The project entails building a system dynamics (stock-and-flow) model to represent and simulate the actions, reactions, and interactions of the stakeholders in the Net Zero transition process (e.g., governments, industries, NGOs, energy producers, and consumers). The system dynamics model will be used to simulate different transition scenarios, and examine the effect of applying various policy tools (e.g., taxes, incentives, caps, promotion) on the speed, cost, and trajectory of the Net Zero transition pathway, aiming to draw evidence-supported insights and policy recommendations.
Research area, student roles & skills
Research area: My research focuses on applying predictive and prescriptive analytics methods (i.e., operations research, simulation, and machine learning) to understand and advance the transition to low-carbon energy systems (i.e., the Net Zero transition). This includes developing and testing novel models and methods for designing low-carbon energy systems and modeling the behavior of stakeholders that participate in or are affected by the transition.
Student roles: The student will conduct a literature review to identify and analyze studies that addressed the dynamics of Net Zero transition, aiming to develop a clear understanding of the state of knowledge and the research gaps. Next, a stock-and-flow model that represents the relations and interactions between stakeholders will be conceptualized with the help of the supervisor. This conceptual model will then be used to develop a computational system dynamics simulation model on a package like Vensim or AnyLogic. The next step involves conducting simulation runs to test different scenarios and policy alternatives. Finally, the results will be thoroughly analyzed to draw insights and recommendations.
Skills required: The student should have some familiarity with the Net Zero transition process and a clear understanding of the stakeholders' roles in it. The ability to develop conceptual dynamic models and translate them into system dynamics simulation models using packages such as Vensim or AnyLogic is a key requirement. The student should also be able to analyze numerical results and extract patterns and trends from them.
2. Building the Future: Energy-Efficient Carbon Capture & Reuse Systems
Supervisor: Wael Ahmed
University: University of Guelph
Location: Guelph, Ontario
Start date: 2027-05-17 (flexible)
Disciplines: Engg-Industrial, Engg-Manufacturing, Engg-Mechanical, Engineering, Engg-Systems and Technology, Engg-Chemical, Engg-Petroleum
Carbon dioxide (CO2) is the main greenhouse gas, remarkably contributing to global warming and climate change. Nonetheless, CO2 is an inevitable output of a vast variety of industries relying on fossil fuels for addressing the energy demand. On the other hand, CO2 is a valuable matter and can be used in many processes; for instance, CO2 plays a vital role in plant growth (i.e., photosynthesis) and it is extensively consumed in greenhouses. Therefore, there exists an opportunity to develop and apply a technology that can not only capture CO2 but also directly integrate it into plant growth process in greenhouses. The main objective of this project is to design an airlift pump a CO2 capture device while dissolving it into water if operating by exhaust flue gases from heating processes. Using the new airlift pump technology can improve plant growth by 40-60% while creating a positive environmental impact. During the course of this project, this objective has been accomplished by designing and testing multiple units of these pumps in the field for potential commercialization for the greenhouse industry. Currently, a final product development and manufacturing process optimization to produce those pumps at commercial scale are needed before our technology is ready for the indoor farming market.
Research area, student roles & skills
Research area: My work is related to the design and operation of energy systems.
Student roles: The student will be involved in all aspect of energy system design, manufacturing and performance testing.
Skills required: Student with strong background of mechanical engineering. Also, student with background on electrical systems, data processing and instrumentation will be as asset.
3. Change-Point-Aware Reinforcement Learning for Non-Stationary Newsvendor Inventory Decisions
This project will develop a change-point-aware reinforcement learning framework for a non-stationary multi-period newsvendor problem. In many inventory systems, demand does not follow a fixed distribution. It may change because of seasonality, market shocks, promotions, disruptions, or product life-cycle effects. Moreover, in lost-sales settings, true demand is often censored because unmet demand is not fully observed. These features make the classical newsvendor solution and standard reinforcement learning methods less reliable.
The proposed project will address this gap by developing a structure-guided RL policy that combines newsvendor theory with adaptive learning. Instead of treating inventory control as a black-box RL problem, the model will use the critical-fractile structure of the classical newsvendor problem as a policy benchmark or learning feature. A change-point detection module will monitor demand observations and identify possible regime shifts. When a regime shift is detected, the RL policy can update, reset, or switch to a more exploratory ordering strategy. This creates an adaptive policy that learns not only how much to order, but also when past demand information should no longer be trusted.
The project will build a Python simulation environment for a single-product, multi-period inventory system with uncertain demand, holding costs, shortage costs, lost sales or backorders, and non-stationary demand regimes. Benchmark policies will include classical newsvendor ordering, rolling-window quantile policies, forecast-based order-up-to rules, and standard RL without change detection. The proposed change-point-aware RL policy will be compared against these baselines.
Performance will be evaluated using total cost, service level, regret, adaptation speed after regime shifts, inventory stability, and robustness under censored demand. The expected contribution is a computational and methodological study showing how newsvendor structure and change detection can improve RL-based inventory decisions under non-stationary demand.
Research area, student roles & skills
Research area: This research area integrates operations research, inventory theory, reinforcement learning, and data-driven decision-making for supply chain systems under uncertainty. The focus is on adaptive inventory control problems in which demand patterns are unknown, partially observed, and may change over time. The work combines stochastic modeling, newsvendor theory, simulation, change-point detection, and reinforcement learning to develop inventory policies that are not only cost-effective, but also adaptive, interpretable, and robust to demand shifts, lost-sales censoring, and limited historical data.
Student roles: The student will contribute to the development, implementation, and evaluation of the proposed change-point-aware reinforcement learning framework. Initial tasks will include reviewing selected literature on the newsvendor problem, non-stationary inventory control, reinforcement learning for inventory systems, change-point detection, and censored-demand learning. The student will summarize relevant modeling assumptions, algorithms, and performance metrics to support the design of the computational study.
The student will then build a modular Python simulation environment for a multi-period newsvendor system. The simulation will include inventory dynamics, demand generators, cost functions, lost-sales or backorder mechanisms, and non-stationary demand regimes such as gradual trends, sudden shifts, and recurring seasonal patterns. After validating the simulation model, the student will implement benchmark policies, including the classical critical-fractile solution, rolling-window empirical quantile policies, forecast-based rules, and standard reinforcement learning methods.
The student will help implement the proposed change-point-aware RL policy. This may include a simple demand-shift detection module, a policy reset mechanism, and a structured state representation that incorporates recent demand history, inventory position, cost parameters, and critical-fractile information. The student will run computational experiments across different cost ratios, demand volatility levels, regime-shift frequencies, and censoring levels.
The student will analyze results using tables, plots, and statistical summaries. Particular attention will be given to identifying when change-aware RL improves performance, when simpler adaptive policies are sufficient, and how demand censoring affects learning quality. Final deliverables will include documented Python code, experimental results, a technical report, presentation slides, and a manuscript-style summary suitable for development into a conference paper or journal submission.
Skills required: The student should have a background in industrial engineering, operations research, applied mathematics, computer science, or a closely related field. Required preparation includes basic probability, statistics, optimization, and inventory control. Python programming experience is essential. Familiarity with simulation, machine learning, reinforcement learning, or time-series analysis would be beneficial but is not required. The student should be comfortable reading technical papers, implementing computational models, conducting numerical experiments, analyzing results, and writing technical summaries.
