Integrated Optimization of Blocking Flowshop Scheduling and Preventive Maintenance Using a Q-Learning-Based Aquila Optimizer

In recent years, integration of production scheduling and machine maintenance has gained increasing attention in order to improve the stability efficiency flowshop manufacturing systems. This paper proposes a Q-learning-based aquila optimizer (QL-AO) for solving integrated optimization problem blocking preventive since jobs processing requires be considered practice environments. proposed algorithmic framework, Q-learning algorithm is designed adaptively adjust selection probabilities four key population update strategies classic optimizer. addition, five local search methods are employed refine quality individuals according their fitness level. A series numerical experiments carried out two groups benchmark. Experimental results show that QL-AO significantly outperforms six peer algorithms state-of-the-art hybrid based on Q-Learning investigated problem. Additionally, effective improving its performance.