Biomass inventory optimization for willow-based pellet production integrating seasonal supply and stochastic demand

Abstract

Biomass supply chains face both internal challenges (low energy density, bulkiness, seasonality) and external challenges (market uncertainty). We developed a two-stage stochastic mixed-integer linear programming (MILP) model that configures an (s, S) inventory control policy for a willow-based pellet production facility. This model accounts for seasonal willow harvesting capacity and stochastic daily wood pellet demand. Using a case study of a proposed wood pellet production facility in Schenectady County, New York, USA, we evaluated model-suggested inventory control policies using multiple out-of-sample pellet demand scenarios by assuming a moderate demand uncertainty with a 10% coefficient of variation. Inventory policies acquired from modeling stochastic demands resulted in a 1% mean cost reduction compared to the deterministic approach. More importantly, these policies reduced cost variability by 44% under moderate demand uncertainty. We expected the performance advantage of the stochastic model to increase as uncertainty levels rise. Test cases show that frequent inventory policy adjustments provide additional cost savings. The model successfully accounted for seasonal supply constraints and stochastic market demand to facilitate a multi-feedstock strategy that offers additional supply chain resilience and associated cost reduction. Overall, the stochastic modeling framework provides facility managers with more robust inventory planning under real-world constraints. The framework’s computational efficiency and broad applicability make it suitable for adoption by diverse biomass industries with uncertainties in their supply chains, particularly those facing seasonal feedstock supply constraints and stochastic end product demands.