Optimization of a Hydrogen Supply Chain Network Design by Multi-Objective Genetic Algorithms

Nowadays, hydrogen is considered as one of the most promising energy carriers for mobility applications. A model of the hydrogen supply chain (HSC) based on MILP formulation (Mixed Integer Linear Programming) in a multi-objective formulation implemented via the epsilon-constraint method to generate the Pareto front was carried out in a previous work and applied to the region of Midi-Pyrenees. Yet, the size and in particular the number of binary variables often may lead to difficulties for problem solution. In this work, the potential of genetic algorithms (GA) via a variant of NSGA-II is explored to cope with the multi-objective formulation, in order to produce compromise solutions automatically. The results obtained by using GA are compared to those presented in the base model as well as the computational effort to generate the solutions. The solutions obtained by GA exhibit the same order of magnitude as those obtained with MILP in the mono-criterion problem, and some compromise solutions are produced in the multi-objective formulation.