  Planning of biorefinery operations is complicated by the stochastic nature of
physical and chemical characteristics of biomass feedstock, such as, moisture
level and carbohydrate content. Biomass characteristics affect the performance
of the equipment which feed the reactor and the efficiency of the conversion
process in a biorefinery. We propose a stochastic optimization model to
identify a blend of feedstocks, inventory levels, and operating conditions of
equipment to ensure a continuous flowing of biomass to the reactor while
meeting the requirements of the biochemical conversion process. We propose a
sample average approximation (SAA) of the model, and develop an efficient
algorithm to solve the SAA model. A feedstock preprocessing process consists of
two-stage grinding and pelleting is used to develop a case study. Extensive
numerical analysis are conducted which lead to a number of observations. Our
main observation is that sequencing bales based on moisture level and
carbohydrate content leads to robust solutions that improve processing time and
processing rate of the reactor. We provide a number of managerial insights that
facilitate the implementation of the model proposed.
