  Blending biomass materials of different physical or chemical properties
provides an opportunity to adjust the quality of the feedstock to meet the
specifications of the conversion platform. We propose a model which identifies
the right mix of biomass to optimize the performance of the thermochemical
conversion process at the minimum cost. This is a chance-constraint programming
(CCP) model which takes into account the stochastic nature of biomass quality.
The proposed CCP model ensures that process requirements, which are impacted by
physical and chemical properties of biomass, are met most of the time. We
consider two problem settings, a centralized and a decentralized supply chain.
We propose a mixed-integer linear program to model the blending problem in the
centralized setting and a bilevel program to model the blending problem in the
decentralized setting. We use the sample average approximation (SAA) method to
approximate the chance constraints, and propose solution algorithms to solve
this approximation. We develop a case study for South Carolina using data
provided by the Billion Ton Study. Based on our results, the blends identified
consist mainly of pine and softwood residues. The cost of the centralized
supply chain is 2 to 6% lower, which shows that the assumption of centralized
decision making leads to underestimating costs in the supply chain.
