Medina-González, S;
Shokry, A;
Silvente, J;
Lupera, G;
Espuña, A;
(2020)
Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework.
Computers and Industrial Engineering
, 139
, Article 105561. 10.1016/j.cie.2018.12.008.
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Abstract
This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.
| Type: | Article |
|---|---|
| Title: | Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.cie.2018.12.008 |
| Publisher version: | https://doi.org/10.1016/j.cie.2018.12.008 |
| Language: | English |
| Additional information: | Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). |
| Keywords: | Supply chain management, Optimization under uncertainty, Data-driven decision-support, Multiparametric programming, Kriging metamodeling |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10068276 |
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