4. Data-Driven Analysis of Reverse Logistics Flows for Circular E-Commerce Systems
Supervisor: Samuel Yousefi
University: Ontario Tech University (Oshawa campus)
Location: Oshawa, Ontario
Start date: 2027-06-01 (flexible)
Disciplines: Engg-Industrial, Engg-Systems and Technology, Management Information Systems, Mathematics, Management
The rapid expansion of e-commerce has led to a significant increase in product returns, making reverse logistics a critical operational function in retail systems. Returned products (e.g., apparel and electronics goods) pass through multiple stages and reintegration into resale, refurbishment, or disposal channels. The increasing volume and complexity of returns place significant pressure on logistics networks and require a deeper understanding of return flow patterns in practice. Return rates differ across product categories due to factors such as product type, customer behavior, and return policies, creating a need to predict return volumes and improve operational planning in reverse logistics systems. This project aims to analyze return flows in e-commerce reverse logistics systems using operational data. Specifically, it examines patterns in product returns, including category-level return volumes and variations in product handling within the reverse logistics network. To achieve this, predictive analytics techniques will be applied to publicly available datasets, such as e-commerce transaction records, retail reports, and open logistics databases. The data will be preprocessed to ensure consistency, address missing values, and prepare it for analysis. Regression analysis will then be used to identify relationships between key variables (e.g., product category and return policy type) and to support the prediction of return volumes and processing requirements. Finally, the results will be translated into actionable insights for improving reverse logistics operations. This includes optimizing facility locations and supporting capacity planning decisions to support circular economy objectives. This research contributes to the literature by providing data-driven insights for enhancing reverse logistics performance and return management strategies. The outcome is an analytical understanding of return flow patterns in the e-commerce sector and how they can be used to reduce costs and improve allocation of resources in return systems.
Research area, student roles & skills
Research area: My research focuses on three interconnected areas: (i) supply chain analytics, (ii) systems modeling, and (iii) risk and disruption management. I develop novel decision support frameworks that combine operations research and simulation modeling to help organizations make informed decisions in uncertain and rapidly changing environments. A key aspect of my work is sustainability, where I aim to design and optimize production and supply chain systems across different industries that are not only efficient and economically viable but also environmentally responsible and socially impactful.
Student roles: The student will contribute to the analysis of reverse logistics return flows in e-commerce systems using a data-driven approach. Under the supervision of the faculty, the student will begin by exploring the structure of reverse logistics operations and identifying the main stages involved in handling returned products, including collection, sorting, processing, and redistribution activities. The student will also review relevant academic and industry sources to understand the major operational challenges associated with increasing return volumes in e-commerce environments. Following this preliminary stage, the student will work with publicly accessible operational datasets obtained from sources such as retail transaction records. The student will contribute to preparing the datasets for analysis by organizing raw data, addressing missing or inconsistent records, and structuring the information into an analyzable format. Next, the student will participate in the analytical evaluation of return flow patterns using predictive analytics methods. In particular, the student will employ regression-based techniques to examine how operational variables such as product categories, return conditions, and customer-related factors influence return patterns and processing requirements across the reverse logistics network. The student will support the interpretation of analytical outputs and investigate how different factors contribute to changes in return intensity. In the final stage of the project, the student will assist in translating analytical findings into operational insights that can support managerial decision-making in reverse logistics systems. This involves identifying opportunities to enhance resource allocation, assist in facility planning decisions, and improve operational efficiency in return management processes. The project will provide the student with hands-on experience in predictive analytics and operational data management within reverse logistics systems. The student will strengthen their ability to examine return patterns in e-commerce environments and generate analytical insights to support evidence-based improvements in reverse logistics operations.
Skills required: The ideal candidate is an undergraduate student in industrial engineering, management, mathematics, or a related field, with familiarity in data analysis tools and programming environments such as Python. Experience in descriptive analysis to summarize operational data into insights and exposure to predictive analysis to extract relevant information from datasets are required. Exposure to reverse logistics and supply chain systems design is considered an asset. The student should be motivated to work on data-driven analysis and decision support for solving practical problems in complex systems.
5. Designing Collaboration Tools to Support Effective Collaboration in Interdisciplinary Teams
Supervisor: Sharon Ferguson
University: University of Waterloo
Location: Waterloo, Ontario
Start date: 2027-05-17 (flexible)
Disciplines: Engg-Industrial, Engg-Software, Engg-Systems and Technology, Engineering, Computer Science, Engg-Computer, Communication
As teams across industries become more global, interdisciplinary, and adopt more flexible working styles, they are presented with new collaboration challenges resulting from new technology, new schedules, and new ways of thinking. However, the exact characteristics that challenge these teams in new ways also present new ways of understanding these challenges and designing their solutions. Social science researchers have identified a number of phenomena that impact how successful teams are, such as Psychological Safety, Shared Understanding, and Conflict. However, these phenomena are commonly measured via surveys, which are costly to frequently re-administer and are not often used by leaders in industry. The broader objective of this work is to build tools that measure these phenomena automatically, and help guide teams towards more effective collaboration. We measure these constructs using the mass amounts of data created when teams use virtual collaboration platforms like Slack, Zoom, or Microsoft Teams. The algorithm development is guided by the social science literature and validated by collecting ground truth data from the teams through surveys and interviews.
Once we can successfully measure these complex phenomena from teams’ digital collaboration data, we can identify periods of ineffective teamwork – signalled by low levels of Psychological Safety, or frequent unproductive conflict – and intervene to nudge teams towards more successful practices. Thus, the second stage in this project involves designing interactive tools that can be added to existing collaboration platforms to measure these phenomena in real-time and intervene to identify harmful practices, prompt reflection, and provide suggestions for teams to improve their collaboration. Upon designing this tool, pilot tests will be used to evaluate and iterate upon the interaction design.
Research area, student roles & skills
Research area: Within the broader field of Human-Computer Interaction, my work studies how we can better understand and support collaboration for knowledge workers in complex, interdisciplinary domains such as engineering design. With more teams adopting hybrid and flexible working styles, they rely on virtual collaboration platforms, like Slack or Microsoft Teams, to collaborate. These platforms create digital traces of their collaboration patterns. My work aims to use these digital traces to 1) better understand how different collaborative patterns relate to positive team outcomes and 2) design and evaluate interactions that support teams through effective teamwork.
Student roles: Software Development Portion: The student will develop the add-on tool/bot that implements a given algorithm, measures a phenomenon in the background, and intervenes in the team’s collaboration when needed. The primary role of the student will be to develop the add-on tool, though they will also be able to collaborate with other students to get exposure to literature surveying and developing the natural language processing algorithm, should they choose to. The hope is for the project to result in a tool that can be released for public download (such as on the Slack app store) as well as a Human-Computer Interaction conference publication (in venues such as CHI and CSCW) outlining the tool's design and evaluation.
User Evaluation Portion: This portion of the project will also work toward the development of an application, but will focus on working with humans to understand how the technology should be designed, or testing how well a technology meets the users' requirements. This may involve conducting formative surveys/interviews/focus groups to inform the tool's design, or assisting in some user testing and in-situ evaluations, where a team uses the tool for a set period of time. Evaluations will use interviews, surveys, observations, and data analysis skills, including both quantitative (statistics) and qualitative (thematic analysis) methods.
Skills required: Ideal candidates will either be interested in the algorithm and software development portion of the project, or the human evaluation portion. The software development portion includes working with APIs, cloud data storage & retrieval, data visualization, Data analysis skills (R, Python, SQL), including basic statistical analysis (regression, t-tests, ANOVA, etc.) and natural language processing (text cleaning, linguistic analysis, topic modelling, LLM prompting). The human evaluation portion includes interview, survey, user testing, observation, and data analysis/statistics skills. All candidates should be willing to learn the following skills: how to read team science literature from the social sciences/management/engineering design communities.
6. Developing a spatio-temporal kriging model for wind power in Atlantic Canada
Supervisor: Ahmed Saif
University: Dalhousie University (Halifax campus)
Location: Halifax, Nova Scotia
Start date: 2027-05-17 (flexible)
Disciplines: Engg-Industrial, Engg-Systems and Technology, Engineering, Mathematics, Statistics
The goal of this research project is to develop a spatio-temporal kriging model that can predict wind speed (and consequently, the output power of wind turbines) anywhere in the Atlantic provinces of Canada by using the wind speed and direction readings in weather stations. The spatio-temporal kriging model combines spatial and temporal information to predict values at unobserved locations and times. This prediction model will later be used in other optimization models to select the types, locations and sizes of wind farms and energy storage facilities to install, though the optimization model is not part of the project. Different kriging models will be tested, aiming to find one that strikes a good balance between simplicity and accuracy.
