Optimization of heterogeneous vehicle logistics scheduling with multi-objectives and multi-centers.

Industrial enterprises have high requirements on timeliness and cost when delivering industrial products to their customers. For this reason, this paper studies the vehicle routing problem (VRP) of different vehicle models in multiple distribution centers. First of all, we consider the multi-dimensional constraints in the actual distribution process such as vehicle load and time window, and build a multi-objective optimization model for product distribution with the goal of minimizing the distribution time and cost and maximizing the loading rate of vehicles. Furthermore, an Improved Life-cycle Swarm Optimization (ILSO) algorithm is proposed based on the life cycle theory. Finally, we use the order data that Yunnan Power Grid Company needs to deliver to the customer (municipal power supply bureau) on a certain day to conduct a dispatching experiment. The simulation and application results show that the transportation cost of transportation obtained by the ILSO algorithm is reduced by 0.8% to 1.6% compared with the other five algorithms. Therefore, ILSO algorithm has advantages in helping enterprises reduce costs and improve efficiency.

Study on border patrol task planning of heterogeneous UAVs group based on swarm intelligence.

Heterogeneous UAVs performing patrol tasks are a new type of border patrol method with high flexibility, high patrol efficiency, and low operating cost In order to improve the ability of heterogeneous UAVs to perform border patrol task, by constructing a three-dimensional complex coordinated planning model, a multi-objective fitness function with the minimum patrol energy consumption and maximum patrol coverage of UAVs in a complex mountain environment is established. Design an improved shuffled frog leaping algorithm (ISFLA) based on spiral search mechanism to solve the problem of task planning in complex mountain environment. The proposed algorithm is verified by simulation experiments. The simulation results show that the ISFLA algorithm for solving the path problem in complex three-dimensional environment has significantly improved the solving efficiency, accuracy and global convergence compared with the particle swarm optimization (PSO), differential evolution (DE) and shuffled frog leaping algorithm (SFLA). The experiments show that the proposed algorithm also has excellent solving ability in solving complex planning problems.