Sustainable supply chain design under correlated uncertainty in energy and carbon prices

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.

Automated summary

This paper aims to improve the modeling of supply chain designs by considering correlated uncertainty among multiple parameters. A new methodology is presented to generate forecasts for historically correlated time series and applied to energy and carbon prices. These scenarios are then used to obtain a three-echelon supply chain design in Europe.