# A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective
## 5 State of the Art of Reinforcement Learning in Production Scheduling
Preventive maintenance prolongs machine life but entails downtime and costs. The study [76] addresses these challenges by optimizing production and maintenance schedules in multi-machine systems, introducing knowledge enhanced Reinforcement Learning to enhance RL effectiveness in guiding production decisions and fostering machine collaboration. Comparative evaluations in deterministic and stochastic scenarios highlight the algorithm's ability to maximize business rewards and prevent failures. According to [PERSON] et al. [54], the production scheduling process involves manufacturing various items using a hybrid production pattern incorporating the multi-agent Deep Reinforcement Learning (MADRL) model. [PERSON] et al. [60] proposed MADRL to optimize flexible production plants considering factors like efficiency and environmental targets. [PERSON] et al. [77] used the Deep Q-Network (DQN) method to address the flexible task shop scheduling problem (FJSP) amid changing processing rates, setup time, idle time, and task transportation. This approach integrates state indicators and actions to enhance the DQN component's efficiency. Additionally, it includes a problem driven exploratory data analysis (EDA) component to improve data exploration. [PERSON] et al. [78] used a Deep Reinforcement Learning (DRL) method to solve the discrete flexible job shop problem with inter tool reusability (DFJSP-ITR), addressing the multi-objective optimization problem of minimizing combined makespan and total energy consumption. The proposed solution includes few generic state characteristics, a genetic programming based action space, and a reward function. [PERSON] et al. [79] proposed using online scheduling strategies based on RL with composite reward functions to improve industrial systems effectiveness and robustness. The work [18] utilized an advanced DRL algorithm to optimize production scheduling in complex job shops, highlighting benefits like enhanced adaptability, global visibility, and optimization. Some authors have focused on developing various DRL algorithms capable of formulating complex strategies for production scheduling. For instance, [PERSON] et al. [26] created an online rescheduling framework for the dynamic multi-objective flexible job shop scheduling problem, enabling minimization of total tardiness or maximization of machine usage rate. [PERSON] et al. [27] used the deep Q-learning technique to address dynamic scheduling in intelligent manufacturing. Another dynamic scheduling method using deep RL is proposed in [80], employing proximal policy optimization to determine the ideal scheduling policy. [PERSON] and [PERSON] [81] examined using the reinforcement Q-learning algorithm for agent based production scheduling. The integration of RL with production scheduling signifies a major advancement in optimizing manufacturing processes. As research and technology continue to evolve, it promises even greater efficiency and adaptability in the industry. Delving into bibliometric data can provide valuable insights into research trends, influential works, and key contributors in this rapidly developing field. Industry 4.0 and Smart Industry lead contemporary manufacturing and production. Industry 4.0 integrates advanced technologies like the Internet of Things (IoT), Artificial Intelligence (AI), Big Data, and Cyber-Physical Systems to create highly automated and networked production environments. The concept of intelligent industry emphasizes real-time data analytics, predictive maintenance, and adaptable manufacturing processes to improve efficiency, flexibility, and production [27]. Implementing RL algorithms is crucial in these paradigms, facilitating machine learning and future prediction through interactions in the production environment. This enhances scheduling, resource allocation, and quality control, enabling firms to achieve high automation and precision in decision-making, leading to more resilient and responsive modern manufacturing [54]. Table 1 illustrates the contribution of RL algorithms to production scheduling