Smart Manufacturing Systems (SMSs) have evolved into intelligent, data-driven ecosystems that integrate cyber–physical systems, digital twins, and artificial intelligence to enhance efficiency, sustainability, and resilience. This review synthesises more than 250 recent studies across four domains: manufacturing technologies, systems management, sustainable production, and human–robot collaboration. In process optimisation, hybrid machine learning and genetic algorithms reduce surface roughness in machining by up to 35% and decrease energy use in additive manufacturing by 20–30%. In systems management, digital twins and reinforcement learning enable adaptive scheduling and predictive maintenance, increasing operational flexibility and reducing industrial downtime. Sustainability-oriented research shows that additive manufacturing can cut energy consumption by up to threefold compared with subtractive routes, while aluminium recycling and hot-forming processes lower life-cycle impacts. Furthermore, the integration of ISO 14001, ISO 50001, and ISO 14040 supports consistent environmental and energy performance assessment across sectors. Building on this evidence, the review critically examines recent developments in manufacturing technologies, systems management, sustainable practices, and human–robot collaboration, highlighting emerging paradigms such as explainable AI and human-centric design that strengthen safety, transparency, and resilience. Open challenges and research opportunities are outlined to guide future innovation toward intelligent, adaptive, and sustainable manufacturing systems.

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.

This literature review examines recent studies on the application of artificial intelligence (AI) in public administration, incorporating real-life examples to demonstrate the impact of AI on various aspects of public administration. The review is organised into the following sections: AI for decision-making, AI for public service delivery, AI for policy analysis, AI for public engagement, and AI for public sector efficiency. The paper concludes by identifying potential challenges and future research directions.

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.

This study presents the tracking moving object method based on the Lucas-Kanade algorithm with feature points regeneration. The given algorithm provides the time-based ability to scale feature points of a moving object, which reduces the probability of tracking errors. Besides, this method allows to track objects, which have parts with different motion velocities. The Lucas-Kanade method and the tracking method based on the biological regeneration principle were considered in our research. Based on the reviewed methods, a new moving object tracking method with adaptive change in the number of feature points was proposed. The method allows reducing the probability of losing the tracked object when passing through the occlusion area.

One of the main ways to modernize the fiber-optic sensors is to minimize the error in measuring the position of spectral peaks of the fiber Bragg grating, which can be achieved by a proper selection of the existing algorithm of spectral peak detection or by various modifications of the algorithms. In this paper, we make a comparative analysis of the Bragg grating peak detection methods: the maximum method, the centroid method, the Gaussian approximation method, and the parabola method. Some solutions for modifying both the described methods and the interrogation system as a whole are also described. The accuracy of the described algorithms was evaluated on data sets of a real point pressure sensor on Bragg diffraction gratings.