Flight Attendant Route Planning and Resource Allocation Algorithm Based on Reinforcement Learning and Dynamic Programming

In this paper, flight attendants' route planning and resource allocation algorithms based on feedback learning (RL) and dynamic programming (DP) are studied. In the air transport industry, effective flight attendant route planning and resource allocation are very important for improving flight punctuality and optimizing operational efficiency. This study first introduces the basic principles of RL and DP, and discusses their applications in route planning and resource allocation. Then, the paper describes in detail how to apply these two algorithms to flight attendants' route planning and resource allocation, and constructs the corresponding mathematical model. Furthermore, a series of experiments are designed to compare the flight punctuality rate and flight attendant resource utilization rate of RL algorithm and DP algorithm in different experimental scenarios. The experimental results show that the two algorithms have different performances in different scenarios, and reasonable work arrangement has an important impact on resource utilization. This study provides a new solution for the air transport industry, and has certain practical significance for optimizing the flight scheduling system and improving operational efficiency.