Graduate Student Research Seminar Day 鈥� June 17, 2026
You are cordially invited to the听Graduate Student Research Seminar听of the Department of Industrial Engineering.
Date:听Wednesday, June 17 2026
Time:听1:00 pm - 2:40 pm AST
In Person:听Room TBD, Sexton Campus
Online:听In-person only
(NOTE:听 Students are reminded that they must attend in person if they are planning to put this toward their Seminar course requirements.)
Schedule:
1300-1325 |
Megan Wiggins, PhD Candidate (Online) Integrating Individual-Level and System-Level Modelling to Evaluate Waitlist Management Strategies for Continuing Care Homes in Alberta, Canada |
| 1325-1350 | Farzad Falahaty, MASc. student |
| 1350-1415 | Maryam Schoghi, MASc. student Network Design for Cultured Meat Supply Chains |
| 1415-1440 | Ian Clarke, MASc. student Mixed Integer Linear Programming Formulations for Naval Maintenance Planning: Work Period Optimization & Multi-Ship Extension with Resource Leveling |
| 1440-1505 | Soroush Safavi, PhD Candidate听 From Simple Rules to Learning-Based Control: Managing Perishable Blood Inventories under Sparse Demand |
Abstracts:
Integrating Individual-Level and System-Level Modelling to Evaluate Waitlist Management Strategies for Continuing Care Homes in Alberta, Canada |
| Megan Wiggins, PhD Candidate (Online) |
| Life expectancy is increasing worldwide, placing growing pressure on health systems as demand for services rises. Compared with the general population, older adults are more likely to experience multimorbidity and complex care needs, contributing to higher utilization of health care services. As care needs progress beyond what can be safely managed in community settings, timely and appropriate placement in continuing care homes becomes a critical component of the care continuum. However, long waitlists and limited continuing care home capacity remain persistent challenges, and placements are complicated by the diverse care needs and personal circumstances of individuals waiting for placement. These individual-level dynamics interact with system-level capacity and flow constraints, creating trade-offs that unfold over time. Addressing this complexity requires an understanding of how health progression at the individual level influences, and is influenced by, waitlist management decisions. This research proposes a framework for evaluating assessment and admission strategies in Alberta's continuing care system that integrates individual health progression with system-level capacity and flow dynamics. Using administrative health data, statistical models will characterize changes in health status over time and estimate the risk of key health events (emergency department visits, hospitalizations) for individuals on continuing care waitlists and within continuing care homes. These models will form the basis of a simulation framework representing individual health trajectories, which will subsequently be embedded within a discrete event simulation of continuing care capacity and patient flow, enabling evaluation of waitlist management strategies with respect to both individual health outcomes and overall system performance. |
听 Engineering cooperation: A multi-stage game-theoretic framework for transforming host-displaced relations through shared resource investment |
| Farzad Falahaty, MASc. student |
| Mission-critical systems in sectors such as aerospace, defence, transportation, petrochemistry, and power generation require high reliability to prevent failures causing major economic losses, environmental damages, and safety risks. For such systems, solving the selective maintenance problem (SMP) yields optimal maintenance planning decisions during scheduled breaks. Its extension, the multi-mission SMP (MMSMP), focuses on optimizing component maintenance, maintenance levels, and repairperson assignments over multiple consecutive missions interspersed with maintenance breaks. While recent advances integrate predictive, resource-constrained, and fleetwide strategies, they rely on the unrealistic assumption of fixed budgets, ignoring the reality of fluctuating and tight financial constraints faced by planners. This study investigates how different maintenance budget allocations across missions affect system performance. Using a two-phase decomposition model and binary integer programming, it explores various budget distribution strategies: uniform, linearly increasing, and inverted-V. The goal is to determine how allocating resources differently across missions can enhance asset reliability within fixed budget limits. The findings aim to guide maintenance planners in making budget decisions to improve overall system reliability while balancing resource constraints. |
听 Network Design for Cultured Meat Supply Chains |
