Data spaces are increasingly recognized as a key enabler of secure, sovereign, and interoperable data exchange across manufacturing and supply chain networks. Despite growing institutional interest in Europe, academic research on this topic lacks a consolidated perspective. This study addresses this gap by combining a systematic literature review with an analysis of early insights from European initiatives to explore how data spaces are being conceptualized and implemented in industrial contexts. The review covers bibliometric trends and thematic content in the scientific literature, while also examining the structure and maturity of ongoing European projects. Results show a recent surge in scholarly interest, with early applications focusing primarily on resilience and sustainability. Practical initiatives are progressing toward implementation, supported by reference architectures like International Data Space and Gaia-X. The study concludes by outlining future research priorities, including the need for standardized design approaches and greater support for cross-sector collaboration.

Quality inspection is critical for ensuring efficiency and compliance in assembly lines. The increasing adoption of AI-driven technologies, such as machine vision systems, offers significant potential to enhance detection performance and reduce inspection costs. However, these technologies are often integrated with human operators in hybrid inspection systems, posing complex design challenges. Motivated by gaps in the existing research, this paper proposes a novel MILP model that introduces several previously unaddressed capabilities in inspection planning. Specifically, it simultaneously optimizes the inspection method selection, sampling rate, and detection rate across multi-product systems featuring multiple inspection technologies with varying costs and accuracies. This unified formulation represents a substantive advancement over existing models, which typically address only isolated aspects of the problem. The model minimizes the total quality-related costs—comprising investment, inspection, penalty, and rework costs—while considering operational constraints such as workforce availability, inspection and rework time limits, and equipment capacity. Key modeling assumptions include heterogeneous inspection accuracies, product-specific defect probabilities, and the feasibility of partial inspections. The approach is validated on both synthetic datasets and a real-world automotive case study, demonstrating its ability to significantly reduce costs and to highlight the benefits of effectively combining human and machine-based inspections.

Ensuring product conformity in industrial assembly lines is becoming increasingly complex due to high production rates, as well as the growing complexity and customisation of products. Zero-Defect Manufacturing (ZDM) aims to eliminate production defects by integrating advanced quality assurance technologies, thereby enhancing processes efficiency and sustainability. Automatic Optical Inspection (AOI), powered by Computer Vision (CV), has emerged as a key enabler of ZDM, automating defect detection and enhancing quality control. However, the industrial adoption of AOI systems is still in its early stages, and there is a need for a structured methodology to assess their technical feasibility and select the most suitable image-acquisition technology. This paper proposes a novel multi-criteria decision-making (MCDM) methodology for selecting image-acquisition technologies in AOI systems. The approach integrates Analytic Hierarchy Process (AHP) and the Weighted Sum Model (WSM) to evaluate alternatives based on key factors such as product dimensions, inspection area dispersion, visibility constraints, and system flexibility requirements. The methodology is validated through two real-world case studies in the automotive sector, demonstrating its effectiveness in supporting technology selection and guiding AOI implementation in real production environments.

The design of sustainable power systems requires planning tools that jointly account for economic, environmental, and social dimensions. However, multi-objective energy system models typically prioritize economic–environmental trade-offs, while the social dimension is still rarely included as an explicit optimization objective. Furthermore, many formulations adopt a low temporal resolution (e.g., annual time steps) and assume fully flexible power plants, potentially overlooking temporal variability and operational constraints. This paper presents a three-objective optimization model for sustainable power system design that minimizes (i) costs, (ii) greenhouse gas (GHG) emissions, and (iii) social opposition (i.e., the public resistance to certain energy technologies). Temporal variability and operational detail are preserved using weighted representative periods with intra-period time steps and a clustered unit commitment (CUC) formulation. The Pareto frontier is generated using the normalized normal constraint (NNC) method, highlighting the space of efficient economic, environmental, and social solutions. A case study focused on the Italian electricity system exemplifies the model application by providing the cost-optimal, emissions-optimal, and social-optimal solutions, together with trade-off solutions. Among the trade-off solutions, the selected best balance solution achieves a significant reduction in emissions (−20%) compared to the cost-optimal solution, with a limited cost increase (+5%) and a marginal increase in social opposition (+0.7%). Overall, the proposed model enables transparent quantification of multi-dimensional trade-offs to support decision-making in sustainable power system design.

The increasing integration of Intermittent Renewable Energy Sources (IRES) and the need for decarbonization are driving significant transformations in the expansion planning of electrical power systems. In this context, the literature highlights the need to integrate long-term strategic decisions with short-term operational constraints in planning models, while preserving high resolution across time, space, and techno-economic detail. This paper addresses this challenge by proposing a high-resolution optimization model for the integrated expansion planning of generation, storage, and transmission in electrical power systems. The proposed model combines high temporal resolution, a multi-node spatial configuration, and a Clustered Unit Commitment (CUC) formulation to account for key operational constraints while limiting computational cost. The practical applicability of the model in real-world contexts is demonstrated through a multi-scenario analysis focused on the Italian electricity system, including a 2021 baseline scenario and 2030 future scenarios differing in terms of carbon tax and IRES support level. The validation of the model outputs in the baseline scenario against the 2021 historical electricity generation mix results in a Mean Absolute Percentage Error (MAPE) of 0.8%. In the 2030 scenarios, the renewable share in electricity generation increases from 40.0% in the 2021 baseline to 51.0–56.5%, while average emission intensity decreases from 0.315 to 0.196–0.177 kg CO₂-eq/kWh. These outcomes are associated with targeted IRES investments, especially in southern Italy and the islands, together with storage deployment and additional transmission capacity along the south–north connections, highlighting the value of integrated high-resolution planning for capturing generation-storage-transmission interactions.