Distributionally robust production and replenishment problem for hydrogen supply chains

This study investigates a two-stage production and replenishment problem for the hydrogen supply chain (HSC), where the uncertainty of demand and the risk of pipeline disruptions are simultaneously considered. We model this problem by adopting a distributionally robust optimization (DRO) approach. Before realizing the demands, the decision-maker decides the hydrogen production, storage, and truck-transported replenishment policies. Then, after demands and pipeline working status are realized, the pipeline-transported replenishment policy is determined in response to the first-stage decisions. The replenishment policies are determined under differentiated service level requirements. In addition, we characterize the uncertainty and correlation of demands through a factor-based model approach to accommodate the uncertainty of the distribution of demand factors. To solve the proposed model, we propose a column and constraint generation (CCG) algorithm, which can terminate in finite iterations. Through theoretical analysis and numerical experiments, we verify that the proposed model outperforms classical benchmark models in terms of total costs and are more robust. Finally, we examine the impact of the replenishment structure on the performance of the HSC and the computational efficiency of the proposed CCG algorithm.