Research Of The Vehicle Scheduling Optimization By Swarm Intelligence For Military Logistics

In this paper, using the optimization theory and the evolutionary algorithms, some deeper researches on the modeling, algorithm improvements and applications, and the simulations have been done, which with the background of the study of military logistics, and the optimization of the vehicle scheduling as the main study object. The main works are as follows:1. For the current state that take the idealized state of all nodes connected as the major research model, the data model based on nodes and connection of the roads has been constructed.2. In order to meet the practical needs of military applications, the mathematical model with the target of the total waiting time of nodes and the total distance of the vehicle has been established. The special requirements of the timeliness, security, priority have been taken into account in the model, and the different weight of the multi-objective has been analyzed in detail according to the practical needs. The state of the same vehicle in one task probably taking many times transports has been considered in this model.3. The mixed method combining the Hybrid Particle Swarm Optimization(HPSO) with the A * algorithm, has been used to solve the vehicle scheduling problem(VSP). In order to overcome the shortcomings of basic PSO(Particle Swarm Optimization) algorithm, the mutation operator has been introduced to enhance the ability of global exploration and the inertia weight has been changed with the generation to enhance the ability of local exploitation; for the A * algorithm some operations have been done to avoid duplication and cycle pruning. This paper has done some simulations and these simulations indicate that the mixed method can effectively solve the vehicle scheduling problems.4. The vehicle dynamic scheduling problem has been studied. A reasonable traffic path arrangement has been reached by using the time when the emergency occurred in the vehicle transportation as a dividing point, analyzing the state of the current vehicles and the nodes, considering the impact of unexpected events, changing the current initial conditions, and using the mixed method combining the Hybrid Particle Swarm Optimization(HPSO) with the A * algorithm to calculate.