Dynamic multi-objective, multi-period optimisation of a hydrogen supply chain in the Gulf Cooperation Council (GCC) region: A Saudi Arabia case study

Home to some of the highest solar radiation levels globally and a strategic export location, Saudi Arabia ranks among the top countries for green hydrogen potential. However, widescale deployment remains constrained by the challenge of designing a supply chain that can effectively balance trade-offs between economic, environmental, and safety/risk objectives. This study presents a multi-objective, multi-period optimisation model for the design of a green hydrogen supply chain (HSC) network in the Northwestern region of Saudi Arabia, considering various production technologies (electrolyser types), storage options, and transportation modes. A novel dynamic framework is developed to simultaneously optimise cost, carbon footprint, and safety/risk. Within this framework, a hybrid AHP–MILP approach is integrated to capture stakeholder preferences and their evolution over time through time-dependent weightings, enabling the relative importance of economic, environmental, and safety criteria to adapt across planning periods in line with changing stakeholder priorities. Four planning periods are considered in this study: establishment phase (T1); early operations phase (T2); steady operations phase (T3) and mature system (T4) - with low, medium, and high demand scenarios analysed in each period. Results showed that as hydrogen demand increases, production technologies converge in performance because their individual strengths and weaknesses counterbalance each other, while storage and transportation technologies diverge as scale amplifies the advantages of various criteria. • Dynamic MILP framework for hydrogen supply chain under shifting demand. • Cost, carbon, and risk optimised via time-based stakeholder priorities. • Production tech converges at scale; storage and transport diverge. • Salt caverns and pipelines lead in high-demand due to scale efficiency. • Staged, hierarchical design reduces trade-offs under uncertainty.