Balanced Uncertainty Sets for Closed-Loop Supply Chain Design: A Data-Driven Robust Optimization Framework with Fairness Considerations

Recycling is crucial for minimizing the environmental impact of plastic waste and plays a key role in sustainable supply chains. However, optimizing these networks is challenging due to uncertainties in parameters such as demand and return rates. This research focuses on designing robust Closed-Loop Supply Chain (CLSC) networks to enhance resource efficiency, reduce costs, and increase recycling rates, making the system more resilient and sustainable. A novel Data-Driven Robust Optimization (DDRO) approach is developed, incorporating historical data to manage uncertainties, such as fluctuating demand and return rates. The research introduces the concept of “balanced uncertainty sets,” where boundaries are equidistant from uncovered data, ensuring a fair and true representation of uncertainty. A Kernel Weight Adjustment (KWA) approach is proposed to balance uncertainty sets across varying levels of conservatism. Additionally, a Robust Fairness (RF) index is proposed to evaluate the balance of uncertainty sets, and a data-driven algorithm is developed to compute the RF index efficiently. Numerical results show that the developed DDRO approach generates more balanced uncertainty sets, and the RF index allows for effective comparison without added computational complexity.