Research area, student roles & skills
Research area: My primary research area is energy system optimization, particularly dealing with uncertainty in supply and demand.
Student roles: Under my supervision and with my support, the student will collect weather data, develop spatio-temporal kriging models and validate their fit for the data. Before that, a survey of the relevant literature will be conducted. The student will summarize their results and findings in a technical report to be submitted by the end of the internship.
Skills required: The student is expected to have a strong background in statistics, particularly time-series models, spatio-temporal analysis, and regression analysis. The ability to use a general-purpose programming language like Python or R, along with the relevant packages, is required.
7. Digital Twin–Based Cybersecurity Framework for Smart Warehouses
This project develops a digital twin of a smart warehouse to monitor, simulate, and predict cybersecurity threats in real time. The framework integrates IoT devices, WMS data, and operational workflows to detect anomalies (e.g., sensor spoofing, AGV hijacking) and evaluate their impact on KPIs such as throughput, accuracy, and safety. The goal is to enable proactive cyber risk mitigation through simulation-driven decision support.
Research area, student roles & skills
Research area: Supply chain management, decision making, digitalization
Student roles: Develop a warehouse digital twin model (process + cyber layer) Identify and model cyber-attack scenarios Design cybersecurity KPIs (e.g., detection time, system disruption index) Conduct simulation experiments and analyze results Propose a decision-support framework for cyber resilience
Skills required: Industrial Engineering / Systems Engineering / Computer Science Simulation tools (AnyLogic, Arena, or Python-based simulation) Basic cybersecurity concepts (networks, threats, vulnerabilities) Knowledge of Industry 4.0 / IoT / CPS systems Data analytics (Python, MATLAB, or R)
8. Enhancing Sustainability Models Through Data Analysis and System Dynamics
Building on a previously developed simulation framework for assessing the sustainability performance of a university’s value chain, this project aims to address critical gaps in data availability and integration. While the initial model successfully identified key greenhouse gas (GHG) emission sources and evaluated sustainability strategies, several data limitations limited the analysis’s accuracy and completeness—particularly regarding Scope 3 emissions and utilization.
The proposed project focuses on identifying missing or incomplete data required to enhance the robustness of the existing dynamic simulation model. This includes emissions linked to procurement, travel, waste management, and indirect activities across the university ecosystem. The project will design and propose data-collection mechanisms that combine institutional records and surveys. Emphasis will be placed on ensuring data quality, consistency, and scalability while adhering to ethical and privacy considerations.
Once collected, the data will be processed and analyzed using appropriate statistical and analytical methods to generate meaningful indicators aligned with environmental, social, and economic sustainability metrics. These enriched datasets will then be integrated into the existing system dynamics model, improving its predictive capabilities and enabling more accurate scenario analysis.
Ultimately, this enhanced simulation framework will provide decision-makers with a more comprehensive and reliable tool to evaluate sustainability strategies, optimize resource allocation, and support the transition toward net-zero emissions. The project will also contribute methodological insights for integrating incomplete or evolving datasets into dynamic models within the service sector.
Research area, student roles & skills
Research area: Fabiola Regis Hernández, a professor at TÉLUQ University, holds a bachelor’s in Industrial and Systems Engineering, a master’s in Manufacturing Systems, and a Ph.D. in Engineering Sciences from Tecnológico de Monterrey. She completed postdoctoral research in home healthcare logistics in Italy. Her research projects focus on operations research, logistics, and supply chain management applied in different contexts, i.e., health care, emergency systems, and industrial problems. Recently, her work has expanded to include big data analysis and sustainable development. She has supervised several theses, collaborated internationally, and edited the book “Humanitarian Logistics from a Disaster Risk Reduction Perspective.”
Student roles: The intern will be based at the Université TÉLUQ building in Quebec City and will have access to a dedicated workspace, an internet connection, and the university’s bibliographic resources. Throughout the internship, the intern will work under my supervision and collaborate with members of our sustainability group. The intern’s responsibilities will include conducting a comprehensive literature review on sustainability assessment and simulation approaches in service systems. They will identify and structure missing or incomplete data required to strengthen the existing dynamic simulation model, enabling a more precise decision-making process. The intern will also gather and organize relevant data from primary and secondary sources, and propose appropriate data-collection mechanisms. The collected data will be analyzed using suitable statistical and decision-support techniques to produce meaningful sustainability indicators. In addition, the intern will contribute to integrating these enriched data into the simulation model (e.g., using tools such as AnyLogic) to improve its accuracy and predictive capabilities. The intern will analyze different scenarios, interpret results critically, and support the identification of effective sustainability strategies. The internship will document the methodology and actionable recommendations to enhance the university’s sustainability performance and support decision-making in a research report.
Skills required: We seek a motivated, autonomous, and proactive individual pursuing a bachelor’s degree in industrial engineering, business administration, or data science/analytics. The candidate will analyze digital practices related to teaching, research, operations, and well-being. Essential skills include proficiency in ESG principles, data collection, and quantitative analysis. Proficiency in continuous simulation tools, such as AnyLogic, and familiarity with tools such as Python or R are mandatory. Strong analytical, communication, project management, and teamwork skills are required. We encourage applications from all qualified individuals to promote an inclusive environment.
9. Exploring simulation as a decision support tool: Connecting strategy, product portfolios and human behaviour through simulation models
This project is part of a larger research program aimed at investigating how to more systematically integrate the human factor into Industry 4.0 technologies and systems at the operational, tactical, and strategic levels. More specifically, we seek to develop data-driven methods for integrating human behavior into simulation models to improve decision-making during the development of new products, services, and processes. Within the framework of this internship project, we will simulate strategic decision-making using a product portfolio modeling approach, for which we have provided a mathematical representation through an optimization model (Albano et al., 2021). Using models for estimating human intentions (e.g., Yokoyama and Omori, 2010), we will simulate the evolution of strategic direction and its impact on performance.
Research area, student roles & skills
Research area: As a full professor of entrepreneurship and innovation management at Polytechnique Montréal, I have spent the last two decades at the intersection of research, innovation, and public policy. My academic and applied work focuses on the strategic management of technology, with a particular emphasis on ecosystems strategic thinking. My work bridges engineering, management, and public policy, with a particular emphasis on how intangible assets, such as patents, scientific knowledge, and entrepreneurial capabilities, can be mobilized to enhance firm-level productivity.
Student roles: The student will contribute in design of some specific agents of the model. More details will be available closer to the beginning of the internship, and will depend on the progress of the research. The student will then be responsible to run some simple simulation models using these agents to test and validate their accuracy in explaining the dynamics of corporate strategy emergence. The student is expected to produce a final internship report in the format of a scientific paper.
Skills required: Basic knowledge of agent-based simulation technologies is required for this position. Expertise with simulation using AnyLogic software is an asset.
This project focuses on analyzing and modeling Quebec’s transportation network, specifically in the forest sector, with an emphasis on integrating sustainability into decision-making. The forestry industry is a vital component of Quebec’s economy, relying on efficient transportation systems to move timber and related products from harvesting sites to processing facilities and markets. However, the transportation network faces numerous challenges, including environmental impacts, infrastructure limitations, and evolving sustainability standards.
To tackle these issues, the project will provide a comprehensive assessment of the forest supply chain and its transportation dynamics, examining road, rail, and multimodal logistics. It will identify key nodes, corridors, and bottlenecks to evaluate their effects on overall system performance. Additionally, stakeholder identification and interaction mapping will help clarify the interconnectedness of harvesting operations, transportation networks, and processing facilities.
