This paper proposes a genetic algorithm approach to solve the identical parallel machines problem with tooling constraints in job shop flexible manufacturing systems (JS-FMSs) with the consideration of tool wear. The approach takes into account the residual useful life of tools and allocates a set of jobs with specific processing times and tooling requirements on identical parallel machines. Two metrics are introduced to evaluate the scheduling decisions and optimize the scheduling process, with the competitive goal of maximizing tool utilization and minimizing production makespan. The proposed approach searches for a set of optimal solutions on the Pareto front that offers the best possible balance between these two objectives, achieving optimal local performance in terms of both makespan and tool utilization. The approach is implemented with a customized genetic algorithm and validated on a real case study from a company operating in the aerospace sector, which confirms its effectiveness in increasing tool utilization and reducing the makespan. The results show that the proposed approach has significant practical implications for the manufacturing industry, particularly in the production of high-value materials such as those in the aerospace sector that require costly tools. This paper contributes to the operational research community by providing advanced scheduling algorithms that can optimize both the makespan and the tool utilization concurrently, improving production efficiency and maintaining competitiveness in the manufacturing industry.

This study presents an optimisation framework for operating a battery swapping station (BSS) to enhance efficiency and sustainability in electric vehicle (EV) infrastructure. A hybrid modelling approach combines agent-based discrete event simulation and linear programming to model the dynamic behaviour of batteries and operational processes within the BSS. The model considers factors such as charging speed, battery degradation, grid power constraints, customer behaviour, and range anxiety. The agent-based model simulates the interaction between vehicles, batteries, and the station, capturing the stochastic nature of EVs’ arrivals and battery demand with the discrete event simulation. The linear programming component optimises battery state transitions to minimise degradation and ensure that the demand is met while respecting the power limits of the grid. Different battery types are considered based on vehicle category, each with specific capacity and usage patterns, reflecting real-world market conditions. The results demonstrate that the proposed optimisation framework can effectively manage the complex operational needs of a BSS. The proposed framework effectively balances service quality with resource efficiency by employing a strategic mix of charging modes and inventory management, reducing operational and degradation costs. This approach supports a more sustainable EV infrastructure, highlighting BSS as a viable solution to enhance the efficiency and sustainability of EV operations. Furthermore, the analysis highlights the critical role of power limits in determining charging strategies and their impact on operational efficiency. The findings suggest that with optimised operations, BSS can play a critical role in accelerating the adoption of EVs by offering a faster, more reliable, and sustainable alternative.

Manufacturing organisations face increasing pressure to assess and report their sustainability performance, particularly with the introduction of the European Sustainability Reporting Standards (ESRS). This paper presents a novel sustainability assessment framework that integrates ESRS guidelines with the Analytic Hierarchy Process (AHP) to provide a comprehensive evaluation methodology for manufacturing organisations. The framework addresses the limitations of existing approaches by systematically incorporating standardised sustainability indicators while enabling precise weighting of their relative importance. Through a structured approach, the framework evaluates environmental, social, and governance dimensions using both measurable and non-measurable indicators derived from ESRS guidelines. The methodology includes automated pairwise comparisons and weight calculations, significantly reducing the complexity traditionally associated with multi-criteria decision-making methods. The framework’s effectiveness is demonstrated through its implementation in a manufacturing organisation, where it successfully evaluated 38 sustainability indicators across 10 ESRS-defined subtopics. The case study highlighted areas requiring immediate attention, particularly in water and marine resources management, where performance fell below the established benchmark. This research contributes to both theory and practice by offering manufacturing organisations a structured approach to sustainability assessment that combines regulatory compliance with effective performance evaluation. The framework’s automated nature and integration of standardised reporting requirements make it particularly valuable for organisations transitioning to mandatory sustainability reporting under ESRS guidelines.

The ability to supply increasingly individualized market demand in a short period of time while maintaining costs to a bare minimum might be considered a vital factor for industrialized countries’ competitive revival. Despite significant advances in the field of Industry 4.0, there is still an open gap in the literature regarding advanced methodologies for production planning and control. Among different production and control approaches, hybrid architectures are gaining huge interest in the literature. For such architectures to operate at their best, reliable models for performance prediction of the supervised production system are required. In an effort to advance the development of hybrid architecture, this paper develops a model able to predict the performance of the controlled system when it is structured as a controlled work-in-progress (CONWIP) flow-shop with generalized stochastic processing times. To achieve this, we employed a simulation tool using both discrete-event and agent-based simulation techniques, which was then utilized to generate data for training a deep learning neural network. This network was proposed for estimating the throughput of a balanced system, together with a normalization method to generalize the approach. The results showed that the developed estimation tool outperforms the best-known approximated mathematical models while allowing one-shot training of the network. Finally, the paper develops preliminary insights about generalized performance estimation for unbalanced lines.

Augmented Reality (AR) is considered as one of the enabling technologies offering the largest potential to improve the way in which modern factories operate, thus becoming an integral part of the Industry 4.0 paradigm. However, there are still some aspects that hinder the widespread adoption of AR. In particular, one limitation is the lack of a measurement methodology of the impact that AR has on the worker's performance in a manufacturing environment. In this regard, the present work addresses the assessment of the performance of workers when they carry out a task, supported by an AR system. To this purpose, comparative experimental tests were carried out on two groups of volunteers who were given a specific assembly task (one group followed paper instructions, whereas the other followed AR-administered instructions). To measure the worker's performance and assess the impact of AR, two figures of merit were used: the processing time required to complete a task and the number of errors made. The experimental tests showed that, while the processing times were comparable between the two groups, administering instructions through AR resulted in a lower error rate and hence an improved quality of the product.