Optimization research of metering material distribution based on improved ant colony algorithm

In power grid operation, optimized metering material distribution is crucial to ensure efficient power work and maintain supply chain stability. However, it faces difficulties such as high distribution time limit, complex cost control, poor network layout, and low storage and distribution efficiency. For this reason, this paper proposes a distribution optimization method based on the improved ant colony algorithm, constructs a logistics scheduling model with the goal of minimizing the fixed, transportation, penalty, and security costs, and considers the constraints such as vehicle capacity and material distribution to realize the overall optimization of the supply chain. At the algorithmic level, the improved ACO model based on dynamic pheromone updating is designed to address the problems of local optimization and slow convergence of the traditional ACO algorithm. The real data simulation shows that compared with the traditional algorithm, this paper’s algorithm reduces the comprehensive cost by $3.7 \%$, improves the convergence speed by $5.6 \%$, and reduces the path detour rate by $3.9 \%$, which significantly reduces the cost and improves the global search efficiency.