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

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Title: Sustainable supply chain design under correlated uncertainty in energy and carbon prices
Authors: Lujan Garcia-Castro, Florencia | Ruiz-Femenia, Rubén | Salcedo Díaz, Raquel | Caballero, José A.
Research Group/s: Computer Optimization of Chemical Engineering Processes and Technologies (CONCEPT)
Center, Department or Service: Universidad de Alicante. Departamento de Ingeniería Química | Universidad de Alicante. Instituto Universitario de Ingeniería de los Procesos Químicos
Keywords: Correlated uncertainty | Energy price | Carbon price | Supply chain | Stochastic programming
Issue Date: 31-May-2023
Publisher: Elsevier
Citation: Journal of Cleaner Production. 2023, 414: 137612.
Abstract: 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.
Sponsor: The authors gratefully acknowledge financial support to the Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital of the Generalitat Valenciana, Spain, under project PROMETEO/2020/064 and project PID2021-124139NB-C21.
ISSN: 0959-6526 (Print) | 1879-1786 (Online)
DOI: 10.1016/j.jclepro.2023.137612
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC license (
Peer Review: si
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Appears in Collections:INV - CONCEPT - Artículos de Revistas

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