less than 1 minute read

Published: December 10, 2025

Great wrap-up from the Winter Simulation Conference! As always, conferences are such a fantastic experience — our research group presentations, the feedback, the fresh ideas, seeing real industry applications, and, of course, catching up with old friends, students, and colleagues, and making new connections.

Congratulations to Jose Arvizu on His Successful Master’s Thesis Defense!

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Published: July 25, 2025

Congratulations to Jose Arvizu on the successful defense of his Master’s thesis, “Digital Twin for Real-Time Monitoring and Control of Conveyor Systems Using FlexSim, AI and PLC Integration.” This accomplishment highlights his dedication to advancing the field of intelligent manufacturing through rigorous research. His work contributes to the integration of simulation, artificial intelligence, and automation for enhanced real-time monitoring and control of industrial systems.

Congratulations to Peggy C. Discenza on Her Successful Master’s Thesis Defense!

less than 1 minute read

Published: May 16, 2025

Congratulations to Peggy C. Discenza on successfully defending your Master’s thesis, “Sustainable Agriculture Through Data-Driven Land and Soil Management for Rural Farming Communities”! The use of AI/ML algorithms for crop prediction and the understanding of data-driven methods for soil management are key innovations that will significantly enhance agricultural sustainability. Your dedication to advancing sustainable farming practices through data-driven approaches is truly commendable!

Wrap-up of “Computer Simulation Applications” Course in Spring 2025!

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Published: May 14, 2025

We wrapped up the Spring 2025 “Computer Simulation Applications” class with outstanding project presentations, great discussions, delicious food, and lots of fun! 🎉 Students showcased impressive simulations and real-world problem-solving skills. A big thank you to everyone for your hard work and energy throughout the semester—what a great way to end the course on a high note!👏💻🍕

Congratulations to Alan Corral Lopez on His Successful Master’s Thesis Defense!

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Published: May 13, 2025

Congratulations to Alan Corral Lopez on successfully defending his Master’s thesis, “A Digital Twin Approach to Job Shop Scheduling: Simulation and Optimization in AnyLogic.” his achievement highlights his dedication to advancing intelligent manufacturing through simulation-driven insights. His work exemplifies the integration of digital technologies to optimize complex systems, and we’re proud of his contribution to the field. Well done!

Digital Twin Demo at IMSE Day 2025

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Published: April 17, 2025

It was an exciting day as we presented our poster and showcased the Digital Twin research for the conveyor system, led by Jose Arvizu, a talented Master’s student in Computer Engineering. His work demonstrates the power of real-time modeling and simulation to optimize manufacturing processes.

Spring Into Fun with Mid-Semester Lab Bash

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Published: February 16, 2025

The Spring Lab Party was such a blast! We had amazing food and tons of laughs while celebrating the Spring semester. It was the perfect chance to kick back, relax, and enjoy quality time with friends and family. It’s going to be even better next time!

Congratulations to Dana Zamorano on His Successful Master’s Thesis Defense!

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Published: December 06, 2024

Congratulations to Dana Zamorano on successfully defending his Master’s thesis, “Optimal Route Planning Considering Microlevel Sequence of Delivery Tasks Using Computer Simulation.” 🎓 His work brings valuable insights into logistics optimization through simulation modeling. Dana’s dedication, innovation, and analytical skills truly shine in this research—an impressive milestone and well-deserved success! 👏📊 Best wishes on your next journey!

Congratulations to Atikur Rahman on His Successful Master’s Thesis Defense!

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Published: August 05, 2024

Congratulations to S.M. Atikur Rahman on successfully defending his Master’s thesis, “Integrating Machine Learning and Simulation for Resource Planning of Hospital Systems based on Predicted Length of Stay!” His innovative work combines machine learning and simulation to improve hospital resource planning, showcasing his dedication and expertise. We’re incredibly proud of his achievement and excited to see what comes next!

