Planning maintenance in manufacturing systems is challenging, especially when dealing with unpredictable machine breakdowns. This paper presents a Deep Reinforcement Learning (DRL) framework to automate and optimise maintenance decisions. Our approach uniquely models a realistic industrial environment where machine failures are stochastic and can occur in succession, a critical factor often simplified in traditional methods. The DRL agents learn to make decisions using local machine data combined with key system-wide performance metrics, enabling a modular yet globally-aware strategy. We benchmarked our DRL policy against a state-of-the-art Metaheuristic Genetic Algorithm (MGA). The results demonstrate that our DRL approach achieves two key advantages. First, it matches the production throughput of the benchmark, particularly under moderate operational stress. Second, it significantly reduces the overall maintenance workload and enhances system robustness against the variability of machine failures. This work highlights the potential of DRL to create intelligent, decentralized maintenance strategies that improve both efficiency and resilience in complex, high-variability industrial environments.

This study addresses the scheduling problem in a single-machine system with variable processing speed. The performance of the Earliest Due Date (EDD) rule, operating at a fixed maximum speed, is compared with a Reinforcement Learning (RL)-based approach capable of dynamically adapting both processing speed and job selection. The analysis considers both temporal performance (tardiness) and sustainability (energy consumption). Simulation results, supported by statistical analysis, show that the RL approach achieves a significant reduction in total energy consumption compared to the EDD rule. Regarding temporal performance, analysis of variance reveals that the chosen policy significantly affects the number of tardy jobs, with an effect that depends on the tightness of job deadlines, whereas the overall average tardiness across all jobs does not differ significantly between the two policies. These findings highlight the potential of adaptive RL-based policies for more sustainable resource management, demonstrating the ability to achieve substantial energy savings while maintaining competitive temporal performance and dynamically managing the trade-off according to deadline pressure.

Maintenance scheduling is critical function across numerous industries, where the dynamic complexity of operations often challenge the systems efficiency. This study explores the application of Deep Reinforcement Learning (DRL) to refine scheduling decisions by integrating a simulation tool that replicates an industrial production line. This integration eases the modelling and simulation in real-time of machine operations, job flows, and maintenance activities, capturing the dynamics and complexities of a typical production environment. Our developed tool assesses various maintenance strategies and their direct impacts on productivity, leveraging DRL to enhance decision-making capabilities. We introduce an innovative job-sequencing rule that complements the DRL framework, systematically analysing its effectiveness against traditional heuristic methods. The comparative analysis confirms that our DRL-based approach, coupled with the job sequencing rule, significantly optimises maintenance timing and resource allocation in a flow shop setting. By synthesizing simulation with intelligent algorithms, our method not only optimize maintenance tasks but also boosts overall production efficiency.

Maintenance scheduling is critical for many industries, and Deep Reinforcement Learning (DRL) has shown great potential in optimizing scheduling decisions in complex and dynamic environments. This proposal introduces an integrated simulation tool and DRL algorithm for effective maintenance event scheduling and planning in a Flow Shop production line. This comprehensive solution aims to optimize maintenance plans and maximize productivity by combining simulation capabilities with intelligent decision-making via DRL. The integrated simulation tool replicates the production line Flow Shop in a virtual environment, allowing for precise modeling and simulation of machine operations, job flows, and maintenance events. The tool evaluates different maintenance procedures and their impact on overall performance by capturing the system’s dynamics and complexities. The novelty of the approach lies in the fact that the training phase is performed on a single machine, and the policy developed is tested on a Flow Shop line with machines with the same Weibull parameters (α and β) and with machines with different Weibull parameters. The proposed integrated simulation tool and DRL algorithm provide a powerful solution for the scheduling and planning of maintenance events in a production line Flow Shop. By combining simulation capabilities with intelligent decision-making through DRL, this approach offers a comprehensive solution to optimize maintenance strategies and enhance overall production performance in all experimental settings tested.

Deep Reinforcement Learning (DRL) has been included into the production system for multiple objectives, including control, scheduling, and maintenance planning. Maintenance must be planned sensibly and economically in order to preserve the usable life of the production systems while not sacrificing productivity and so minimising costs and losses. In this work a hybrid simulation-based and DRL approach is employed to develop an agent that can autonomously determine when to do preventative maintenance by considering the failure probability at a particular instant and the length of time since the last maintenance operation has been performed. The novelty of this approach is the configuration of the DRL setting, in particular the reward function. Results are promising comparing the approach with a heuristic from the literature, as they show that the frequency of machine failures is dramatically reduced.

Considering a Flow Shop production line in an Industry 4.0 setting where the Cyber-Physical System (CPS) and Internet of Things (IoTs) can be deployed, a newly Performance-based Decentralised Dispatching Rule (PDDR) is proposed. It combines known dispatching rules with the knowledge of the monitored production system state. The goal is to provide a novel dispatching rule based on production line performance oversight. The governance system considers the machine condition in terms of machine utilisation. Regarding the assessment scenario, the proposed rule has been tested and compared with the well-known Short Processing Time (SPT) and the First-In-First-Out (FIFO) rule in a higher generality way by taking into account unforeseen events that may occur in production (such as breakdowns, potential rework, micro-stops, and unplanned machine setups). The simulation results showed interesting results where the flexibility of this rule, as well as its practical use with real hypotheses are its main advantages.