A stochastic decision-support model for circular supply chain performance under energy imbalance

The integration of circular economy (CE) principles into supply chains is increasingly affected by renewable energy intermittency, grid instability, and energy price volatility. This study develops a multi-stage stochastic optimization framework to coordinate production, recycling, inventory, and energy procurement decisions in a closed-loop supply chain under uncertainty. The model integrates circular material flows, energy imbalance constraints, renewable-priority requirements, and carbon-cost considerations within a scenario-based decision structure. The framework is demonstrated through a lithium-ion battery recycling case study calibrated to operational conditions in California. Results show that, relative to a deterministic benchmark, the stochastic model improves emissions performance and recycling outcomes, while shifting the composition of energy-related costs. The analysis further shows that total system cost can decline when adaptive recourse reduces penalties and improves operational coordination, even when direct energy expenditure rises under some scenarios. To ensure reproducibility while respecting confidentiality restrictions, the study uses a synthetic dataset designed to replicate the key statistical properties of the operational data; the paper provides a clearer validation protocol for this synthetic data and for scenario generation. A final scenario-based industry-scaling analysis is presented as an illustrative extension rather than a predictive forecast. The study contributes an integrated decision-support framework for analyzing the interaction between energy volatility and circular supply chain performance under uncertainty.