Abstract
Effective warehouse management plays a pivotal role in optimizing supply chain performance, particularly in high-demand, time-sensitive environments. This study introduces a simulation-based decision support system designed to improve the management of Picking-By-Line (PBL) operations in cross-docking distribution centres. Developed in FlexSim and calibrated with empirical data from an industrial case study, the model replicates real-world warehouse conditions and is validated against observed operational performance. The tool supports warehouse managers in evaluating and comparing operational strategies, such as dynamic storage allocation policies and picker routing constraints, with the goal of reducing operator travel distances, mitigating congestion, and enhancing overall efficiency. A key contribution of this work is the integration of congestion-sensitive performance indicators that allow for a detailed analysis of the trade-offs between travel efficiency and localized congestion-an aspect often overlooked in traditional optimization methods. This study demonstrates the value of simulation as a scalable and realistic decision-support tool for optimizing PBL operations in complex and variable environments where human movement is a major cost and performance driver. The proposed tool bridges the gap between theoretical modelling and practical implementation, offering actionable insights for warehouse layout, space utilization, and resource allocation.

Abstract
ABSTRACT This study explores the impact of Green Knowledge Sharing (GKS) on Organizational Performance (OP), considering the mediating roles of Green Organizational Culture (GOC) and Technological Innovation (TI). Addressing current gaps in the literature, the research extends beyond sector-specific analyses and incorporates a cross-country perspective, examining 297 small and medium-sized enterprises (SMEs) in Portugal, Spain, and the United Kingdom. Additionally, this study acknowledges the influence of digital transformation in enhancing GKS, a factor often overlooked in previous research. By adopting a Structural Equation Modeling (SEM) approach, this article confirms a direct and positive effect on both OP and GOC, with GOC further influencing OP, establishing its mediating role in this relationship. However, the relationships between GKS and TI, as well as the indirect effect of GKS on OP through TI, are not supported. These findings offer theoretical advancements by broadening the conventional understanding of OP beyond financial metrics and present practical implications for SME managers, highlighting strategies to foster a green organizational culture and leverage technological innovation for sustainable performance.

Abstract
The rapid advancement of warehouse automation has increased the need for intelligent intralogistics solutions that enhance material handling efficiency and optimize space utilization. This research presents a simulation-based methodology that integrates Autonomous Mobile Robots (AMRs) with container loading optimization in a unified decision-support framework that dynamically synchronizes AMR routing with optimized truckload configurations, a feature not commonly addressed jointly in existing literature to improve warehouse operations. By leveraging a hybrid approach combining discrete event and agent-based simulation in FlexSim, the study evaluates the impact of AMR fleet size, routing strategies, and truckload configurations on overall logistics performance. A proof-of-concept industrial case study illustrates how different scenarios influence key performance metrics, such as total operation time and resource utilization. The findings demonstrate that synchronized AMR deployment and optimized container loading strategies contribute to increased throughput, reduced handling time, and enhanced logistics unit utilization. This work provides a framework for dynamic logistics planning, offering valuable insights for companies seeking to enhance warehouse efficiency and sustainability through simulation-driven decision support. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
The adoption of Autonomous Mobile Robots (AMRs) has emerged as a promising solution to enhance efficiency and reduce operational costs for industrial companies. Given the significant cost of AMRs, it is crucial to determine the optimal number and characteristics before making significant investments. This study proposes a decision-support framework based on simulation to assess the impact of integrating AMR robots in a complex distribution center. Additionally, this framework aids decision-makers in determining the optimal fleet size of AMR robots and corresponding charging stations. A simulation model was developed using data from a leading retail company, focusing on pallet movement within the facility, comparing scenarios combining AMRs with other intralogistics implementations. This methodology incorporates uncertainty, variability (statistical distributions to create transportation orders, acceleration, demand and offer fluctuations) and implements fleet management, transportation capacity, demand matching, and resource utilization according to real case scenarios. The proposed model replicates accurate robot coordination and actual deployment environments, ensuring that the tested scenarios approximate the real-world conditions as much as possible. Preliminary findings show results supporting the decision-making for a fleet size to meet weekly production targets, optimize robot utilization, and coordinate charging instances to prevent production stops. Conclusions suggest that the proposed simulation approach is an effective tool for planning and implementing logistics solutions, enabling users to make informed decisions before investing.

Abstract
essential for achieving the Sustainable Development Goals (SDGs), particularly in regions aiming to balance energy efficiency, waste management, and urban development. This study explores the application of multicriteria decision-making and statistical techniques to evaluate municipal sustainability, with a focus on renewable energy, using the Alto Minho region of Portugal as a case study. The analysis incorporates 12 SDG indicators across ten municipalities, addressing energy consumption, urban renewal, and waste management. Cluster analysis revealed distinct groups of municipalities, highlighting disparities in sustainability performance. Municipalities such as Melgaco and Moncao excelled in energy-related metrics, while others showed strengths in waste management and urban renewal. The Analytic Hierarchy Process (AHP) emphasized the importance of renewable energy indicators, revealing notable changes in rankings when energy-related criteria were prioritized. Ponte de Lima and Melgaco ranked highest under energy-focused weighting schemes, showcasing their leadership in energy efficiency and renewable adoption. The findings underscore the need for targeted policies to enhance sustainability across municipalities, particularly in regions lagging in energy performance.

Abstract
The application of Artificial Intelligence (AI) in the energy sector offers new opportunities for developing flexible, efficient, and sustainable infrastructures. Nevertheless, real-world deployment is still constrained by the lack of large-scale, integrated environments that can evaluate advanced algorithms under realistic operating conditions while ensuring regulatory compliance. This paper presents EnerTEF (which stands for Energy Testing and Experimentation Facility), a federated platform for testing and experimentation in the energy sector designed to address this gap. We introduce a unified TEF architecture that enables full-stack evaluation of intelligent systems, including predictive modeling, optimization, learning under data distribution shifts and federated learning across geographically distributed sites. The framework integrates high-fidelity digital twins, a privacy-preserving data exchange framework and regulatory sandboxing to support transparent, explainable and robust AI development. EnerTEF demonstrates how such a framework can be deployed in critical energy domains through three real-world scenarios including short-term hydropower generation forecasting, coordination between distribution network operators and distributed energy resources and real-time optimization of self-consumption for municipal buildings. Results show that EnerTEF effectively enables the development of novel AI models, improves cross-context generalizability and supports innovation for complex energy infrastructures, ultimately creating a practical, scalable path for addressing different energy-related problems and heterogeneous data.