Industrial Engineering Commons^™

Optimizing The Processing Temperature For Synthesis Of Silver Nanoparticles Within Cellulose-Wool Keratin Supramolecular Matrix Using Butylmethylimmidazolium Chloride Ionic Liquid, Ngesa E. Mushi

Tanzania Journal of Engineering and Technology (TJET)

To date, the synthesis of silver nanoparticles on the surface of cellulose and wool keratin biopolymer, while dissolved in ionic liquid, is attractive because of its biomedical potential. However, the optimal processing temperature for the nanoparticle formation is unclear. The previously reported temperature of approximately 120°C gives unpredictable results. The current study employs a combination of 50% cellulose and 50% keratin, along with 69 mg of silver chloride, in Butylmethylimidazolium Chloride BMImCl ionic liquid, using a single-pot process to produce a supramolecular film via non-derivatized mechanochemical interactions. The primary objective is to experimentally establish the processing temperature to achieve stable …

Mechanical Properties Of Pineapple Braided Fabric Reinforced Epoxy Matrix Composite Fabricated Via Vacuum-Assisted Resin Infusion Moulding, Bwire S. Ndazi

Tanzania Journal of Engineering and Technology (TJET)

Fibres and fabrics obtained from lignocellulosic materials have attracted attention as reinforcements in polymer composites due to their competitive mechanical and ecological benefits. While the potential of randomly oriented pineapple leaves fibres (PALF) in composites have explored in previous studies, the mechanical behaviour of braided PALF fabric composites fabricated via vacuum-assisted resin infusion moulding (VARIM) remains relatively unexplored. In this study the tensile, compression and flexural strengths of epoxy-based composite containing 40 vol.% braided pineapple fabrics, fabricated using VARIM, for load carrying applications were investigated. The results revealed that the three-layered unbleached pineapple braided fabric composites exhibited a tensile strength …

Root Cause Analysis Of Performance Degradation For An Xyz Thermal Power Plant In Tanzania, Simon I. Marandu

Tanzania Journal of Engineering and Technology (TJET)

Thermal power plants in Tanzania have been experiencing performance degradation, typically arising from component and subsystem failures, which contribute to power system instability and unreliability. This paper presents a case study of the root causes of performance degradation of an XYZ thermal power plant. The study was conducted by reviewing plant documentations, including maintenance and operational data logbooks, and by standardising the approach through the adoption of ISO 14224:2016 for maintenance and reliability data exchange. Data evaluation was carried out using principal component analysis and scree plot analytical techniques to enhance the depth and accuracy of root cause identification. Additionally, …

The Role Of Internal Factors On Vehicular Mobility, Aziz Mdimi

Tanzania Journal of Engineering and Technology (TJET)

Vehicle mobility internal factors are influenced by the performance state of the road surface quality, governor, engine, gear train, differential unit and mobility unit. Studies on vehicular mobility models exist for off-road external factors but absent on on-road internal factors. The on-road internal factors model describes the vehicular mobility performance as a function of internal factors. In the current undertaking, results are generated by the determination of mobility performance characteristics with the application of 2nd Order Ordinary Differential Equations and using Laplace operator with MATLAB Software simulation. The effect of road surface against the time taken varies accordingly. At a …

Wet Gas Metering Performance Using Conventional Flow Measurement Devices, Vitali Mwinyi

Tanzania Journal of Engineering and Technology (TJET)

The demand for and production of natural wet gas from wells has significantly risen in recent years. Although natural gas liquids hold substantial value in the oil and gas market, their presence in wet gas adversely affects gas metering, leading to overreading during measurement. A comprehensive study has been undertaken to formulate and enhance correlations for rectifying overreading in wet gas metering. Nevertheless, most current correlations are designed for horizontal configurations of traditional meters. Mitigating this constraint is crucial to consider the impact of gravity on pressure loss in vertically oriented systems. This study seeks to evaluate the efficacy of …

Safe Human–Robot Collaboration With Risk Tunable Control Barrier Functions, Vipul K. Sharma, Pokuang Zhou, Zhengtong Xu, Yu She, S. Sivaranjani