The project will enable the mapping of the logistics landscape and facilitate the analysis of the environmental, social, and economic impacts of transportation within the forest sector. Potential scenarios may include routing adjustments, modal shifts, infrastructure improvements, and policy interventions aimed at reducing environmental impacts while ensuring economic and social viability.
Research area, student roles & skills
Research area: Fabiola Regis Hernández, a professor at TÉLUQ University, holds a bachelor’s in industrial and systems engineering, a master’s in manufacturing systems, and a Ph.D. in Engineering Sciences from Tecnológico de Monterrey. She completed postdoctoral research in home healthcare logistics. Her research projects focus on operations research, logistics, and supply chain management applied across different contexts, including industrial, health care, and emergency systems. Recently, her work has expanded to include big data analysis and sustainable development. She has supervised several theses, collaborated internationally, and edited the book “Humanitarian Logistics from a Disaster Risk Reduction Perspective.”
Student roles: The intern will be located at the Université TÉLUQ building in Quebec City and will have access to a dedicated workspace, an internet connection, and the university’s bibliographic resources. Throughout the internship, the intern will work under my supervision and will collaborate with industrial partners and researchers from the forest group. The intern’s responsibilities will include conducting a literature review on transportation networks and decision-making in the forest sector. The intern will identify the key components of Quebec’s transportation network and prepare a visualization that integrates all essential nodes for the forest sector, using tools such as ArcGIS or Python. Finally, the intern will create a document outlining the methodology used and summarizing the main findings.
Skills required: We seek a motivated, autonomous, and proactive individual pursuing a bachelor’s degree in industrial engineering or business administration. The candidate will analyze Quebec’s transportation network, specifically the forest sector. Essential skills include proficiency in ESG principles, data collection, and qualitative and quantitative analysis. Familiarity with visualization tools such as ArcGIS or Python is mandatory. Strong analytical, communication, project management, and teamwork skills are required. We encourage applications from all qualified individuals to promote an inclusive environment.
11. Human-Centric Cyber Resilience in Industry 5.0 Supply Chains
This project explores the human role in cybersecurity resilience within Industry 5.0 supply chains. It studies how human factors (training, awareness, decision-making, fatigue) influence cyber risk and system recovery. The aim is to develop a human-centered resilience model that integrates technology, human behavior, and system performance.
Research area, student roles & skills
Research area: Supply chain management, decision making, digitalization
Student roles: Analyze human-related cyber vulnerabilities Develop human-centric resilience metrics Model interaction between human decisions and cyber events Conduct experiments (simulation or survey-based) Propose training, policy, and system design improvements
Skills required: Industrial Engineering / Human Factors / Ergonomics Knowledge of Industry 5.0 concepts Basic cybersecurity awareness Behavioral analysis and system modeling Qualitative + quantitative research methods
12. Hydrogen economy: challenges and solutions from a supply chain perspective
The research project seeks to investigate the entire value chain of export-oriented low-carbon hydrogen, with a particular emphasis on its production, storage, transportation, and delivery via maritime shipping and chemical carriers. The project will involve assessing the technical, economic, and environmental aspects of blue and green hydrogen production methods, evaluating the optimal shipping routes and logistics for transporting hydrogen, and developing strategies for ensuring safe and efficient delivery to international markets. Through comprehensive analysis, the project aims to identify potential challenges, recommend best practices, and provide insights for the successful implementation of large-scale export-oriented low-carbon hydrogen systems.
Research area, student roles & skills
Research area: This research focuses on the export-oriented production and delivery of low-carbon hydrogen, specifically blue and green hydrogen, using maritime shipping and chemical carriers. Blue hydrogen is produced from fossil fuels with carbon capture and storage, while green hydrogen is derived from renewable energy sources through electrolysis. The aim is to explore and optimize (using Operations Research methods) the feasibility, scalability, and environmental impact of these technologies in large-scale applications, enabling the international trade and deployment of low-carbon hydrogen as a clean energy solution.
Student roles: The student will play a vital role in conducting extensive literature reviews to establish the current state of knowledge in the field of export-oriented low-carbon hydrogen and maritime shipping. The student will assist in designing and conducting techno-economic and life cycle assessments to evaluate the feasibility and sustainability of different production and transportation pathways. The student will contribute to modelling and simulating hydrogen systems, analyzing data, and interpreting results. Additionally, they will actively participate in collaborative discussions, present findings, and contribute to the preparation of research reports and academic publications. Overall, the student will be responsible for providing valuable insights and contributing to the success of the research project by conducting rigorous analysis and delivering high-quality work.
Skills required: The student engaged in this research should possess a strong background in industrial engineering, energy systems, or a related field. Mathematics students are also encouraged to apply. Proficiency in conducting techno-economic assessments, life cycle analysis, and modelling of hydrogen production and transportation systems is crucial. Knowledge of maritime shipping and chemical carrier operations, as well as familiarity with international trade and regulations, would be beneficial. Excellent analytical and problem-solving skills, along with the ability to work independently and collaborate with a multidisciplinary team, are essential for the successful completion of this research project.
13. Innovations in Sustainable Additive Manufacturing
This project investigates how printing parameters and innovative composite influence the mechanical performance, durability, and manufacturability of 3D‑printed parts. Innovative composite filament/ pellets are manufactured from combine virgin and recycled plastics, plant fibres, tire, and targeted additives through extrusion and chemical treatment. Both Fused Filament Fabrication (FFF) and pellet‑fed additive manufacturing will be explored to print samples for material characterization. Material characterization will follow ASTM standards for tensile, flexural, degradation and moisture absorption. Optical microscopy study of composites may be conducted. In addition to variation in composition, variation in printing parameters (like infill density, orientation ad layer thickness) will also be analyzed. Finite Element Analysis on 3D print specimen may also be performed to compare with experimental results. Suitable product for the developed innovative composite will be identified, designed, and optimized through FEA analysis, functionality, ergonomics, manufacturability and research teams’ feedback. Assess market viability, industrial adoption barriers, regulatory requirements, and raw‑material supply‑chain resilience, with special attention to recycled feedstock logistics and quality control. Commercialization potential for both composite filaments/pellets and finished prototypes will be developed, including cost modelling and value‑chain mapping.
The project will train high‑quality personnel (HQP) through hands‑on experience in materials processing, ASTM testing, CAD/FEA, and pilot‑scale printing; mentorship in industry engagement; and knowledge in Canadian regulatory and commercialization processes. Intern will gain competencies aligned with Canadian priorities in circular economy, and sustainable manufacturing, supporting Canada’s 2030 goal to achieve zero plastic waste and help foster domestic advanced manufacturing. Outcomes include validated composite formulations, optimized printing parameters, market-ready prototypes, and a commercialization roadmap to enable industry adoption and recommendations.
Research area, student roles & skills
Research area: My research successes apply mechanical engineering principles to design, analysis, dynamics, vibration, instrumentation, medical/sports technologies, soil-tool interaction, AI applications, materials science, safety systems, and lean manufacturing. Recently, valorizing waste streams (i.e. crop residue, tires, and polymers) into innovative composites and commercial products to promote circular economies benefitting consumers, industries, and the environment has occurred. This will generate secondary income and support sustainable agricultural practices. New ventures in plasticizing natural fibers and additive manufacturing using reclaimed materials are integral to my research My expertise fosters strong interdisciplinary collaboration with academic and industry partners, advancing innovation, knowledge mobilization and successful commercialization.
Student roles: The intern will be an integral member of a dynamic research team committed to advancing Canada’s Zero Plastic Waste Agenda. Reporting to the principal investigator and research assistant, the intern will contribute to the development of sustainable materials, additive manufacturing and gain valuable hands-on experience in engineering research with real-world impact.
