  Renewable energy policies have driven the wood pellet market over the last
decades worldwide. Among other factors, the return from this business depends
largely on how well the producers manage the uncertainty associated with
biomass yield and quality. This study develops a two-stage stochastic
programming model that optimizes different critical decisions (e.g.,
harvesting, storage, transportation, quality inspection, and production
decisions) of a biomass-to-pellet supply system under biomass yield and quality
uncertainty. The goal is to economically produce pellets while accounting for
the different pellet standards set forward by the U.S. and European markets. We
propose two parallelization schemes to efficiently speed up the convergence of
the overall decomposition method. We use Mississippi as a testing ground to
visualize and validate the performance of the algorithms.
