Sustainable network design for multi-purpose pellet processing depots under biomass supply uncertainty

This study develops a two-stage stochastic mixed-integer programming model to manage multi-purpose pellet processing depots under feedstock supply uncertainty. The proposed optimization model would help to minimize cost and mitigate emissions from the supply chain network. We consider three alternative Biomass Processing and Densification Depot (BPDD) technologies; namely, conventional pellet processing, high moisture pellet processing, and ammonia fiber expansion. These three technologies pre-process/pre-treat and densify different types of biomass into more highly densified intermediate products for different markets in order to improve movability and overall supply network performance in terms of costs and emissions. A hybrid decomposition algorithm was developed that combines Sample Average Approximation with an enhanced Progressive Hedging (PH) algorithm to solve this challenging NP-hard problem. Some heuristics such as Rolling Horizon (RH) heuristic, variable fixing technique were later applied to further enhance the PH algorithm. Mississippi and Alabama were selected as a testing ground and ArcGIS was employed to visualize and validate the modeling results. The results of the analysis reveal promising insights that could lead to recommendations to help decision makers achieve a more cost-effective environmentally-friendly supply chain network.