This paper aims to provide an improvement in the modeling of supply chain designs by incorporating correlated uncertainty among multiple parameters, resulting in a more resilient design. A new methodology to generate forecasts for historically correlated time series, regardless of their underlying probability distributions, is presented and applied to generate scenarios for energy and carbon prices, which historically proved to be correlated. These scenarios are then used in a stochastic computation to obtain a three-echelon supply chain design in Europe maximizing the economic performance. The emissions were monetarized through the incorporation of the European Union cap-and-trade emissions trading system into the model. The social impact of the supply chain network is measured in terms of the direct, indirect and induced jobs it creates, which are proportional to the economic performance. By combining the developed methodology with data mining algorithms, a reduction in the number of required scenarios by more than 90% was achieved. The numerical case study moreover shows that the stochastic design ensures an average reduction of emissions by more than 3 ktons compared to the use of a deterministic approach. In comparison, the computation of a stochastic supply chain design without parameter correlation takes 5 times longer.

本文旨在通过合并多个参数之间的相关不确定性来改进供应链设计的建模，从而实现更具弹性的设计。提出了一种新的方法来为历史相关的时间序列生成预测，而不管其潜在的概率分布如何，并将其应用于生成能源和碳价格的情景，这在历史上被证明是相关的。然后将这些场景用于随机计算，以获得欧洲的三级供应链设计，以最大限度地提高经济绩效。通过将欧盟限额与交易排放交易系统纳入模型，排放量被货币化。供应链网络的社会影响是根据它创造的直接、间接和诱发的就业机会来衡量的，与经济绩效成正比。通过将开发的方法与数据挖掘算法相结合，所需场景的数量减少了 90% 以上。此外，数值案例研究表明，与使用确定性方法相比，随机设计确保平均减排 3 吨以上。相比之下，没有参数相关性的随机供应链设计的计算时间要长 5 倍。此外，数值案例研究表明，与使用确定性方法相比，随机设计确保平均减排 3 吨以上。相比之下，没有参数相关性的随机供应链设计的计算时间要长 5 倍。此外，数值案例研究表明，与使用确定性方法相比，随机设计确保平均减排 3 吨以上。相比之下，没有参数相关性的随机供应链设计的计算时间要长 5 倍。