A Data-Driven Optimization Framework for the Design and Operation of Adaptive and Resilient Energy Supply Chain Networks under Uncertainty

Recent geopolitical disruptions and extreme weather events have underscored the importance of resilience in global energy supply chains, particularly for import-dependent economies pursuing ambitious energy transition targets. These events have exposed the limitations of supply chain designs focused solely on cost minimization that lack the flexibility and redundancy required for secure operation under stress. As energy systems evolve toward higher shares of variable renewable energy and increased demand uncertainty, episodic manual re-planning becomes inadequate, highlighting the need for modeling frameworks that integrate predictive modeling, optimization, and control to enable intelligent and adaptive supply-chain design and operations under uncertainty. This work presents a comprehensive data-driven modeling and optimization framework for adaptive energy supply-chain networks under evolving demand. The framework integrates three layers: (i) a machine-learning model for demand forecasting and scenario generation; (ii) a multi-period stochastic optimization model for strategic network design and operations; and a (iii) learning layer that monitors performance metrics and triggers strategic recourse when demand patterns shift significantly. The operational stage, which acts as a learning layer, is posed as a rolling horizon control problem determining necessary recourse decisions to adapt to changes in demand patterns. An illustrative case study, encompassing multiple energy generation hubs, energy carriers and transportation modes is shown to demonstrate the applicability of the framework.