School of Industrial Engineering Faculty Publications

In this article, we consider the problem of guaranteeing safety constraint satisfaction in human–robot collaboration (HRC) with uncertain human position. We pose this problem as a chance-constrained problem with safety (chance) constraints represented by uncertain control barrier functions, where the probability of safety constraint satisfaction under uncertainty is bounded by a tunable user-defined risk. We solve this stochastic optimization problem using a sampling-based approach and obtain a risk-tunable controller to safely accomplish HRC tasks. We demonstrate the safety and performance of this approach through both simulation and hardware experiments on a 7 degree-of-freedom Franka–Panda manipulator and characterize the tradeoff between …

Automation Of Post Fermentation Must Removal, Grace Hurley, Jakob Spink, Ariel Metscher

Industrial and Manufacturing Engineering

The Harvest Haulers project addresses a critical operational inefficiency at Saucelito Canyon Winery, where post-fermentation must removal from wine barrels was labor-intensive and potentially hazardous. This project aimed to develop a custom forklift attachment that could securely handle Bordeaux and Burgundy barrels, streamline the dumping process, and improve worker safety.

The resulting solution is a forklift-compatible fixture designed to lift, secure, and tilt barrels using a robust combination of a modified aluminum pallet, padded hoop, ratchet straps, and a custom hinge mechanism. The design meets all engineering requirements, including a 600 lb. load capacity, 135° tilt, and a single-operator setup …

Data-Driven Product Recommendations: A Decision Support Framework Utilizing Customer Reviews, Tyler A. Lopez

Master's Theses

With the considerable presence of e-commerce in society, vast number of purchasable goods, and increasing brand variety, consumers are faced with the challenge of buying products that they perceive to be of greatest value to them. To assist consumers with making better informed decisions, e-commerce websites allow individuals to post their own experiences and score the products that they purchase. Despite this information, the variety of experiences and feedback that consumers share do not always lead to clarity on whether a product is best suited for the purchaser. To help guide customers through a simplified purchasing process from the perspective …

Machine Learning And Optimization For Intelligent Decision-Making, Elson Cibaku

Dissertations

This dissertation presents a series of innovative machine learning and optimization model designs that address complex operational challenges across logistics and power systems. By integrating advanced neural architectures with robust optimization techniques, the work delivers scalable solutions designed to improve efficiency, reliability, and decision-making in dynamic and real-world environments. The first study introduces a two-stage approach to effective vaccine distribution. This framework tackles the capacitated vehicle routing problem by combining adaptive clustering techniques with reinforcement learning and a simulated annealing pickup policy. Through extensive computational experiments, the approach demonstrates substantial improvements in routing efficiency, reducing both computational time and logistical …

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 …

An In Depth Examination Of Educational, Family, Economic, And Personal Determinants On Academic Performance, Engagement, And Stress: A Comprehensive Study Among Engineering Students, Oumaima Larif

Theses and Dissertations

This study explores the effects of family, education, economic, and personal factors on students’ decisions to pursue engineering as a profession and their long-term impact on performance as engineering students. We adopted a mixed-method approach, collecting data through surveys administered to undergraduate and graduate engineering students at Mississippi State University. The study results revealed that family, education, economic, and personal factors profoundly influence students' decisions to study engineering. We found that parental expectations, background information, and socioeconomic status, in conjunction with cultural norms, values, gender expectations, and religious beliefs, affect students. Additionally, this study identified gaps in the existing literature …

Advancing Wood Chip Moisture Content Prediction Using Advanced Generative Ai Techniques, Daniel Esteban Marulanda

Theses and Dissertations

In this study we propose a deep learning method to optimize the classification of wood chip moisture content levels using the Vision Transformer and then ultimately increase the classification performance by creating synthetic images using the diffusion transformer model. In the first chapter of our study, we complete a detailed explanation of how the moisture content levels of 10 different wood chips were gathered ranging from 2 to 50$\%$. This chapter serves as a foundation for subsequent sections, illustrating the challenges associated with the current data collection process, which is both time-consuming and inefficient. Accurately determining moisture content for wood …

The Importance Of Community: An Investigation Of Stress, Coping, And The Value Of Social Support For First Responders, Brian Reid

Theses and Dissertations

People are designed to be in community with others, to work together and share the load and weight of life. First responders are a community that has not emphasized the importance of social support to mitigate and buffer against the stress inherent in their jobs. This study investigates the sources of stress, coping methods, and social support of first responders. Results from the first study show the impact of workplace and family stress on the first responder is impactful from the beginning. The secondary study finds that adaptive coping methods are the preferred method to cope with stress and that …

