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Simulation-Driven Decision Support Frameworks for Designing Resilient and Adaptive Bioenergy Supply Chain Networks

Title:

Simulation-Driven Decision Support Frameworks for Designing Resilient and Adaptive Bioenergy Supply Chain Networks

Valipour, Mahsa (2025) Simulation-Driven Decision Support Frameworks for Designing Resilient and Adaptive Bioenergy Supply Chain Networks. PhD thesis, Concordia University, Concordia Institute for Information Systems Engineering.

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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.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (PhD)
Authors:Valipour, Mahsa
Institution:Concordia University, Concordia Institute for Information Systems Engineering
Degree Name:Ph. D.
Program:Information and Systems Engineering
Date:16 October 2025
Thesis Supervisor(s):Mafakheri, Fereshteh and Wang, Chun
Keywords:Renewable Energy, Supply Chain Analytics, Logistics Planning, Risk and Disruption Management, Simulation-based Optimization
ID Code:996609
Deposited By: Mahsa Valipour
Deposited On:29 Jun 2026 17:55
Last Modified:29 Jun 2026 17:55

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