Won the Runner-Up Position in the HSPI-2024 Simulation Competition

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Published: February 15, 2024

We are thrilled to share that our team recently participated in the FlexSim simulation building competition at the 2024 Healthcare Systems Process Improvement (HSPI) Conference and emerged as the runner-up! This competition brought together some of the brightest minds in healthcare systems simulation, and we were proud to showcase our skills and innovative approach to solving real-world challenges.

Current PhD Students

Published: January 01, 2026

Current Master’s Students

Published: January 01, 2026

Current Undergraduate Students

Published: January 01, 2026

Other Mentorship Accomplishment

Published: January 01, 2026

Dissertation Committee Co-Chair/Member

Dissertation Committee Member: Ismail Hossain, PhD in Computer Science (In Progress)
Dissertation Committee Co-Chair: Solayman Hossain Emon, PhD Candidate in Computational Science Program (In Progress of Ph.D. and Completed MS in December 2025 )
Dissertation Committee Member: Pablo Bustamante, PhD Candidate in Computational Science Program (In Progress)
Dissertation Committee Member: Solomon N. Amoo, PhD Candidate in Data Science Program (In Progress of Ph.D. and Completed MS in December 2025 )
Dissertation Committee Member: Basavarajaiah S Totada, PhD in Electrical and Computer Engineering, December 2025 (Completed)
Dissertation Committee Member: Andrews T Anum, PhD in Computational Science Program, December 2021 (Completed)

Thesis Committee Co-Chair/Member

Thesis Committee Co-Chair: Md Abubakar, MS in Manufacturing Engineering, July 2025 (Completed)
Thesis Committee Member: Manuel Soto, MS in Systems Engineering, May 2025 (Completed)
Thesis Committee Member: David Kojo Amakye, MS in Computational Science Program, May 2025 (Completed)
Thesis Committee Member: Emmanuella Duah, MS in Computational Science Program, May 2025 (Completed)
Thesis Committee Co-Chair: Tenzin Lhaden, MS in Systems Engineering, November 2024 (Completed)
Thesis Committee Co-Chair: Tamim Alam, MS in Computational Science Program, November 2024 (Completed)
Thesis Committee Co-Chair: Md Asif Khan Sharon, MS in Manufacturing Engineering, June 2024 (Completed)
Thesis Committee Co-Chair: Jeevarathinam Sentilkumar, MS in Industrial Engineering, December 2021 (Completed)

Undergraduate Student Supervision

Diana G Lopez-Valdez, IMSE Undergraduate student, SHS/FlexSim Simulation Competition 2024
Juan A Ortiz, IMSE Undergraduate student, SHS/FlexSim Simulation Competition 2024

Formal PhD Students

Published: January 01, 2026

Formal Master’s Students

Published: January 01, 2026

Jose F Arvizu Astorga

Program: MS in Computer Engineering
Research Topic: Digital Twin for Real-Time Monitoring and Control of Conveyor Systems Using FlexSim, AI, and PLC Integration
Graduation Date: 07/25/2025
Abstract: This project developed a Digital Twin (DT) of a conveyor system using FlexSim simulation software, the FlexSim Emulation module, a vision system, and a programmable logic controller (PLC). The system integrates multiple sensors, including a vision sensor, to capture real-time data. The vision system uses artificial intelligence (AI) to train a model for object classification. Using a convolutional neural network (CNN) called EfficientNetB0, we developed a classifier capable of identifying object colors, shapes, and defective items not intended for processing. A mini-scale prototype served as a testbed, demonstrating real-time monitoring, remote oversight, and enhanced system control. To demonstrate how a Digital Twin can enhance manufacturing systems, a case study was conducted comparing a traditional inspection scheme with a DT-based inspection approach. A simulation of a standard 8-hour production shift was run using the developed model. The results, based on performance metrics provided by the simulation software, indicate that the DT inspection scheme is more cost-effective, reduces scrap, and improves overall production efficiency.

