Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

Abstract This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources if uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertainty factors considered. Such a complexity produced that, if tractable, the problem is solved after a large computational effort (CPU s). Therefore, in this work a data-driven decision-making framework is proposed to address these issues. Such a framework exploits the machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertainty 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. Such a representative data is 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.