Hello, I'm Joël Mongeon

PhD Student in Bioresource Engineering

I'm at the Sustainable Agri-Food Systems Engineering Lab (SASEL) at McGill University, under the supervision of Dr. Ebenezer Miezah Kwofie in the Department of Bioresource Engineering.

My core research centers around supply chain optimization under network uncertainties in global and local value chains. I focus on developing computational and mathematical models to enhance the resilience, sustainability and efficiency of trade systems via complex emergent insights.

My research lies at the intersection between operations research, system dynamics, machine learning, and complexity science, with a core focus on developing self-adaptive and robust decision-making models for sustainable and resilient value chains.

I develop quantitative and computation models that integrate uncertainty, real-world constraints, and causal relationships. Some of my most recent models include the following:

• Sustainability-Resilience Quantification: Leveraging graph theory and network analysis, hybrid LCA models and Markov Chains to simultaneously model, compute and optimize the inherent sustainability-resilience trade-off of value chain operations across different value chain resilience stages.
• Stochastic Modeling & System Dynamics: Employing probabilistic models (e.g., Bayesian Networks, Monte Carlo Sampling, and Agent-Based Modeling) within system dynamic models to simulate the cascading effects of agentic decisions during disruptive events on value chains.
• Prospective Network Topological Analysis: Utilizing hypergraph theory and geospatial analysis to assess the impact of radical value chain topological shifts (e.g., protectionist trade policies) on value chain performance and resource competition.
• Value Chain Modeling and Optimization: Leveraging insights from neural networks, multiobjective optimization, and time-series models to simulate and optimize value chain operations across multiple dynamic real-world constraints and performance objectives.
• Adaptive Decision-Making: Developing hybrid machine learning frameworks to create proactive sustainable and resilient adaptation in value chain models.

My work is motivated by the critical need to design intelligent, resilient and antifragile systems that can plan, adapt and reason under uncertainty to address various value chain challenges.

Research Interests

• Supply Chain Optimization & Logistics
• Stochastic and Robust Optimization
• System Dynamics & Complex Socio-Technical and Socio-Ecological Systems
• Graph Theory & Network Science
• Bayesian Networks & Causal Inference
• Evolutionary and Reinforcement Learning
• Life Cycle Assessment Refinement
• Multi-Criteria Decision Making
• Complexity Science