Alan Lopez

Program: MS in Systems Engineering
Research Topic: A Digital Twin Approach for Optimizing Job Shops Scheduling: Simulation and Optimization in AnyLogic
Graduation Date: 05/13/2025
Abstract: 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 fixed routing with first-in-first-out (FIFO) order and an alternative configuration with fallback logic. Performance was assessed across different KPIs, including makespan, machine utilization, throughput, and average waiting time. Simulation experiments were conducted in a FIFO (First In, First Out) sequence and with downtime events, including both preventive and corrective maintenance. Results were validated through sensitivity analysis and statistical testing. The findings showed that fallback logic reduced makespan by 8.24%, improved throughput by 8.89%, and led to more balanced machine utilization. A two-sample t-test confirmed that the makespan reduction was statistically significant at the 90% confidence level (p = 0.0304). These results suggest that integrating fallback routing into a job shop’s digital twin enhances system flexibility and resilience, enabling more adaptive and efficient production control. The study contributes to the growing body of research on digital twin technology and supports its role in enabling smart, disruption-tolerant manufacturing systems aligned with Industry 4.0 objectives.

Peggy Clareece Discenza

Program: MS in Systems Engineering
Research Topic: Sustainable Agriculture Through Data-Driven Land and Soil Management for Rural Farming Communities
Graduation Date: 05/16/2025

Abstract: This thesis aims to investigate sustainable farming practices by utilizing a database containing soil, weather, and water data. Additionally, it evaluates cropping strategies using a system that incorporates nutrient dynamics and environmental values. The objective is to monitor, develop predictive models, and assess the impact of both controlled and uncontrolled nutrient variables. This is achieved through the development of an algorithm that provides information on soil health based on datasets and land use, with a particular focus on residual values after harvesting for deeper analytical insights and interpretation. This study focuses on key nutrients, including pH levels, water availability, and temperature forecasts. Utilizing the available dataset, machine learning algorithms are applied to evaluate and compare two main scenarios: pure cropping and intercropping. Additionally, the proposed model assesses the selection process for these features using three methodologies, which guide the identification of either a primary crop or a companion crop, depending on the scenario. This approach optimizes rural farming operations by providing farmers with predictive insights into soil health and nutrient availability, thereby promoting the long-term sustainability impact for both farmers and their communities.

Dana Zamorano

Program: Masters in System Engineering
Thesis Title: Optimal Route Planning Considering Microlevel Sequence of Delivery Task Using Computer Simulation
Graduation Date: 12/06/2024
Abstract: As globalization reshapes industries worldwide, supply chain management has evolved into a highly interconnected process that oversees traditional management. Today’s rapid technological advancements enable companies to share information across continents in seconds, facilitating operational alignment and enhancing adaptability to rapid changes. Therefore, industries must adjust to the new technology and use it to their advantage. However, even when this impact can be observed globally, the general impact can be analyzed and eradicated by performing micro-level analysis within the supply chain using technologies to their advantage. Hence, this study aims to investigate micro-level analysis, simulating how different delivery strategies could be adopted for houses, departments, and apartments within the El Paso, TX, location. Key components, such as delivery delays and overall processes, were thoroughly integrated into the simulation framework. The MOST (Maynard Operation Sequence Technique) analysis technique and a Monte Carlo simulation were employed to avoid time bias in the simulated scenarios. In addition, the results obtained from this analysis were embedded using GIS mapping in AnyLogic simulation software. This study highlights the significance of conducting a detailed analysis by illustrating scenarios in which companies may make the right hiring decision with improved resource utilization and revenue. The result also demonstrates the scenarios where companies may have fewer employees failing to make the required deliveries, resulting in unsatisfactory staff/customers due to the high workload and missing deliveries.