Enhancing Profitability In The Air Transport Industry Through Improved Air Passenger Forecasting: A Comparative Analysis Of Arima, Holt-Winters And Lstm Time Series Forecasting Techniques, Megan Skowronek

Theses and Dissertations

Predicting air passenger volumes is crucial for airports and airlines seeking to reduce costs and enhance profitability. Accurate forecasting enables better planning and efficiency improvements within the air transport industry. This study applies LSTM, ARIMA and HW to U.S. air passenger datasets. Each analysis shows a methodology for predicting air passenger volumes across airports, airlines and across airports and airlines simultaneously. ARIMA was found to have limited applicability, since only a subset of the datasets was stationary. LSTM and HW were applicable to all airlines and ARIMA was applicable to no airlines. LSTM had less error compared to HW at …

In-Hand Singulation, Scooping, And Cable Untangling With A 5-Dof Tactile-Reactive Gripper, Yuhao Zhou, Pokuang Zhou, Shaoxiong Wang, Yu She

School of Industrial Engineering Faculty Publications

Manipulation tasks often require a high degree of dexterity, typically necessitating grippers with multiple degrees of freedom (DOF). While a robotic hand equipped with multiple fingers can execute precise and intricate manipulation tasks, the inherent redundancy stemming from its high-DOF often adds complexity that may not be required. In this paper, we introduce the design of a tactile sensor-equipped gripper with two fingers and five-DOF. We present a novel design integrating a GelSight tactile sensor, enhancing sensing capabilities and enabling finer control during specific manipulation tasks. To evaluate the gripper's performance, we conduct experiments involving three challenging tasks: 1) retrieving, …

Data-Driven Dynamic Decision-Making Using Discrete Optimization And Supervised Machine Learning, Navid Rashedi

Dartmouth College Ph.D Dissertations

In recent years, the operations research community has developed data-driven optimization techniques to solve complex combinatorial problems with the aid of machine learning. This thesis contributes to these efforts by combining machine learning with optimization to expedite online decision-making, with applications in transportation and healthcare.

In the domain of airline operations recovery, the focus is on the aircraft recovery process—repairing disrupted schedules by minimizing overall disruption costs. Traditional exact methods are too time-consuming, while heuristic approaches often yield poor solution quality and lack generalizability across varying formulations. To address these challenges, this research employs supervised machine learning to identify near-optimal …

Fff Process Parameter Identification With Machine Learning Models, Owen Davis Smith

Honors Theses

Additive manufacturing (AM) has seen increasing popularity in recent times, owing to its efficiency and high speeds, particularly with processes such as Fused Filament Fabrication (FFF). Input process parameters have large impacts on the final part. Incomplete process parameters, which can occur for a variety of reasons, make tasks such as replicating AM studies difficult. A machine learning model can be trained on in-situ layer-wise images collected during a print to combat this issue, predicting process parameters with sufficient data. In this study, two parameters were tested: infill pattern orientation and extrusion width. Twelve parts were produced per parameter and …

Minimizing Uncovered Triples: An Integer Programming Approach To College Football Conference Scheduling, Caleb Mallett

Industrial Engineering Undergraduate Honors Theses

Collegiate football teams often compete in groups of 8-20 teams known as conferences. One such conference, the Atlantic Coastal Conference (ACC), added three new schools for the 2024-25 season, bringing their total to 17 teams. Currently, each ACC team plays eight games against others within the ACC. At the season’s conclusion, the two ACC teams with the best intraconference record compete in a conference championship game. With 17 total teams each playing eight games, the ACC could have a three-way tie for the best record where none of top the three teams play one another. To avoid this situation, we …

Developing 3d-Printed Packaging To Protect Biodegradable Sensors Used For Large-Scale, Efficient Water Quality Monitoring, Han L. Siew

Industrial Engineering Undergraduate Honors Theses

Freshwater degradation due to human activities costs the United States over $2.2 billion annually, highlighting the urgent need for improved water quality monitoring methods. Traditional water sampling techniques are labor-intensive, time-consuming, and reliant on single-use plastics that contribute to environmental pollution. This research aims to address these issues by developing 3D-printed biodegradable sensor packaging for unmanned aerial vehicle (UAV)-assisted water sampling. By utilizing biodegradable materials, the research seeks to create an eco-friendly solution that ensures sensor durability while reducing negative environmental impact. The study will involve adjusting the packaging design to withstand UAV deployment by testing and improving the resistance …

