Rolling horizon data driven robust optimization for supply chain planning

This paper discusses the challenge of production planning in a dyadic supply chain, where uncertainties disrupt plans that are updated through a rolling horizon DRP process. These uncertainties, such as demand fluctuations, machine failures, and delivery delays, cause instability and worsen the bullwhip effect, which reduces the reliability and effectiveness of production plans. To address these challenges, we propose a data-driven robust optimization framework that uses historical data analysis and clustering techniques to create well-defined uncertainty sets. Numerical experiments, using simulated historical data, show that this approach improves supply chain planning by balancing precision and robustness in managing uncertainties.