Intern will begin with onboarding training including lab orientation and certifications, including WHIMS, UofR Health and Safety, Engineering Safety, chemical lab safety, and TCPS 2 :CORE. Other available professional development and training opportunities will be discussed. Intern will involve in composite material preparation (chemical treatment and extrution), CAD modelling specimens as per ASTM standards and 3D printing (pellet and filament based) samples. Intern will be trained and assist in conducting tensile, flexural, degradation and moisture absorption test according to ASTM standards; analyze and compare the results with FEA. The intern will involve in CAD modelling of prototype and iterative design refinement based on functionality, manufacturability and feedback. They will assist in development of survey and Research ethics board documentation. Interns will support market, regulatory, and supply‑chain assessments, contribute to cost modelling and commercialization planning.
The intern will participate in the weekly research meeting, presenting their progress and contributing to scientific discussions. In the last week of the research, the intern will prepare final report and presentation summarizing methods, findings and future recommendations.
Skills required: Required background: mechanical, materials science or related engineering, with previous experience in 3D printing (FFF/pellet), polymer/composite manufacturing, extrusion, and ASTM materials testing. Proficiency in CAD (e.g., SolidWorks), FEA (ANSYS) and Splicing software. Strong laboratory skills, attention to safety and quality control; comfortable with Microsoft 365 platform for documentation and data analysis. Excellent communication (verbal and written), teamwork, and interest in sustainability and circular‑economy practices.
14. Innovations in Sustainable Composites: Plastination and Additive Manufacturing
This research project investigates the development and commercialization of sustainable composite materials derived from recycled and/or waste resources. The intern will gain practical experience from material valorization to sustainable product development. The composite manufacturing phase includes identifying waste streams (plastics, plant fibre, rubber and others), sorting, cleaning, size reduction, pre-treatment, blending and manufacturing composites. Lab-scale thermo-compression molding, extrusion, vacuum forming and related lab-developed techniques are used. Analyzing and characterizing composite materials are in accordance with ASTM and TAPPI standards. Typical properties include tensile, flexural, and compression, moisture absorption, degradation, acoustic and vibration properties.
In prototyping and commercialization, the project involves an iterative design and development of products in collaboration with industry partners, research team, and potential end users. Feedback will be collected through surveys, interviews, focus groups, and/or structured discussions based on material selection, product design, manufacturability and performance requirements. The intern will assist in customer surveying, generating CAD models, and performing simulations and analyses to assess functional performance and design feasibility. Preliminary market analysis and commercialization feasibility will be completed.
Overall, the project aims to advance the valorization of waste materials to environmentally responsible composite products, contributing to circular economy and sustainable material innovation.
Research area, student roles & skills
Research area: My research successes apply mechanical engineering principles to design, analysis, dynamics, vibration, instrumentation, medical/sports technologies, soil-tool interaction, AI applications, materials science, safety systems, and lean manufacturing. Recently, valorizing waste streams (i.e. crop residue, tires, and polymers) into innovative composites and commercial products to promote circular economies benefitting consumers, industries, and the environment has occurred. This will generate secondary income and support sustainable agricultural practices. New ventures in plasticizing natural fibers and additive manufacturing using reclaimed materials are integral to my research My expertise fosters strong interdisciplinary collaboration with academic and industry partners, advancing innovation, knowledge mobilization and successful commercialization.
Student roles: The intern will serve as an active member of a multidisciplinary research team focused on developing sustainable composite materials from recycled and/or waste resources. Under the supervision of the principal investigator and research staff, the intern will contribute to recycling technologies and advancing of circular economies.
The onboarding training includes lab orientation and certifications, including WHIMS, UofR Health and Safety, Engineering Safety, chemical lab safety, and TCPS 2 :CORE. Other available professional development and training opportunities will be available. The intern will conduct literature reviews on composite materials, standard testing (ASTM and TAPPI), raw material identification, and availability. Following lab training (under supervision), the intern will complete pre-processing, composite manufacturing, and specimen preparation for material characterization according to the standard. Intern will design CAD prototype of product and perform iterations based on feedback (from research team; industry, and end user survey) and simulation-based analysis. Assist in survey development, economic analysis and commercialization plan.
The intern will participate in the weekly research meeting, presenting their progress and contributing to scientific discussions. In the last week of the research, the intern will prepare final report and presentation summarizing methods, findings and future recommendations.
Skills required: The ideal student is enthusiastic, innovative, and able to work both independently and collaboratively. A background in mechanical or related engineering is required, with knowledge of material science, strength of materials, basic design, and manufacturing. Proficiency in CAD (e.g., SolidWorks or SolidEdge) and finite element analysis (preferably ANSYS) is essential. Skill in using Microsoft 365 for documentation, survey design, and data analysis. Strong verbal and written communication skills are important, as is the ability to contribute effectively in a multidisciplinary team focused on sustainability, design innovation, and real-world impact. An environment for learning and developing skills will be fostered.
15. Integration of Cybersecurity KPIs into Industry 4.0 Supply Chain Performance Models
This project integrates cybersecurity performance metrics into traditional supply chain KPIs (cost, service level, efficiency). It develops a unified KPI framework that links cyber events (e.g., data breaches, system downtime) with operational outcomes. The research supports decision-making by quantifying cyber risks alongside operational performance
Research area, student roles & skills
Research area: Supply chain management, decision making, digitalization
Student roles: Identify and define cybersecurity KPIs Map relationships between cyber risks and operational KPIs Develop analytical or simulation-based models Validate framework using case studies or datasets Create visualization tools (dashboards, radar charts, heatmaps)
Skills required: Industrial Engineering / Supply Chain Analytics Strong understanding of KPIs and performance measurement Data analysis and visualization Basic cybersecurity knowledge Familiarity with Industry 4.0 technologies
16. LLM for selecting right solvers for the right problem
Although many optimization solvers are now freely available or low-cost, they remain largely inaccessible to small and mid-sized companies due to the lack of expertise in optimization and operations research. Our objective is to democratize access to these powerful technologies by developing an LLM-based assistant capable of interacting directly with planners, understanding their operational challenges, and recommending the most suitable optimization tools in a simple and intuitive way. Such an approach could significantly improve worldwide accessibility to optimization technologies, particularly in regions such as Africa where access to specialized expertise remains limited.
Research area, student roles & skills
Research area: - large scale optimization
- AI/optimization interface
- transportation/production systems
Student roles: - interfacing solvers/humans - LLM building - writing white papers
Skills required: - basic knowledge of operations research solvers - good in LLMs
17. Modeling Risk and Resilience in Urban Transportation Networks
Supervisor: Saeid Saidi
University: University of Calgary
Location: Calgary, Alberta
Start date: 2027-05-03 (flexible)
Disciplines: Engg-Industrial, Engg-Environmental, Engg-Civil, Engg-Systems and Technology, Geography
This research project supports the planning and delivery of a multidisciplinary workshop on infrastructure network resiliency and emergency evacuation during natural disasters such as wildfires and floods. With direct input from municipal stakeholders in the Towns of Jasper, Banff, Canmore, and the City of Calgary, the project aims to synthesize real-world experiences into a conceptual framework for digital decision-making and risk-informed infrastructure planning. The undergraduate student will support background analysis, risk and network assessment modeling, and the synthesis of lessons learned from the workshop.
Research area, student roles & skills
Research area: My main research activities focus on transportation network modeling, public transportation planning and operations, and big data analytics using mobility sensing data. I work on applying advanced data analytics techniques to transportation systems in order to generate actionable intelligence for cities and transportation authorities. This includes analyzing large-scale mobility datasets to support real-time decision-making, improve system efficiency, and enhance the resilience and sustainability of urban transportation networks, particularly in the context of emergency response and infrastructure planning.
