A stochastic optimization method was proposed in order to solve the vehicle number allocation problem of the minimum autonomous vehicle fleet driven by spatio-temporal multi-tasks of terminal delivery. Firstly, the influence of service time and waiting time on the route planning of autonomous vehicle fleet was analyzed to build the shortest route model, and the service sequence network was constructed based on the two-dimensional spatio-temporal network. Then, the vehicle number allocation problem of the minimum autonomous vehicle fleet was converted into a network maximum flow problem through the network transformation, and a minimum fleet model was established with the goal of minimizing the vehicle number of the fleet. Finally, the Dijkstra-Dinic algorithm combining Dijkstra algorithm and Dinic algorithm was designed according to the model features in order to solve the vehicle number allocation problem of the minimum autonomous vehicle fleet. Simulation experiments were carried out in four different scales of service networks, the results show that:under different successful service rates, the minimum size of autonomous vehicle fleet is positively correlated with the scale of service network, and it decreases with the increase of waiting time and gradually tends to be stable, the One-stop operator introduced into the proposed algorithm greatly improves the search efficiency, and the proposed model and algorithm are suitable for the calculation of the minimum vehicle fleet in large-scale service network.

Aiming at the problem that the traditional Laplacian score for feature selection cannot be directly applied to multi-label tasks, a multi-label feature selection algorithm based on Laplacian score was proposed. Firstly, the sample similarity matrix was reconstructed by the correlation of the common and non-correlated correlations of the samples in the overall label space. Then, the correlation and redundancy between features were introduced into Laplacian score, and a forward greedy search strategy was designed to evaluate the co-operation ability between candidate features and selected features, which was used to evaluate the importance of candidate features. Finally, extensive experiments were conducted on six multi-label data sets with five different evaluation criteria. The experimental results show that compared with Multi-label Dimensionality reduction via Dependence Maximization (MDDM), Feature selection for Multi-Label Naive Bayes classification (MLNB) and feature selection for multi-label classification using multivariate mutual information (PMU), the proposed algorithm not only has the best classification performance, but also has a remarkable performance of up to 65%.

Aiming at the capacity P-median problem of continuous domains under the dense demand, the Centroidal Capacity Constrained Power Diagram (CCCPD) theory was proposed to approximately model the continuous P-median problem and accelerate the solving process. The Power diagram was constructed by extended Balzer's method, centroid restriction was imposed to satisfy the requirements of P-median, and capacity constraint was imposed to meet the capacity requirements of certain demand densities. The experimental results show that the proposed algorithm can quickly obtain an approximate feasible solution, having the advantages of better computing efficiency and capacity accuracy compared to Alper Murata's method and Centroidal Capacity Constrained Voronoi Tessellation (CCCVT) respectively. Additionally, the proposed method has excellent adaptability to complex density functions.