PhD Oral Exam - Mahsa Valipour, Information and Systems Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

The increasing complexity and volatility of supply chains (SCs) underscores the importance of resilient and adaptive network design, particularly in the energy sector, where disruptions can compromise both security and continuity. Conventional static configurations are insufficient to handle several uncertainty sources and systemic interdependencies that can propagate failures across the network. As the global energy landscape transitions from fossil fuels to renewable sources, energy SCs must evolve into dynamically reconfigurable systems capable of continuous adaptation to market fluctuations and climate-induced disruptions.

Given the need to address these challenges and leverage opportunities offered by bioenergy production, this thesis develops simulation-driven frameworks for modeling adaptive and resilient biomass SC networks. The research begins with quantifying disruptions and their propagation across the network, where graph-theoretic analysis is combined with simulation to identify structural and operational vulnerabilities and measure cascading effects. In this setting, contingency plans are dynamically triggered, depending on the scale and nature of disruptions. This is followed by extending the digital modeling environment with an optimization layer that accounts for multiple sources of operational risks and supports adaptive replenishment planning under inventory policies across several nodes. In the third objective, the optimization layer is extended into a bi-level hierarchical model. At the upper level, multi-echelon production planning is optimized to align biomass supply availability with community energy demand under uncertainty. At the lower level, the model incorporates multi-modal transportation planning, taking into account heterogeneous fleet capacities. The two levels are connected through a coordinated network enabled by information-sharing mechanism. This thesis is applied to a case study of remote off-grid communities in Quebec, where the objective is to integrate bioenergy, which is a locally available energy resource, into existing diesel-based energy networks under logistical constraints, including seasonal accessibility restricted to a limited navigable waterway.

By unifying disruption propagation analysis, resilience quantification, and adaptive risk-aware planning within a digital modeling paradigm, this thesis advances the development of next-generation SC networks that are structurally robust and operationally agile in the face of multidimensional uncertainty. Moreover, the research brings together ideas from simulation modeling, derivative-free optimization, and natural resource management, which can foster the development of resilient, sustainable, and affordable energy sources while promoting equity within communities.