Sustainable Timber Supply Chain Optimization

Abstract

The U.S. timber supply chain faces mounting challenges related to capacity constraints, sustainability, and supply resilience at a time when federal policy calls for a rapid expansion of domestic timber production. Following the March 2025 executive order to reduce reliance on foreign timber imports, achieving near-term production targets requires a nationwide redesign of supply chain infrastructure under significant data and operational uncertainty. This study develops a data-driven optimization framework to support short-term, actionable planning for the U.S. timber supply chain. We propose a hybrid machine learning–mixed-integer linear programming (ML–MILP) model that captures the flow of timber from mills through distribution centers to demand points, with the objective of minimizing total transportation and facility-opening costs. U.S.–wide implementation is complicated by incomplete and fragmented data, particularly for mill counts, production levels, and facility locations. To address these gaps, we leverage machine learning models, including gradient boosting, ridge regression, and weighted K-Means clustering, to reconstruct a comprehensive national dataset and generate candidate distribution center locations informed by socioeconomic and environmental factors. The resulting MILP generates an infrastructure and flow plan and is evaluated through sensitivity and scenario-based analyses reflecting demand growth, transportation disruptions, and disaster impacts. Results highlight the dominant role of transportation costs, diminishing returns to capacity expansion, and heightened vulnerability in the South and West regions. Overall, the proposed framework provides policymakers and industry stakeholders with a scalable, sustainability-oriented decision-support tool for guiding domestic timber supply chain expansion under evolving policy objectives.