Research on the Optimization Model of Power Grid Material Fulfillment Supply Chain Based on Modern Digital Supply Chain

With the continuous development of power grid construction, the supply chain management of power grid materials faces increasingly complex challenges. Traditional supply chain optimization methods have limitations in handling large-scale data, dynamic demand fluctuations, and logistics timeliness issues. This paper proposes an optimization model for the power grid material fulfillment supply chain based on a hybrid optimization algorithm, combining linear programming with simulated annealing and genetic algorithms. The model comprehensively optimizes factors such as material delivery paths, inventory management, and demand response, aiming to improve the efficiency and reliability of the supply chain. Experimental results show that the optimized solution reduces total costs by approximately $15 \%$, improves delivery timeliness by over $20 \%$, and increases delivery accuracy from $90 \%$ to $98 \%$. The model proposed in this paper performs excellently in cost control, timeliness, and accuracy, providing new ideas and methods for optimizing the power grid material supply chain.