Student roles: The student will assist with literature and case study reviews, spatial and network data collection, and preliminary analysis of infrastructure vulnerability and evacuation dynamics. They will assist in developing preliminary risk assessment models and contribute to the design of a framework that integrates network analysis with emergency response planning. The student will also support the preparation of materials by synthesizing technical insights and identifying research gaps in current infrastructure resiliency approaches.
Skills required: The ideal student should have a background in transportation engineering, industrial and systems engineering, or a related discipline, with interest or experience in transportation networks, risk analysis, or infrastructure systems. Skills in data analysis, GIS or geospatial mapping tools (e.g., QGIS, ArcGIS), and basic programming (e.g., Python, R) are considered assets. Familiarity with emergency management or sustainable infrastructure concepts is beneficial, as is a demonstrated ability to synthesize technical and qualitative information for reports and presentations.
18. Modeling of heat exchanger working with ice slurry
The needs in environmentally friendly refrigerants and efficient heat transfer fluids are currently increasing in the food and fishery industries, medicine, energy storage systems and for the air-conditioning of residential or commercial buildings and deep mines. Two-phase refrigerants, such as ice slurries, represent an interesting alternative to secondary fluid in conventional refrigeration systems due to their high energy density. The objective is to develop a numerical model to quantify the performance of shell-and-tube heat exchangers working with ice slurries. The code could be developed using Matlab, Python or any other language depending on the candidate. The -NTU method would be used to determine the heat transfer rates. To better capture the pressure drop distribution, the emphasis will be put on the modeling of the rheological behavior of the slurries determined experimentally. The model will be validated against experimental data from the literature in terms of pressure drop and overall heat transfer coefficient. The model could be extended to other heat exchanger type. The final step would be to couple it with an optimization algorithm (like the fmincon algorithm in Matlab) to optimize either the performance or the design of the heat exchanger.
Research area, student roles & skills
Research area: I am expert in fluid mechanics for energy systems, bioengieering and coastal applications, aerodynamics, etc. My research concerns mainly the development of advanced numerical modelings, from optimization algorithm coupled to 1D models to 3D direct numerical simulations for any problems involving heat and mass transfer and fluid flow. Part of my research focuses also on characterizing the thermophysical properties of complex fluids (phase change materials, slurries, nanofluids, drilling fluids, bioinspired fluids) for a wide range of applications.
Student roles: Do the literature review on ice slurries, their applications and properties; Develop a numerical model to predict the performance of ice slurry in a heat exchanger and validate it with experimental data from the literature; Perform a parametric analysis; Couple it with an optimization algoritn and optimize the heat exchanger; Write a technical report.
Skills required: Good knowledge in basic fluid dynamics and heat transfer
19. Multi-tier Cyber Risk Modeling in Digital Supply Chains using Simulation
This project focuses on modeling cyber risks across multi-tier supply chains (suppliers, distributors, logistics partners). Using simulation, it evaluates how cyber incidents propagate across tiers and impact cost, service level, and resilience. The research aims to develop a quantitative risk propagation model and identify critical vulnerabilities in complex supply networks.
Research area, student roles & skills
Research area: Supply chain management, decision making, digitalization
Student roles: Develop a multi-tier supply chain simulation model Incorporate cyber risk propagation mechanisms Define risk metrics (e.g., disruption probability, recovery time) Run scenario analyses (e.g., supplier attack, data breach) Identify critical nodes and mitigation strategies
Skills required: Supply Chain Management / Operations Research Simulation and modeling (discrete-event or agent-based) Probability, risk analysis, and stochastic modeling Basic cybersecurity awareness (especially third-party risk) Programming (Python, AnyLogic preferred)
20. Optimizing waste collection and waste-to-energy systems
This project aims to investigate new operational research models and methods that can be used to design cost-effective and environmentally-friendly integrated waste collection and waste-to-energy systems. The developed methods will enable decision-makers to select the optimal strategy to operate fleets of garbage trucks and to select, locate, and size waste-to-energy technologies (e.g., incineration, gasification, pyrolysis, anaerobic digestion) that can process the collected waste and transform it into valuable energy and downstream chemical products.
Research area, student roles & skills
Research area: My research area is applied operations research, with applications in energy, supply chain, location/allocation and technology selection problems. I use large-scale optimization (relaxation, decomposition, and approximation) and optimization under uncertainty (stochastic, robust, chance-constrained, fuzzy) methods extensively in my research, and try to incorporate machine learning models in predictive-prescriptive frameworks.
Student roles: The student will review the extensive literature on waste management optimization and identify research gaps and possible extensions. Based on that, the student will develop large-scale optimization (mathematical programming) models for waste collection and waste-to-energy that encompass multiple technology alternatives and policies, and that account for realistic considerations (e.g., budget and capacity limitations, environmental regulations, demand for downstream products, uncertainty about waste composition, operating costs, selling prices, and demand). The developed models and methods will be applied to a realistic case study and the results will be analyzed to draw useful insights and policy recommendations.
Skills required: The student should possess a strong background in mathematical optimization models and methods. Familiarity with integer and nonlinear programming techniques is an asset. It is expected that the student has some prior knowledge or interest in waste-to-energy technologies and waste management practices. A solid background in organic chemistry, physics, and mathematics is needed.
21. Quality-Aware Stochastic Inventory-Routing for Hydrogen Supply Chains with Usable-Hydrogen Service Levels
Hydrogen supply chains are often modeled using conventional mass-flow assumptions, where one kilogram of hydrogen produced is treated as equivalent to one kilogram delivered. However, hydrogen differs from traditional commodities because its delivered value depends on storage conditions, carrier form, pressure, purity, conversion losses, transportation mode, and end-use requirements. A delivery that satisfies the required mass may still be operationally inadequate if it does not meet usability requirements at the demand point.
This project will develop a quality-aware stochastic inventory-routing model for hydrogen supply chains. The model will consider a simplified multi-echelon hydrogen network with production facilities, intermediate storage, transportation links, and demand nodes such as refuelling stations, industrial users, or port operations. Demand will be uncertain, and hydrogen may be transported or stored in different forms, such as compressed hydrogen, liquid hydrogen, ammonia, or another carrier representation. Each option will have different cost, capacity, loss, and usability characteristics.
The main theoretical contribution is the development of a “usable hydrogen” inventory balance and service-level concept. Instead of measuring performance only by kilograms delivered, the project will define quality-adjusted supply measures that account for losses, conversion efficiency, delivery timing, and end-use compatibility. This enables comparison between conventional quantity-based planning and quality-aware planning.
The student will formulate a tractable stochastic optimization or simulation-optimization model and test it on stylized hydrogen supply chain instances. Policies will be evaluated using total cost, unmet usable demand, inventory loss, transportation utilization, service reliability, and quality-adjusted fill rate. The expected outcome is a publishable computational study showing when conventional hydrogen supply chain models underestimate risk and when quality-aware planning changes infrastructure, inventory, and routing decisions.
Research area, student roles & skills
Research area: This research area focuses on industrial engineering approaches for hydrogen supply chain design, planning, and operations. It applies operations research, stochastic optimization, inventory theory, transportation modeling, and simulation to support reliable and cost-effective hydrogen production, storage, distribution, and delivery. Particular attention is given to the operational challenges that distinguish hydrogen from conventional commodities, including storage losses, conversion losses, carrier selection, delivery reliability, infrastructure limitations, and end-use quality requirements such as pressure, purity, and usable energy content.
Student roles: The student will contribute to the development and testing of a quality-aware hydrogen supply chain planning model. Initial tasks will include reviewing selected literature on hydrogen supply chains, inventory-routing problems, stochastic optimization, service-level modeling, and hydrogen storage and transportation technologies. The student will identify how existing models represent hydrogen flow and where they may overlook usability-related constraints such as pressure, purity, conversion loss, storage loss, and carrier compatibility.
