Abstract
Companies are increasingly adopting technologies such as Robotic Process Automation (RPA) to reduce costs and improve productivity. RPA is deployed in areas like accounting, payroll, and finance to automate business processes. While RPA does not necessarily result in unemployment, it has notable effects on employees and company governance. This study explores the impact of RPA implementation on employees and company governance, using a qualitative methodology based on thirteen semi-structured interviews with RPA experts from four multinational companies. The results indicate that the impacts of RPA vary depending on the automation strategy adopted (task-oriented or process-oriented). In task-oriented strategies, citizen developers often play a central role, contributing to rapid implementation. In contrast, process-oriented strategies tend to rely on professional developers and require more structured governance. The findings also point out that RPA influences not only task execution but also employee upskilling, job role redefinition, and the evolution of governance models. The study proposes an integrated framework linking automation strategy, governance, upskilling, and employee adaptation, offering both practical insights and theoretical contributions to digital transformation research and for managing risks and enhancing workforce capabilities. It also advances academic understanding by linking real-world RPA implementations to organisational and technological impacts.

Abstract
Online retail is transforming the way distribution networks are managed. One prominent change is that retailers can now use their full network to fulfil orders. This process involves allocating orders to fulfilment nodes and, depending on the setting, can include other operational decisions, such as order consolidation, shipping mode selection and product substitution. This order allocation problem (OAOR) has garnered considerable attention in recent years. However, there is no comprehensive view of what has been done in the literature, nor a consistent terminology across papers, which makes it hard to position existing work and identify research gaps. To address these concerns, we conduct a systematic literature review, where we find over 60 articles contributing to the OAOR literature. From this review, we formulate the baseline problem, consider multiple extensions, and identify key problem characteristics. Additionally, we analyse and categorize the solution methods found based on the optimization mechanism, policy class, and incorporation of future information and learning. Our review points to several avenues for future research, both in problems and in solution methods.

Abstract
As collaborative robots increasingly redefine industrial automation, understanding the factors that drive their adoption is essential to operations management. This study examines the main drivers of collaborative robot adoption in the Brazilian manufacturing sector by combining theory-driven framing with a machine learning classification approach. It was developed a Random Forest classifier to identify the strongest predictors of cobot adoption and to rank their relative importance. Data were collected from a sample of respondents-primarily managers and chief executive officers-representing 300 industrial companies. Grounded in the Technology-Organization-Environment (TOE) framework and complemented by Diffusion of Innovations (DoI) and Institutional (INT) perspectives, the analysis shows that technological advantages, namely space efficiency, cost reduction, and ease of integration, are critical drivers of adoption. Organizational factors, including proactive managerial involvement and alignment with an innovation-oriented culture, significantly increase the likelihood of collaborative robot uptake. The model demonstrated robust predictive performance and produced interpretable variable importance scores that confirm the relative influence of technological and managerial factors. These findings provide a structured lens for understanding and guiding managerial decision-making on cobot adoption and translate into practical recommendations for managers.

Abstract
-This study focuses on providing meaningful information to vulnerable road users (VRUs) to support their objectives and perceptions while navigating urban spaces, employing a novel route planning concept. Through three focus group sessions, a comprehensive survey was conducted to identify the needs and concerns of VRUs, leading to the development of an integrated and collaborative mobile application for active mobility. The application encompasses route calculation, prioritizing safety, comfort, civic participation, and empathy. The solution aims to bridge citizen users and city managers, facilitating alerts, historical information on safety and comfort, and collaborative problem-solving and sharing of urban attractions. A prototype of the concept was developed and extensively tested by potential users, and subjective evaluation and feedback demonstrated the usefulness and added value of the integrated and collaborative approach. This study highlights the proposed solution relevance and differentiation from official alerts, user experiences, and civic participation, positioning it as a comprehensive solution for active mobility.

Abstract
We consider the problem of selecting bases for firefighting activities (e.g., vigilance, water refill, initial attack) and links between them in the context of wildfire promptness. Bases can be facilities, such as watchtowers and water tanks, or positions from where an initial attack is conducted. It is assumed that it is advantageous to connect bases in such a way that resources (e.g. ground crews) can quickly move between them. The general problem is modelled in a general way as integration of a set covering problem (for selecting the location of the bases) and a travelling salesman problem where the cities are the selected locations and the arcs the links that connect them. We propose a mixed integer programming model where objectives are addressed by lexicographic optimization. The first objective is related to cover potential ignition points with a high estimate of their initial spread rate of the fire at the detection time. Computational experiments are discussed for a scenario, of an actual landscape, with parameters estimated from a fire behaviour model that takes into account slope, fuels, and wind. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
The Manufacturer's Pallet Loading Problem involves optimising the packing of a maximal number of identical rectangular boxes onto a single rectangular pallet. This problem arises in various logistic operations that involve the storage and transportation of boxed products, where efficient packing can result in substantial cost reductions and improved operational efficiency. Logistics managers anticipate that some boxes can be damaged during handling and transport, so the stability of the pallet load is essential to avoid such damage. The interlocking method is commonly used in practice to improve stability when loading pallets, minimising product damage and reducing the risk of injury to personnel handling the pallet. This study introduces a Mixed Integer Linear Programming model that addresses the Manufacturer's Pallet Loading Problem, promoting static stability through interlocking. Stability is evaluated with respect to the relationship between successive layers of the loading plan, with three types of interlocking incorporated into the mathematical model. Computational experiments with real-world instances were conducted to assess the model's performance using different objective functions and post-optimisation heuristics that target real-world requirements. Three stability metrics were used to evaluate the load plans generated by the mathematical model. The results show the interlocking method's benefits on the pallet loads' stability while maximising the pallet volume usage.