raj Dey, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Surface machining using hard turning is an intricate operation due to the influence of multiple machining parameters, their non-linear interactions, and the inherent variability introduced by different experimental trials. This study proposes a Linear Mixed Model (LMM) for predicting surface roughness, effectively addressing the challenges in traditional linear models, posed by the influencing factors, non-linearity, and interactions. The LMM incorporates variability from both fixed effects, such as cutting parameters (feed rate, depth of cut, and cutting speed), and random effects arising from tool wear across experimental runs. As a result, it provides a more comprehensive understanding of how these factors …

The Prospect Of Geospatial Analysis In The Prediction Of Surface Quality In Machining, Prithbey Raj Dey, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

This research underscores the prospect of geospatial analysis in machining operations to enhance precise prediction and robustness, offering a comprehensive framework of spatial modeling for advanced manufacturing processes. Geospatial analysis not only provides accurate predictions but also estimates the uncertainty associated with these predictions, offering valuable insights for process optimization. The surface quality in the machining processes is expressed by the estimation of the average surface roughness. While machining parameters are extensively analyzed for their influence on surface quality, the roughness profile parameters are inadequately explored. This work integrates these underexplored parameters into geospatial predictive models and evaluates their impact …

A System Of Systems (Sos) Meta-Architecture Approach To Design Digital Platform-Based Domestic Worker Distribution System, Prithbey Raj Dey, Cihan H. Dagli, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

In this research, a System of Systems (SoS) meta-architecture is conceptualized to design a digital platform-based system framework for the distribution of domestic workers to boost the crowd-sourced economy. While an Object Process Methodology (OPM) is used to articulate the relationships between the objects and functions, a Design Structure Matrix (DSM) has been applied to address the interactions between individual components to categorize them into subsystems to create the SoS architecture. This SoS architecture incorporates several Key Performance Attributes (KPAs) and Key Performance Parameters (KPPs) to systematically evaluate the meta-architectures. The Analytical Hierarchical Process (AHP), Pugh’s Evaluation Matrix, and Technique …

Blockchain-Based Ai-Assisted Cyber-Physical Systems For Robust And Reliable Machining Processes, Prithbey Raj Dey, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

This study demonstrates the prospects of Blockchain-based Cyber-Physical Systems (CPS) to establish a scalable framework for designing a secured, automated, and traceable modeling in machining processes. Machining operations like turning, being inherently complex, rely on different types of explanatory parameters such as feed rate, depth of cut, cutting speed, tools, and environmental factors. All of these variables significantly influence key response variables like surface quality, tool wear, cutting forces, and energy consumption. The proposed Blockchain-based framework, designed using the Object-Process Methodology (OPM) systems modeling language, enables the reliable exchange of comparative data streams within a unified data analytics platform. By …

Neural Network-Based Renewal Reward Theory (Rrt) For Optimal Maintenance Scheduling, Samiksha Aryal, Abhijit Gosavi, Susan L. Murray

Miners Solving for Tomorrow Research Conference

No abstract provided.

Sources Of Tensions In Ai Adoption For Organ Procurement Organizations, Amaneh Babaee, Grace Hall, Daniel Burton Shank, Casey I. Canfield

Miners Solving for Tomorrow Research Conference

No abstract provided.

How Do Human And Ai Gender Bias Interact In Hiring Decisions?, Eyuel Getahun, Daniel Burton Shank, Casey I. Canfield, Jessica L. Cundiff, Jenny Davis, Celia Freed

Miners Solving for Tomorrow Research Conference

No abstract provided.

Predicting Baseball Game Wins With Machine Learning, Alexandar Djidjev, Nathan Hellwege

Miners Solving for Tomorrow Research Conference

No abstract provided.

Approximations And Bounds For Optimal Controls Via Finite Fourier Series, Gabriel Nicolosi, Terry Friesz, Christopher Griffin

Engineering Management and Systems Engineering Faculty Research & Creative Works

This work considers the problem of approximating initial condition and time-dependent optimal control and trajectory surfaces using multivariable finite Fourier series. A modified Augmented Lagrangian algorithm for translating the optimal control problem into an unconstrained optimization one is proposed. A quadratic control problem in the context of Newtonian mechanics is solved to demonstrate the proposed algorithm and various computational results are presented. Use of automatic differentiation is explored to circumvent the elaborated gradient computation in the first-order optimization procedure. Furthermore, mean square error bounds are derived for the case of one and two-dimensional Fourier series approximations, suggesting a general bound …

Intelligent Turning Cyber-Physical Systems Modeling Using Sysml, Prithbey Raj Dey, David Lee Enke, Mario F. Buchely

Engineering Management and Systems Engineering Faculty Research & Creative Works

Cyber-Physical Systems (CPS) support industrial automation that incorporates people, hardware, signal, computation, and control using networking to achieve desired results. The complex automated CPS design demands a standard and comprehensive approach to appropriately identify the system requirements, define the architecture, and model the relationships among the software and hardware components. Systems Modeling Language (SysML) provides the capability for a comprehensive modeling to capture the desired design requirements in the systems architecture. SysML enables performance estimation of the model by analyzing constraints while identifying interactions among the components through various behavioral diagrams. In this paper, SysML is applied to the design …

Simulation-Based Models For Postearthquake Response: A Survey And Research Directions, Abhijit Gosavi, Lauryn A. Spearing, Lesley H. Sneed

Engineering Management and Systems Engineering Faculty Research & Creative Works

Computer simulation is increasingly being used by emergency planners as a tool to improve disaster response given that it can model real-world scenarios, such as earthquakes. Although there has been an increase in simulation research focused on disaster response, much of this literature is from disparate fields and across disaster scenarios. To bridge this gap, this paper surveys simulation-based models for post-earthquake response from the year 2000 onward. Advantages of simulation over closed-form statistical models are discussed. Three main subproblems in post-earthquake response models are explored: (1) service distribution (e.g., food, water), (2) infrastructure restoration (at the building and transportation …