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 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 blends identified and the suppliers selected by both models are different. The cost of the centralized supply chain is 2%–6% lower. The implications of these results are twofold. First, these results could lead to improved collaborations in the supply chain. Second, these results provide an estimate of the approximation error from assuming centralized decision making in the supply chain.

中文翻译：

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分散供应链中的随机生物量混合问题

混合具有不同物理或化学性质的生物质材料提供了调整原料质量以满足转化平台规格的机会。我们提出了一个模型，该模型可以确定正确的生物质混合物，从而以最小的成本优化热化学转化过程的性能。这是一个机会约束规划（CCP）模型，其中考虑了生物质质量的随机性。提出的CCP模型可确保大部分时间都满足受生物质的物理和化学特性影响的过程要求。我们考虑两个问题设置，即集中式供应链和分散式供应链。我们提出了一个混合整数线性程序来模拟集中设置中的混合问题，并提出了一个双层程序来模拟分散设置中的混合问题。我们使用样本平均近似方法来近似机会约束，并提出求解算法来解决该近似问题。我们使用十亿吨研究提供的数据为南卡罗来纳州开发了一个案例研究。根据我们的结果，确定的混合物主要包括松木和针叶木残留物。两种模型确定的混合物和选择的供应商都不相同。集中式供应链的成本降低了2％–6％。这些结果的含义是双重的。首先，这些结果可能会改善供应链中的协作。第二，