Biomass supply chain performance is heavily affected by uncertainties stemming from supply, demand, or unexpected disruptions. Unlike petrochemical plants that use crude oil, biorefineries often have to deal with the uneven spatial‐temporal distribution of feedstock supply. The modular production strategy provides more flexibility in chemical manufacturing by allowing fast capacity expansion and unit movement. However, modeling and optimizing modular biomass supply chain under uncertainties becomes challenging due to high dimensionality and the existence of discrete decisions. This work optimizes the multiperiod biomass supply chain using the rolling horizon planning and two‐stage stochastic programming framework. We then applied generalized Benders decomposition to reduce the computational complexity of the stochastic mixed integer nonlinear programming supply chain optimization. Furthermore, the solution of the stochastic programming could be used to quantitatively describe the life‐cycle assessment uncertainties of the biomass supply chain performance, demonstrating seasonality and random variability.

中文翻译：

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使用广义 Benders 分解在不确定性下设计和运营模块化生物炼制供应链

生物质供应链绩效受到供应、需求或意外中断引起的不确定性的严重影响。与使用原油的石化厂不同，生物炼油厂通常必须应对原料供应时空分布不均匀的问题。模块化生产策略通过允许快速产能扩张和单元移动，为化学制造提供了更大的灵活性。然而，由于高维度和离散决策的存在，在不确定性下建模和优化模块化生物质供应链变得具有挑战性。这项工作使用滚动时间规划和两阶段随机规划框架优化多周期生物质供应链。然后，我们应用广义 Benders 分解来降低随机混合整数非线性规划供应链优化的计算复杂度。此外，随机规划的解决方案可用于定量描述生物质供应链绩效的生命周期评估不确定性，证明季节性和随机变异性。