Research on a Multi-period Cutting Stock Problem Considering Demand Uncertainty and Leftover Reuse

Improving utilization and reducing waste during material-cutting operations is crucial for cost efficiency and sustainability. This study addresses a multi-period cutting stock problem (MCSP) considering both demand uncertainty and leftover reuse. We develop a two-stage stochastic mixed-integer programming model to holistically optimize production planning across multiple periods. The framework incorporates: scenario-based demand representation, adaptive leftover inventory management, and reprocessing of residual materials in subsequent periods. To solve the model, we propose a branch-and-price algorithm enhanced with a rolling horizon strategy to handle real-time demand fluctuations. Numerical experiments using real-life industrial data from a film manufacturer demonstrate significant improvements.