Survival Signature Estimation Using Optimization And Monte-Carlo Simulation For K ≥ 3 Classes Of Nodes On Two-Terminal Networks, Md Sazid Rahman

Graduate Theses and Dissertations

This research develops an efficient approach to estimating survival signatures for two-terminal networks with more than two classes of components. Recently, the survival signature has gained substantial attention in the literature on network reliability estimation due to its unique separability property, which enables passing the network topology information independent of the failure distribution of the components. Following recent results from the literature, estimating the two-terminal survival signature by Monte Carlo simulation entails solving a multi-objective maximum capacity path problem on a two-terminal network in each replication. We adapt a multi-objective Dijkstra’s algorithm from the literature to construct the set of …

Extending Simulation-Enhanced Bayesian Optimization Of System Designs: A Computational Study, Luke Kim

Data Science Undergraduate Honors Theses

This honors thesis builds off work initially accepted for publication in the Proceedings of the 2025 IISE Annual Conference & Expo, which introduced “Simulation-Enhanced Bayesian Optimization” (SEBO)—a hybrid testing optimization approach that combined the usage of unbiased but costly physical experiments with the usage of cheaper but potentially biased computer experiments to optimize engineered systems. The original study established the SEBO methodology and demonstrated its effectiveness on a multimodal, two-dimensional benchmark function. Expanding on the work performed, we conduct a broader evaluation of the SEBO framework through parameter testing and experimentation under a variety of additional benchmark functions. This investigation …

A Causal Model Of Performance Shaping Factors For Human Reliability Analysis In Manufacturing., Prameet Ranjan Jha

Electronic Theses and Dissertations

Human reliability analysis is a critical component of probabilistic risk assessment, aimed at predicting and mitigating human errors in complex systems. This dissertation develops a novel approach to human reliability analysis in manufacturing by integrating structural equation modeling and Bayesian networks to improve the estimation of human error probabilities. Traditional human reliability assessment methods, such as the Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) and the Technique for Human Error Rate Prediction (THERP), provide structured techniques for estimating human error probabilities. However, these methods often fail to capture the complex interdependencies among performance shaping factors (PSFs), limiting their applicability in …

Engineering Solutions For The Transplant Supply Gap: Social Network Analysis, Artificial Intelligence, And Optimization In Living Kidney Donation., Joshua Nielsen

Electronic Theses and Dissertations

Kidney transplantation is the gold standard for treating end-stage renal disease, yet over 90,000 patients remain on the transplant waitlist. This dissertation introduces engineering-driven solutions to help reduce the transplant supply gap by addressing three challenges: illicit trafficking, donor recruitment, and evaluation inefficiencies. First, we model illicit organ trafficking networks using social network analysis. We demonstrate that targeting transplant clinics alone is insufficient to disrupt operations. Instead, disrupting the network requires detaining brokers who organize behind-the-scenes logistics—offering a more effective strategy for intervention. Second, we seek to identify a latent population of potential living donors who face barriers such as …

Survival Signature Estimation For All-Terminal Networks By Solving The Multi-Objective Bottleneck Spanning Tree Problem, Dewan Maisha Zaman

Graduate Theses and Dissertations

This research examines the problem of estimating the survival signature of all-terminal networks using Monte Carlo (MC) simulation. Following a recent similar result for twoterminal networks, we show that the work required within each MC replication corresponds to solving a multi-objective bottleneck spanning tree (MOBST) problem. We implement the resulting MC procedure using a “Blocks” algorithm from the literature to solve the MOBST in each replication by identifying its minimal set of non-dominated points. We compare this implementation against intuitive benchmark procedures for completing the work within an MC replication. We conduct numerical experiments to assess the efficacy of multi-objective …

A Digital Twin Approach To Job Shop Scheduling: Simulation And Optimization In Anylogic, Alan Alejandro Corral Lopez

