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.

Information asymmetry and the misreporting of operational data severely challenge contemporary supply chains (SCs), leading to inefficiencies, disruptions and performance deterioration. Traditional anomaly detection techniques typically focus on detecting downstream consequences rather than proactively identifying misreporting behaviors. To address this gap, this study introduces an interpretable AI-driven framework, employing the Isolation Forest algorithm, for the early detection of misreported data in multi-tier SCs. Central to this approach is the introduction of Engineered Cross-Stage Features (ECSFs), designed to capture relational discrepancies across different SC echelons. The framework was evaluated using a synthetic three-tier SC dataset, comprising both standard operational conditions and artificially perturbed data points representing misreporting behaviors and different scenarios of information asymmetry among SC actors. In a comparative assessment against Hotelling’s T2 and an Autoencoder, our ECSF-based Isolation Forest framework demonstrated superior efficacy, highlighting its potential as a practical solution for proactively identifying and mitigating misreporting in complex SC environments.

The advent of Industry 4.0 has revolutionised manufacturing systems, introducing unprecedented levels of customisation and variability. Traditional methods for controlling Work-In-Progress (WIP) often fall short in these dynamic environments, necessitating the development of adaptive and intelligent control strategies. This paper explores the application of Reinforcement Learning (RL) for adaptive WIP control in semi-heterarchical architectures for flow-shop production systems. We propose a novel framework that integrates RL, specifically Deep Q-Networks (DQN), with Discrete-Event Simulation (DES) to derive optimal control policies without relying on closed-form mathematical models. Preliminary simulation experiments demonstrate the effectiveness of the proposed approach in handling variations in job processing time variability and throughput reference targets, showcasing the merit and potential of RL for adaptive WIP control.

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.

To stay competitive, modern market scenarios are forcing a radical shift in the manufacturing concept, focusing companies’ attention on customer satisfaction through increased product customization and quick response strategies. Significant progress has been made in the field of Industry 4.0 technologies, but there is still an open gap in the literature regarding methodologies for efficiently managing a manufacturing system’s available productive resources. Spearman et al. proposed the CONtrolled Work-In-Progress (CONWIP) production logic, which allows controlling Work-In-Progress (WIP) in a production system while monitoring throughput. However, in order to face with the increased variability that enters into the production system, an affordable performances estimation tool is still required. Taking advantages of the recent innovation in the field of machine learning, this paper contributes to the development of a tool for estimating the performance of a production line using a deep learning neural network. The results demonstrated that the proposed estimation tool outperforms the current best-known mathematical model when estimating the throughput of a CONWIP Flow-Shop production line with a given processing time distribution and WIP value.

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.