Supply chain planning problems have multiple interconnected layers of decision-making. In paper manufacturing, these layers include production scheduling, trimming decisions, vehicle loading, multi-period demand fulfillment, and inventory management. To solve the fully integrated problem at industrial scale, we develop a hybrid method combining column generation and dynamic programming for the supply-side decisions with Benders decomposition for downstream fulfillement. On real instances from a major North American paper manufacturer, the proposed approach lowers total costs by 24.4% compared to a traditional CG-DP on challenging eight-week planning problems.