Open Access Theses & Dissertations

In complex manufacturing environments such as job shops, machine breakdowns and maintenance activities can significantly disrupt production flow, leading to delays, bottlenecks, and reduced throughput. This thesis presents the development of a digital twin for a job shop system using AnyLogic simulation software to evaluate the effectiveness of fallback routing logic, where jobs are dynamically rerouted to alternative machines when primary machines are unavailable due to scheduled and unscheduled events. The digital twin replicates real-world job shop conditions, incorporating variable job sequences, machine-specific processing times, and both preventive and corrective maintenance schedules. Two scenarios were compared: a baseline configuration with …

A Systems Engineering Approach For Mitigating Space Debris In Earth's Orbital Regimes, Manuel Soto

Open Access Theses & Dissertations

Space debris in Earth's orbital regimes is a complex threat that originates with human space activity and is maintained by Earth's gravitational force. Space debris poses the risk of colliding with and destroying operational space assets. These collisions, in turn, pose the risk of compounding space debris fragments by turning operational space entities involved in collisions into additional space debris. In other words, the space debris impact does not end with a fragment colliding with a space entity. Instead, the collision is a new space-debris birth since the entities colliding are likely to become multiple fragments. Depending on the collision's …

Multistage Random Key Genetic Algortihm Optimization For Scheduling Flexible Flow Lines With Sequence Depenedent Setup Times, Aadithan Anbuvanan

All Theses

This thesis proposes a new variation to the Random Key Genetic Algorithm (RKGA) for scheduling optimization in flexible flow line manufacturing with sequence dependent setup times. The proposed RKGA representation decodes scheduling information independently at each stage, unlike the traditional RKGA, which is only sequenced based on the first stage, limiting flexibility. The proposed method's performance is compared to the traditional method with varying numbers of jobs and stages. It is compared regarding performance ratio and statistical significance of differences through the Wilcoxon Signed Rank Test. Results show that the proposed RKGA outperformed traditional RKGA in high complexity (8 Stage …

Bridging The Gap: Care Team’S Perspectives On Technology And Ai Integration In Healthcare, Sarah Fernandes, Pranathi Boyina, Awatef Ergai Dr., Wellstar Health System, Mohammad Yousef Mousa Naser, Sylvia Bhattacharya Dr.

Symposium of Student Scholars

As healthcare systems increasingly integrate digital solutions, understanding the perspectives of frontline healthcare workers on technology adoption is critical. This study explores how Registered Nurses (RNs), Licensed Practical Nurses (LPNs), and Certified Nursing Assistants (CNAs), collectively referred to as the Care Team, interact with existing and emerging healthcare technologies, including artificial intelligence (AI). Given the growing reliance on digital tools for clinical and administrative tasks, this research examines the challenges and benefits perceived by healthcare professionals when incorporating AI-driven solutions into their workflows.

A cross-sectional research design was employed, involving 30 semi-structured interviews with Care Team members from an Intensive …

A Critical Realist Erp Implementation In Zimbabwean Mining Industry Organisations, Jairos Mukwenha

Tanzania Journal of Engineering and Technology (TJET)

This research uses a critical realist framework to examine the factors influencing the success of enterprise resource planning (ERP) system implementation in Zimbabwean mining industry organisations. From the perspective of critical realism, the mining industry in Zimbabwe faces a complex interplay of opportunities and obstacles while implementing ERP systems. The deployment of ERP in mining firms is critically examined in this paper, emphasising how these systems might improve operational efficiency while considering Zimbabwe's particular socioeconomic circumstances. By exploring underlying structures, mechanisms, and outcomes, the research aims to identify critical challenges and opportunities and develop practical recommendations for improving ERP adoption …

Application Of Artificial Neural Network Models For Predicting Diesel And Petrol Prices In The Geographically Sparsed Regions In Tanzania, John M. Kafuku

Tanzania Journal of Engineering and Technology (TJET)

Fuel consumption in Tanzania, mainly diesel and petrol, accounts for 82 percent of the energy consumption in the country, with significant price volatility affecting market stability, availability of fuel, and investment decisions. This study uses an artificial neural network (ANN) with a backpropagating algorithm to predict fuel prices in four regions of Tanzania. Key input parameters include the currency inflation rate (CIR), the petrol fuel inventory (PFI), the diesel fuel inventory (DFI), and the fuel transport costs (FTC). The study selected the 6-10-10-2 ANN structures for Sumbawanga-Rukwa, Mpanda-Katavi, and Mbeya-Mbeya as well as 6-10-9-2 for the Songea-Ruvuma region. The results …