The scheduling problems in mass production, manufacturing, assembly, synthesis, and transportation, as well as internet services, can partly be attributed to a hybrid flow-shop scheduling problem (HFSP). To solve the problem, a reinforcement learning (RL) method for HFSP is studied for the first time in this paper. HFSP is described and attributed to the Markov Decision Processes (MDP), for which the special states, actions, and reward function are designed. On this basis, the MDP framework is established. The Boltzmann exploration policy is adopted to trade-off the exploration and exploitation during choosing action in RL. Compared with the first-come-first-serve strategy that is frequently adopted when coding in most of the traditional intelligent algorithms, the rule in the RL method is first-come-first-choice, which is more conducive to achieving the global optimal solution. For validation, the RL method is utilized for scheduling in a metal processing workshop of an automobile engine factory. Then, the method is applied to the sortie scheduling of carrier aircraft in continuous dispatch. The results demonstrate that the machining and support scheduling obtained by this RL method are reasonable in result quality, real-time performance and complexity, indicating that this RL method is practical for HFSP.

Optimizing naval aircraft deck sortie scheduling is important for improving the sortie efficiency of naval aircraft groups. It is also an important link in realizing the automated scheduling of aircraft carriers. Firstly, a single-aircraft surface transit path library was constructed. This paper used the improved A* algorithm and the optimal control deck path planning algorithm to construct the transit path library between the deck parking position and take-off position, or between warmable and non-warmable parking positions. Secondly, a mathematical optimization model was constructed for the sortie scheduling of a mixed fleet of fixed-wing carrier aircraft and carrier helicopters. The model took the maximum sortie time of the fleet and taxiing time of the fleet of fixed-wing aircraft as the optimization objectives, and considered the process flow, spatial, and resource constraints. Thirdly, a discretized improved whale optimization algorithm (IWOA) was designed to solve the problem. To improve the optimization-seeking capability of the algorithm, we discretized the algorithm, optimized its coding method, and introduced pre-constraints, learning factors, parameter improvements, and population restart. Finally, we set up various cases and proposed a new strategy for simulation experiments: these simulations verified the validity of the model, the excellent optimization performance of the IWOA, and the superiority of the sortie strategy. The research in this study will help to implement automated aircraft carrier scheduling.