The student will then help define a simplified hydrogen supply chain network with production nodes, storage facilities, transportation links, and demand points. The student will formulate inventory balance equations that distinguish between physical hydrogen quantity and usable hydrogen delivered to customers. The student will also define performance metrics such as quality-adjusted fill rate, unmet usable demand, delivery reliability, inventory loss, and total operating cost.
Using Python and appropriate optimization or simulation tools, the student will implement the proposed model and compare it with a conventional mass-flow hydrogen supply chain model. Computational experiments will examine different demand uncertainty levels, storage-loss rates, carrier options, transportation capacities, and end-use quality requirements. The analysis will identify cases where quality-aware modeling significantly changes inventory, routing, or infrastructure decisions.
The student will prepare visualizations, summarize numerical results, and develop managerial insights for hydrogen supply chain planners. Regular meetings with the supervisory team will support model development, scope control, and interpretation of results. Final deliverables will include documented code, a structured set of test instances, mathematical model documentation, computational results, a technical report, presentation slides, and a manuscript-style summary suitable for development into a conference paper or journal submission.
Skills required: The student should have a background in industrial engineering, operations research, supply chain management, applied mathematics, energy systems, or a related field. Basic knowledge of optimization, probability, inventory control, and transportation modeling is expected. Python programming experience is essential. Familiarity with mixed-integer programming, stochastic modeling, simulation, or hydrogen systems would be beneficial but is not required. The student should be comfortable reading technical literature, developing mathematical models, running computational experiments, and writing technical reports.
22. Reconfigurable Layouts for Flexible Consumer Electronics Manufacturing Systems
Supervisor: Samuel Yousefi
University: Ontario Tech University (Oshawa campus)
Location: Oshawa, Ontario
Start date: 2027-05-17 (flexible)
Disciplines: Engg-Industrial, Engg-Manufacturing, Engg-Mechanical, Engg-Systems and Technology, Management Information Systems
Consumer electronics manufacturing systems are typically designed under assumptions of stable and predictable operations. However, real-world environments are highly dynamic, with variability in demand and evolving production requirements. Existing literature often evaluates facility layouts under static conditions, leaving a gap in understanding how different structural configurations perform under operational uncertainty. This project aims to address this gap by investigating how reconfigurable layouts influence system performance using a simulation-based comparative approach. In this regard, the proposed study focuses on evaluating and explaining the behavior of alternative layouts under controlled variability. In the first phase of this project, a baseline simulation model of a consumer electronics manufacturing system will be developed. This model will represent material flow, processing stages, and operational interactions. Building on this model, a set of alternative layouts will be implemented and systematically tested in the second phase. Each configuration will be evaluated under identical simulation scenarios to ensure fair comparison. The analysis will focus on key performance indicators such as material flow efficiency, congestion dynamics, and workload balance across production stages. The main contribution of this research is the development of a simulation-based comparative decision framework for evaluating the robustness of alternative facility layout designs under operational uncertainty. Therefore, the project advances facility layout design by moving beyond static approaches toward adaptive, performance-driven evaluation under realistic operating conditions. The findings will support manufacturing decision-making by providing a structured way to compare and prioritize layout configurations based on their ability to maintain performance in high-variability environments.
Research area, student roles & skills
Research area: My research focuses on three interconnected areas: (i) supply chain analytics, (ii) systems modeling, and (iii) risk and disruption management. I develop novel decision support frameworks that combine operations research and simulation modeling to help organizations make informed decisions in uncertain and rapidly changing environments. A key aspect of my work is sustainability, where I aim to design and optimize production and supply chain systems across different industries that are not only efficient and economically viable but also environmentally responsible and socially impactful.
Student roles: The student will play an active role in a structured simulation-based comparative study of production layout configurations in a consumer electronics manufacturing system. The student will first, under the supervision of the faculty, develop a baseline simulation model representing an existing production layout that captures material flow and interactions between production units. The student will then implement a set of predefined alternative layout configurations developed collaboratively by the research team (i.e., supervisor and student) and integrate them into the simulation environment for analysis. In the following step, the student will evaluate the candidate layout designs using a consistent set of simulation scenarios. This enables systematic comparison among layouts in terms of several performance indicators (e.g., material flow efficiency). During the comparison process, the student will record output data across multiple simulation runs to ensure reliable performance assessment. This will help the research team examine how differences in layout structure influence system behavior, with particular attention to flow stability, bottleneck formation, and the propagation of delays across the system. In the final step, the student will interpret simulation results under the supervision of the faculty to uncover underlying structural patterns that explain differences in system performance. This includes linking layout design features to observed operational outcomes, providing exposure to analytical research thinking beyond implementation tasks. Through this project, the student will also gain hands-on experience in manufacturing systems simulation modeling and systems-level performance assessment under structured conditions. By the end of the internship, the student will develop skills in descriptive and predictive analytics and gain a strong understanding of how design choices influence operational efficiency and manufacturing system stability.
Skills required: The ideal candidate is an undergraduate student in industrial engineering, manufacturing engineering, mechanical engineering, or a related field with strong analytical and quantitative skills. A solid understanding of manufacturing systems and basic facility layout design concepts is required. Familiarity with computational tools such as Excel is expected. Prior exposure to simulation software (e.g., Arena or similar platforms) is an asset. The student should be comfortable interpreting system outputs, working with structured data, and applying systems-thinking to analyze operational performance.
23. Suivi de la production d'usinage du bois à l'aide de l'inspection par caméra et du contrôle d'usure des outils
Le projet en sera un de recherche et développement et sera réalisé en collaboration avec le personnel des entreprises partenaires, des chercheurs et des étudiants gradués. L'équipe de soutien étant interdisciplinaire. L'étudiant sera amené à travailler à créer et développer les systèmes manufacturiers de demain. Les outils et prototypes développés sont testés au sein de notre usine universitaire pleinement fonctionnelle avant d'être implantés chez le partenaire industriel. Il sera amené à travailler à la mise en place de systèmes industriels axés vers la production sur-mesure automatisée de produits designés et conçus par le client.
Dans le cadre de ce projet, nous nous intéressons au suivi de la production d'usinage de panneaux de bois. Après la découpe à l'aide d'une CNC, nous visons à valider que les pièces découpées sont les bonnes. Pour ce faire, nous déploierons un système de vision artificielle par caméras afin de vérifier la conformité et la précision des découpes. Ce système utilisera des modèles d'intelligence artificielle issus de la littérature. De plus, comme les outils de coupe s'usent progressivement en fonction du type d'usinage et du volume d'utilisation, un module de suivi de l'outillage sera intégré. Ce dernier permettra de garantir que chaque outil est encore opérationnel et performant avant de lancer la découpe suivante.
Research area, student roles & skills
Research area: La recherche au Lab-Usine se situe au niveau de l'ingénierie automatisée à base d'intelligence artificielle et de modèles mathématiques, à des systèmes de production auto-reconfigurables avec lancement automatique de la production (planification automatisée des opérations en temps réel) et suivi de la production s'appuyant sur la science des données, l'internet des objets, la réalité augmentée et les jumeaux numériques.
Student roles: Prendre connaissance des systèmes en place (MES, CNC, Caméras) (sem 1-2) Établir un plan de suivi d'usure des outils. (sem 3) Développement du système de suivi des outils. (sem. 4-5) Se familiariser avec l'acquisition des données à l'aide des caméra au Lab-Usine (sem 6-7) Établir et mettre en place le suivi de qualité des pièces à l'aide des caméras (sem 8+)
Le stagiaire sera sous la responsabilité du directeur du Lab-Usine (Jonathan Gaudreault) pour minimum une rencontre par semaine ou au besoin et recevra le support d’un professionnel de recherche et d’un étudiant gradué sur une base quotidienne.
