Hydrogen is currently considered one of the most promising sustainable 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, multi-period formulation, implemented via the ε-constraint method to generate the Pareto front, was conducted in a previous work and applied to the Occitania region of France. Three objective functions have been considered, i.e., the levelized hydrogen cost, the global warming potential, and a safety risk index. However, the size of the problem mainly induced by the number of binary variables often leads to difficulties in problem solution. The first innovative part of this work explores the potential of genetic algorithms (GAs) via a variant of the non-dominated sorting genetic algorithm (NSGA-II) to manage multi-objective formulation to produce compromise solutions automatically. The values of the objective functions obtained by the GAs in the mono-objective formulation exhibit the same order of magnitude as those obtained with MILP, and the multi-objective GA yields a Pareto front of better quality with well-distributed compromise solutions. The differences observed between the GA and the MILP approaches can be explained by way of managing the constraints and their different logics. The second innovative contribution is the modelling of demand uncertainty using fuzzy concepts for HSC design. The solutions are compared with the original crisp models based on either MILP or GA, giving more robustness to the proposed approach.

氢目前被认为是用于机动性应用的最有希望的可持续能源载体之一。在先前的工作中，建立了基于MILP公式（混合整数线性规划）的多目标，多周期公式中的氢供应链（HSC）模型，该模型通过ε约束方法实现以生成帕累托峰。并应用于法国的Occitania地区。已经考虑了三个目标函数，即平均氢气成本，全球变暖潜力和安全风险指数。但是，主要由二进制变量的数量引起的问题的大小通常导致解决问题的困难。这项工作的第一个创新部分是通过非主导排序遗传算法（NSGA-II）的变体来探索遗传算法（GA）的潜力，以管理多目标公式化以自动生成折衷解决方案。由GA在单目标公式中获得的目标函数值显示出与使用MILP获得的目标函数相同的数量级，而多目标GA产生质量更好的Pareto前沿，且折衷方案妥善解决。GA和MILP方法之间观察到的差异可以通过管理约束及其不同逻辑来解释。第二个创新贡献是使用模糊概念对HSC设计进行需求不确定性建模。将解决方案与基于MILP或GA的原始清晰模型进行比较，