S.M. Atikur Rahman

Program: Masters in Industrial Engineering
Thesis Title: Integrating Machine Learning and Simulation for Resource Planning of Hospital Systems Based on Predicted Length of Stay
Graduation Date: 08/05/2024
Abstract: Hospital systems are critical to efficient resource planning, especially in the event of operating emergency rooms and high patient invasion. The uncertainty in patient arrival and length of stay (LOS) makes the task complex. Usually, the hospital systems rely on previous experience and run on pre-determined resource planning, often leading to unsatisfactory patient service and low throughputs. The existing literature usually focuses on the traditional optimization techniques based on the available system boundary and neglects the critical factor of patient LOS. This paper closes this research gap and proposes a novel approach for resource planning in hospital systems, considering the impact of patient LOS. The methodology integrates discrete event simulation (DES) and machine learning (ML), making it, to the best of our knowledge, the first paper to showcase such a combination in this area of application. The paper first investigates the ML model to predict LOS based on patients’ vitals. Later, a model was simulated using AnyLogic software to mimic time-independent patient arrival and flow in hospital systems. The simulation model was connected with the trained ML model to predict the LOS in real time. Finally, we propose and integrate the dynamic planning algorithm with the simulation model, which uses the predicted LOS for resource planning. It results in zero (0) service loss, a significant drop in average waiting time, and approximately 10% of resource savings compared to the traditional planning policy. The proposed approach was validated through rigorous experimentation, scenario analysis, and quantitative evaluation matrices.

Miriam M. Aguilera Nava

Program: Masters in System Engineering
Thesis Title: Optimal Route Planning Using Computer Simulation in the Context of Distributor-to-Consumer Supply Network
Graduation Date: 12/06/2023
Abstract: Effective route planning for distribution centers is a multifaceted challenge crucial to business success, as it directly impacts time and cost savings while enhancing overall delivery efficiency. Oversight of proper planning can lead to problems and inefficiencies in the distribution process. Hence, this work undertakes a comparative experiment of two delivery strategies in a distributor-to-consumer supply network. While the first strategy investigates the delivery process by zip area, the second strategy considers the clustering of the areas for more streamlined delivery fulfillment. The effectiveness of the delivery process is gauged based on a set of metrics, including total distance traveled, average delivery time, missed target deliveries, and resource utilization. The study specifically addresses five distribution centers in the El Paso, Texas, USA, area and the delivery addresses across various zip codes in El Paso. The delivery network was simulated using the AnyLogic software, with results analyzed on both daily and weekly timelines. Simulation data were systematically logged into the AnyLogic database for subsequent analysis. In doing so, this paper presents a comprehensive analysis derived from observed data, offering a comparative assessment of the two delivery strategies. The findings provide a quantitative evaluation of the effectiveness of route planning within diverse scenarios in end-user supply networks.

Fazle Rabbi

Program: Masters in Industrial Engineering
Thesis Title: A Novel Approach for Defect Detection of Wind Turbine Blade Using Virtual Reality and Deep Learning
Graduation Date: 07/09/2023
Abstract: Wind turbines are subjected to continuous rotational stresses and unusual external forces such as storms, lightning, strikes by flying objects, etc., which may cause defects in turbine blades. Hence, a periodical inspection is required to ensure proper functionality and avoid catastrophic failure. The task of inspection is challenging due to the remote location and the inconvenient accessibility for human inspection. Researchers used images with cropped defects from the wind turbine in the literature. They neglected possible background biases, which may hinder real-time and autonomous defect detection using aerial vehicles such as drones or others. To overcome such challenges, in this paper, we experiment with defect detection accuracy by having the defects with the background using a two-step deep-learning methodology. In the first step, we develop virtual models of wind turbines to synthesize the near-reality images for four types of common defects - cracks, leading edge erosion, bending, and light striking damage. The Unity® perception package generates wind turbine blade defect images with variations in background, randomness, camera angle, and light effects. In the second step, a customized U-Net architecture is trained to classify and segment the defect in turbine blades. The outcomes of the U-Net architecture have been thoroughly tested and compared with 5-fold validation datasets. The proposed methodology provides reasonable defect detection accuracy, making it suitable for autonomous and remote inspection through aerial vehicles.