This paper aimed at the modeling research of the real-time regulation of ambulances in emergency scenarios, an online reinforcement learning algorithm based on the data enhancement method is proposed and applied to the solution of the ambulances control model. The experimental comparison with various algorithms shows that the method proposed in this paper not only has a strong computational advantage in the simulation experiment but also has a strong computational advantage. Moreover, it has practical application potential in emergency rescue operation regulation.
The use of large aircrafts for logistics supply is a key development direction in the future, but the existing research on mission planning problems has few problems and considers the actual environmental factors. This paper mainly considers the actual environmental factors such as aircraft loss and delivery window period, and models the task assignment and path planning of large-scale aircraft for material delivery and supply. On the basis of direct optimization with a basic genetic algorithm, an improved optimization idea and corresponding algorithm combining prior knowledge are proposed, which improves the problem of slow convergence caused by too large search space, and improves the solution speed and optimization effect of the model.
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