A predictive-reactive strategy for flight test task scheduling with aircraft grounding

In flight test engineering, the flight test duration (FTD) affects the aircraft's delivery node and directly impacts costs. In the actual flight test process, the environmental status updates frequently, and various uncertain events are often encountered, which affect the flight test progress and project implementation. Therefore, when scheduling flight test tasks, rescheduling should be taken into account. This paper proposes a predictive-reactive strategy based on a deep reinforcement learning approach to solve the flight test task scheduling problem with consideration of aircraft grounding. In the predictive stage, a constructive heuristic algorithm is designed to generate an initial schedule. The rescheduling problem is solved by the appropriate rescheduling method that aims to optimize the FTD deviation, task reallocation, and workload cost simultaneously. The problem is modeled as a Markov decision process, including the well-designed state features, rewards, and actions based on different rescheduling methods. The policy is trained by the proximal policy optimization algorithm. At last, numerical results are provided to demonstrate the effectiveness and superiority of the proposed approach.