Supply chain under demand uncertainty has been a challenging problem due to increased competition and market volatility in modern markets. Flexibility in planning decisions makes modular manufacturing a promising way to address this problem. In this work, the problem of multiperiod process and supply chain network design is considered under demand uncertainty. A mixed integer two-stage stochastic programming problem is formulated with integer variables indicating the process design and continuous variables to represent the material flow in the supply chain. The problem is solved using a rolling horizon approach. Benders decomposition is used to reduce the computational complexity of the optimization problem. To promote risk-averse decisions, a downside risk measure is incorporated in the model. The results demonstrate the several advantages of modular designs in meeting product demands. A pareto-optimal curve for minimizing the objectives of expected cost and downside risk is obtained.

中文翻译：

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考虑需求不确定性的模块化供应链优化管理风险

由于现代市场竞争加剧和市场波动，需求不确定下的供应链一直是一个具有挑战性的问题。规划决策的灵活性使模块化制造成为解决这一问题的有前途的方法。在这项工作中，考虑了需求不确定性下的多周期过程和供应链网络设计问题。混合整数两阶段随机规划问题是用表示过程设计的整数变量和表示供应链中物料流的连续变量来制定的。该问题使用滚动水平方法解决。Benders 分解用于降低优化问题的计算复杂度。为了促进规避风险的决策，模型中加入了下行风险措施。结果证明了模块化设计在满足产品需求方面的几个优势。获得了用于最小化预期成本和下行风险的目标的帕累托最优曲线。