<p>Renewable-based energy carriers have emerged as key components of sustainable process systems, especially within integrated and optimized operational frameworks. The study aims to formulate a mixed-integer programming model for optimizing the design of the green-hydrogen supply chain, while minimizing total economic costs. The model also accounts for policy-driven factors, such as the impact of advertisements and government subsidies, on improving economic feasibility. A genetic algorithm is utilized to solve the complex optimization problem and identify the cost-effective network configuration. A numerical example demonstrates that the setup cost of a solar plant accounts for 47% of the total economic cost. Sensitivity analysis reveals that advertisements and government subsidies lead to a 55% rise in hydrogen demand, while subsidies reduce the total cost by 3.7 %. The analysis highlights that the genetic algorithm improves solution accuracy by approximately 8.33%, while particle swarm optimization maintains an advantage in computational speed. The proposed framework offers practical insights for managers and stakeholders to design cost-effective and strategic energy supply chains.</p>