A risk-averse multi-objective analytics framework for green supply chain design under uncertainty

In response to increasing concerns about environmental issues, businesses, and industries face pressure to mitigate their negative environmental impacts. Consequently, firms must reevaluate their operations to align with environmental standards. To address both economic and environmental objectives, industries need to green their supply chains. However, uncertainties in the real world, such as economic instability, add complexity to this greening process. This study proposes a novel risk-averse two-stage stochastic model for green supply chain (GSC) design under uncertainty, integrating Conditional Value at Risk (CVaR) with multi-objective programming. The model uses discrete Fuzzy Random Variables (FRVs) to capture both randomness and fuzziness in cost and emission parameters. To solve the model, we apply possibility theory and Fuzzy Chance-Constrained Programming (FCCP) to derive deterministic equivalents for optimistic, pessimistic, and hybrid decision-making attitudes. Numerical results from a flour supply chain case in Iran show that higher risk aversion increases both cost and CO₂ CVaR, while possibility levels affect outcomes differently across models. The approach provides managers with a flexible tool for balancing economic and environmental goals under uncertainty.