The transition toward sustainable manufacturing requires not only high-performance production strategies but also transparent and interpretable decision-making tools. While Machine Learning (ML) has been widely applied in Additive Manufacturing (AM) to predict process outcomes, its adoption often suffers from a lack of interpretability. This study addresses this gap by integrating Explainable AI (XAI) into a data-driven framework for Wire Arc Additive Manufacturing (WAAM), using real experimental data from the deposition of Invar 36 alloy. Two ensemble ML algorithms, XGBoost and Random Forest, were employed to predict key output variables, such as layer width, height, specific energy consumption (SEC), and Global Warming Potential (GWP). SHAP (SHapley Additive exPlanations) values were used to interpret model predictions, revealing feature interdependencies and their relative contributions to each target. By coupling predictive accuracy with interpretability, the proposed framework provides actionable insights for the multi-indicator interpretation of WAAM processes, supporting both energy efficiency and environmental sustainability in AM.

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.

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.

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.

In today’s environment, characterized by high complexity and volatility of demand, responsiveness, quality, and timeliness in the transmission of information between all parties involved in Supply Chain operations, are critical aspects to manage. In this context, the most successful companies have developed an integrated view of the Supply Chain to improve its efficiency. The realization of these objectives is achieved through adopting Supply Chain management methods and tools appropriate to their operations, with a view to continuous improvement through data analysis and forecasting. The difficulty lies in intercepting and organising data from disparate sources, multiple data sets provide incomplete information that inaccurately represents the performance and service levels received by suppliers and offered to customers, caused by the competitive nature of different companies in wanting to keep their information confidential. For this reason, this work proposes an ontological Supply Chain model with a governance element that enables the information exchange, preventing misreporting behaviour by different companies and optimising the parameters of the entire Supply Chain. In addition to the definition of all major incoming and outgoing information flows that characterise the relationships and performance of the Supply Chain actors as individual elements and as a whole.

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.

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.

This paper presents a scheduler built around a set of Commercial Off of the Shelf (COTS) functions where the overall complexity have been reduced by a set of pre-processing and post-processing functions. This approach offers many advantages compared to design-for purpose heuristics in term of time to market as well as in term of design and implementation costs.

This paper describes an application of the cutting stock problem where great part of the complexity of the problem was actively addressed by mean of a definition of a suitable ontology. It deals with the realization of a series of concentric rings starting from a raw circular flange. The goal is to minimize the scrap and satisfy the customers demand, keeping into account all the constraints related to the coupling of rings and flanges. To reach the goal a combinatory optimization approach was employed. The mathematical model is solved by the LINGO software, an interactive tool for the solution of linear, quadratic and integer number programming problems.

The purpose of this paper is to show the impact of simulation software in the production processes. In particular, the study focused on the simulation techniques to model and to optimize the operations. It was carried out a review of different simulation software. Moreover, in this paper it was developed a case study on olive oil production system. In particular, in the case study, it was developed a simulation model that, starting from a scheduling of arrivals in real time, evaluates the performance of the system in order to optimize the parameters that can be chosen according to the requirements (yields optimization, quantity optimization, quality optimization, revenue maximization, etc.).

The objective of this work is the realization of a tool for decision support: a macro parametric model, consisting of two modules, the Flow Shop Main Module, representative of a typical production system and Sequencing Module, representative of the sequencing according to the FIFO rule of dispatching.

The aim of the paper is to investigate the integration of different analytical and simulations tools to support people to make decisions, and to show how the relationships among the different methods can be advantageous to solve specific problems. ORM (object - role modeling), PN (Petri Nets) and SD (System Dynamics) have been combined to capture the static and dynamic aspects of system.

Today we depend more and more on logistic networks, which often know nothing, or worse, on which our power of control is almost zero. It is impossible to imagine a life without certain types of products or food, all of that to get us often follow long and complex network and therefore vulnerable. Let see how increase in energy costs has engulfed many small companies. Differently by rising energy costs, there are also changes that are not so easily predictable, so it is essential for the survival of a company to have “redundant” resources, able to operate strategies and proactive behavior. It's important to be flexible and adapt better to the changes that are imposed by external or even internal conditions. More than on flexibility, it is necessary to focus on the concept of Resilience, which requires the ability to remain calm, to address a crisis, but maybe leave it weakened but with the strength, the ability and the confidence to create a tomorrow of own business, adapting to change.

The aim of this work is to create a simulation model of a manufacturing system operating within the supply chain by system dynamics approach heeding dynamics of system-company and factors that may affect performance, so that management can have a useful tool for decision support. The results have shown interesting correlations between management choices and the system outputs.

In recent years, there has been an increasing interest in sustainable development that could be regarded both as new constraint and opportunity to achieve a competitive advantage. Several tools like the implementation of an Environmental Management system, the adoption of Life Cycle assessment and Eco-Labelling may be used in order to enhance companies' competitiveness. In this context, policy makers are challenged to design effective policies and organizations for exploiting the opportunities that increasing environmental awareness provides. In order to make this task easier a holistic and systemic approach based on modeling may be used, so that users understand both structure and dynamics of complex systems in which they are in.