| Maryam Schoghi, MASc. Student |
| This seminar presents two complementary contributions. First, a systematic literature review synthesizing 79 studies on optimization under uncertainty in sustainable agri-food supply chains (2015鈥�2025) shows that while robust optimization and stochastic programming methodologies are well-established, emerging domains like cultured meat and circular agri-food networks remain understudied. The review identifies critical gaps: limited multi-dimensional uncertainty modeling, under-representation of quality and shelf-life dynamics, and a predominant focus on cost minimization rather than triple-bottom-line sustainability. Second, a preliminary mixed-integer linear programming model for cultured meat network design addresses these gaps by: (i) replacing continuous amino acid blending with discrete, empirically-validated media recipes that reflect actual cell-growth efficiency; (ii) incorporating multi-modal transportation (truck, rail, ship) with mode-specific capacities, costs, and disruption vulnerabilities; and (iii) integrating on-site circular cell culture algal biorefinery operations for spent-media recycling and nutrient recovery. The model targets 100 million kg annual production and serves as a robust baseline for introducing stochastic and robust optimization layers in future research. This work bridges bioprocessing accuracy with supply chain optimization, advancing decision-support tools for an emerging sector. |
听 Mixed Integer Linear Programming Formulations for Naval Maintenance Planning: Work Period Optimization & Multi-Ship Extension with Resource Leveling |
| Ian Clarke, MASc. student |
| The planning of maintenance tasks for naval surface ships presents a complex scheduling problem. Work period planning at the operational level considers resource and precedence constraints when scheduling maintenance tasks. Tactical level planning considers a longer horizon where periodic maintenance tasks are assigned to pre-defined work periods. At this level, due dates, labour capacity, and certification requirements must be respected. This presentation explores mixed integer linear programming (MILP) optimization models that address both the operational and tactical planning levels. 听The first model builds on a resource-constrained project scheduling problem (RCPSP) formulation that optimizes a work period schedule for a single ship. This formulation used a priority-duration (PD) objective to frontload high priority, long duration tasks. Schedule quality was then evaluated using the duration weighted centroid (DWC) as a post-solution metric. Our model optimizes the DWC explicitly as a MILP objective. Experiments show that the DWC objective improves frontloading of schedules compared to the PD objective, confirming the hypothesis that the DWC is a better optimization target. The second model is a multi-ship extension of an existing constraint programming (CP) formulation for tactical level preventive maintenance scheduling. First, we reformulate the single-ship CP model as a MILP. Next, we extend the model to multiple ships and incorporate a resource leveling term based on job categories. To reflect the shared resource environment of multiple ships competing for labour resources, the model penalizes concurrent scheduled tasks per job category across overlapping work periods. |
From Simple Rules to Learning-Based Control: Managing Perishable Blood Inventories under Sparse Demand |
| Soroush Safavi, PhD Candidate |
| This presentation introduces a decision-support framework for managing perishable blood inventories under sparse and zero-inflated demand, with Low-Titer O Whole Blood as the motivating healthcare application. The research addresses a central operational tension: hospitals must maintain readiness for rare but urgent trauma demand while limiting wastage of short-shelf-life blood products. The first study evaluates classical (s,S) policies under Zero-Inflated Poisson demand and identifies when simple stock-based control remains reliable. The second study develops a scenario-based rolling horizon approach that uses short-term demand scenarios and age-structured inventory information to improve decisions in intermediate-demand settings. The third study compares Approximate Dynamic Programming and Proximal Policy Optimization against a stochastic dynamic programming benchmark. Overall, the findings show that no single method dominates all demand regimes. Simple policies are suitable when demand is sufficiently frequent, rolling horizon optimization improves performance when age information becomes important, and model-based ADP provides the most stable learning-based approach under highly sparse demand. |
Contact Person:
Hamid Afshari, Ph.D., P.Eng.
email:听hamid.afshari@dal.ca听