Skills required: Nous recherchons un(e) étudiant(e) autonome et débrouillard(e), possédant une bonne maîtrise de la programmation et capable de s’adapter rapidement à de nouveaux outils et concepts. Il ou elle devra faire preuve d’une réelle volonté d’apprentissage tout au long du projet.
This project investigates printing parameters and innovative composites affects on mechanical performance, durability, and manufacturability of 3D‑printed parts. Innovative composite filament/pellets are manufactured by combining virgin and recycled plastics, plant fibres, tire, and targeted additives through extrusion or die-cast molding. Commercialization potental of both composite filaments/pellets will be examined.
Both Fused Filament Fabrication (FFF) and pellet‑fed additive manufacturing will be explored to print samples for material characterization. Material analysis and characterization will follow ASTM standards for tensile, flexural, compression, degradation and moisture absorption. Optical microscopy of composites may also be conducted. In addition to varying omposition, changing printing parameters (like infill density, orientation and layer thickness) will also be analyzed. Finite Element Analysis on 3D printed specimen will be validated with experimental results. Commercial products from the innovative composite will be identified, designed, and optimized through FEA analysis, functionality, ergonomics, manufacturability and research teams’ feedback. As part of commecialization, market viability, industrial adoption barriers, regulatory requirements, and raw‑material supply‑chain resilience, recycled feedstock logistics and quality control will be examined.
The project will train high‑quality personnel (HQP) through practical experience in materials processing, ASTM testing, CAD/FEA, and pilot‑scale printing; mentorship in industry engagement; and knowledge in Canadian regulatory and commercialization processes. Intern will gain competencies associated with Canadian priorities in circular economy, and sustainable manufacturing that supports Canada’s 2030 goal to achieve zero plastic waste and help foster advances in manufacturing. Outcomes include validated composite formulations, optimized 3D-printing manufacturing, developing market-ready prototypes, and initiation of commercialization for industry adoption and success.
Research area, student roles & skills
Research area: My research successes apply mechanical engineering principles to design, analysis, dynamics, vibration, instrumentation, medical/sports technologies, soil-tool interaction, AI applications, materials science, safety systems, and lean manufacturing. Recently, valorizing waste streams (i.e. crop residue, tires, and polymers) into innovative composites and commercial products to promote circular economies benefitting consumers, industries, and the environment has occurred. This will generate secondary income and support sustainable agricultural practices. New ventures in plasticizing natural fibers and additive manufacturing using reclaimed materials are integral to my research My expertise fosters strong interdisciplinary collaboration with academic and industry partners, advancing innovation, knowledge mobilization and successful commercialization.
Student roles: The intern will be an integral member of a dynamic research team committed to advancing Canada’s Zero Plastic Waste Agenda at the University of Regina. The intern will report to the principal investigator and research assistant and contribute to the development of sustainable materials, additive manufacturing and gain valuable practical experience in engineering research that has real-world impact.
Intern onboarding training includes lab orientation and training certifications in WHIMS, UofR Health and Safety, Engineering Safety, chemical lab safety, and TCPS 2 :CORE. Other professional development and training opportunities will be available. The intern will participate in composite material preparation (chemical treatment and extrusion), CAD modelling specimens as per ASTM standards and 3D printing samples (with pellet and/or filaments). The intern will be trained and assist in conducting tensile, flexural, compression, degradation and moisture absorption tests according to ASTM standards; analyze and compare the results with FEA. The intern will complete CAD prototype modelling and iterative design refinement based on functionality, manufacturability and feedback. The intern will gain experience in completing Research ethics board applications and surveys. The interns will support market, regulatory, and supply‑chain assessments, contribute to cost modelling and commercialization planning.
The intern will participate in the weekly research meeting, presenting their progress and contributing to scientific discussions. In the last week of the research, the intern will prepare a final report and presentation summarizing methods, findings and future recommendations.
Skills required: Required background: mechanical, materials science or related engineering, with previous experience in 3D printing (FFF/pellet), polymer/composite manufacturing, extrusion, and ASTM materials testing. Proficiency in CAD (e.g., SolidWorks), FEA (ANSYS) and relevant 3D printing software (Splicing and G-code software). Strong laboratory skills, attention to safety and quality control; comfortable with Microsoft 365 platform for documentation and data analysis. Excellent communication (verbal and written), teamwork, and interest in sustainability and circular‑economy practices.
25. Sustainable Hydrogen Supply Chain Network Design for Net-Zero Transition
Supervisor: Samuel Yousefi
University: Ontario Tech University (Oshawa campus)
Hydrogen is considered an important energy carrier for decarbonizing sectors such as heavy industry and long-distance transportation, where electrification through the power grid may be challenging. However, the successful deployment of hydrogen infrastructure depends on the design of supply chain networks that integrate production, storage, and distribution into a coherent and scalable system. This project aims to develop an analytical approach for hydrogen supply chain network design. The research will first identify the key components of this type of supply chain, including production facilities, storage nodes, transportation links, and demand regions. Based on this network representation, optimization modeling will be used to formalize strategic design decisions under practical operational and infrastructural constraints. This model will incorporate key supply chain considerations such as capacity limitations, demand satisfaction requirements, and transportation feasibility between network layers. The proposed approach will then be applied to a small-scale hydrogen supply chain network design problem to determine optimal facility location decisions and material flow allocations between existing and newly established facilities, while minimizing total cost. Beyond numerical optimization results, the project will investigate how supply responsibilities are allocated across different tiers of the network, how flows are distributed through alternative routes, and how infrastructure components interact to shape overall system performance. Overall, the project will contribute to a more systematic understanding of hydrogen supply chain planning by combining optimization modeling with practical network design considerations. The findings are expected to provide insights into efficient hydrogen network configuration and support future clean energy infrastructure planning initiatives.
Research area, student roles & skills
Research area: My research focuses on three interconnected areas: (i) supply chain analytics, (ii) systems modeling, and (iii) risk and disruption management. I develop novel decision support frameworks that combine operations research and simulation modeling to help organizations make informed decisions in uncertain and rapidly changing environments. A key aspect of my work is sustainability, where I aim to design and optimize production and supply chain systems across different industries that are not only efficient and economically viable but also environmentally responsible and socially impactful.
Student roles: The student will play an active role in developing an analytical approach for analyzing a hydrogen supply chain network. The student will begin by identifying the key members of the supply chain (e.g., production facilities and demand regions) and defining the overall network structure in the Canadian context. Based on this structure and a set of assumptions, the student will define a network design problem under the supervision of the faculty. This includes identifying key decision variables and associated operational constraints. The constraints will be derived from literature and publicly available reports and will typically include capacity limitations, demand satisfaction requirements, and feasible connectivity between network components. The research team (i.e., supervisor and student) will then formulate the problem as an optimization model that captures strategic and tactical decisions. Strategic decisions refer to long-term infrastructure choices (e.g., location of facilities), while tactical decisions relate to medium-term decisions (e.g., distribution of flows). The model will include an objective function aimed at minimizing the total cost of these decisions while ensuring efficient hydrogen supply chain operations. Next, the student will implement the model using an appropriate optimization tool and apply it to a small-scale case study or numerical example. In the final step, the student will interpret the results and develop an understanding of how to derive practical and managerial implications for hydrogen supply chain design and planning. This project will provide the student with exposure to both optimization modeling and systems-level thinking. By the end of the project, the student will be able to implement optimization-based solutions and develop prescriptive analytics skills to support decision-making in energy supply chain design and planning.
Skills required: The ideal candidate is an undergraduate student in industrial engineering, energy engineering, civil engineering, or a related field with strong analytical and quantitative skills. A basic foundation in optimization modeling is required. Experience with optimization solvers such as CPLEX, Gurobi, or similar tools is beneficial. Familiarity with supply chain systems and engineering decision-making is considered an asset. The student should be motivated to work on decision-making and planning problems, as well as interpret model outcomes.