Optimal Design of Hydrogen Supply Chains by a Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D)

EasyChair Preprint 2595

Abstract

Hydrogen is expected to play a significant role in low-carbon energy landscape. In this work the hydrogen supply chain (HSC) is optimized, minimizing simultaneously the total daily cost (TDC) and the global warming potential (GWP). This problem, containing binary variables often leads to difficulties for problem solution and the treatment of large problem instances can be viewed as a challenging issue from a numerical viewpoint. To overcome these barriers, the solution of this problem is addressed through a multiobjective evolutionary algorithm, namely MOEA/D. In order to obtain efficient results for larger instances, MOEA/D is coupled with a local search procedure (linear programming). The original MILP problem is solved by a master-slave strategy where the evolutionary algorithm (master) manage only integer variables, while the LP solver (slave) treats the continuous variables as well as the constraints.

Keyphrases: evolutionary algorithm, hydrogen supply chain, multiobjective optimization

@booklet{EasyChair:2595,
  author    = {Victor Hugo Cantú and Catherine Azzaro-Pantel and Antonin Ponsich},
  title     = {Optimal Design of Hydrogen Supply Chains by a Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D)},
  howpublished = {EasyChair Preprint 2595},
  year      = {EasyChair, 2020}}