Distribution Path Optimization with Multiple Depot Based on an Improved Distribution Estimation Algorithm

With the increasing of sudden health emergencies, the demand for emergency medical is growing day by day, but there are some problems such as low efficiency in the distribution of emergency medical supplies. The paper proposed an improved estimation of distribution algorithms to solve the distribution paths with multi distribution depot for multiple emergency medical material distribution. To avoid premature convergence of distribution estimation algorithms during optimization processes such as path planning and NP hard convergence, two types of chaotic maps for chaotic mutation operations, utilizing the diversity, traversal, and randomness of chaotic mutation to alleviate the conflict between population diversity and algorithm convergence in the algorithm. The strategy achieved the goals of improving customer satisfaction, saving costs, and reducing carbon emissions generated during the distribution process. Comparing with traditional genetic algorithm, the proposed algorithm significantly improved various indicators compared to other algorithms.