Virtual reality (VR) has been brought closer to the general public over the past decade as it has become increasingly available for desktop and mobile platforms. As a result, consumer-grade VR may redefine how people learn by creating an engaging “hands-on” training experience. Today, VR applications leverage rich interactivity in a virtual environment without real-world consequences to optimize training programs in companies and educational institutions. Therefore, the main objective of this article was to improve the collaboration and communication practices in 3D virtual worlds with VR and metaverse focused on the educational and productive sector in smart factory. A key premise of our work is that the characteristics of the real environment can be replicated in a virtual world through digital twins, wherein new, configurable, innovative, and valuable ways of working and learning collaboratively can be created using avatar models. To do so, we present a proposal for the development of an experimental framework that constitutes a crucial first step in the process of formalizing collaboration in virtual environments through VR-powered metaverses. The VR system includes functional components, object-oriented configurations, advanced core, interfaces, and an online multi-user system. We present the study of the first application case of the framework with VR in a metaverse, focused on the smart factory, that shows the most relevant technologies of Industry 4.0. Functionality tests were carried out and evaluated with users through usability metrics that showed the satisfactory results of its potential educational and commercial use. Finally, the experimental results show that a commercial software framework for VR games can accelerate the development of experiments in the metaverse to connect users from different parts of the world in real time.

The design of the industrial facilities distribution is one of the most important decisions to be made, as it will condition the operation thereof. The concept of industrial installation as it is known today has evolved to the point that it integrates automation and information systems. Indeed, such evolution has given rise to the so-called intelligent factory. At present, in order to produce customized mass products according to customers' requirements, it is become an important issue the distribution of facilities with the generation of successful layout designs, based on the flexibility, modularity and easy configuration of production systems.This paper proposes a methodology to solve the problem of plant distribution design and redesign based upon a novel modular approach within an industry 4.0 context. Proposed methodology is an adaptation of the "SLP" Methodology (Systematic Layout Planning-Simulation) so-called SLP Modulary 4.0 (systematic planning of the Layout based on a modular vision under a context of Industry 4.0); this methodology incorporates in its structure an integrated design system (IDS) into its structure, which allows collaborative work with different CAD design and simulation tools. For the validation of the proposed methodology, a case study of a coffee processing plant is considered. The distribution design results obtained from the case study prove the benefit and usefulness of the proposed methodology.

The transition to electric mobility requires the coordinated evolution of vehicles, charging infrastructure, power systems, and intelligent digital platforms. This study examines the role of Industry 4.0 technologies in enabling large-scale electric vehicle (EV) adoption and effective EV grid integration and synthesizes the existing evidence into a coherent analytical framework to support planning and policy decision-making. A systematic review of 27 peer-reviewed studies published between 2018 and 2025 was conducted in accordance with PRISMA 2020 guidelines, capturing the acceleration of electromobility following the consolidation of Industry 4.0 technologies and the emergence of large-scale policy commitments worldwide. The analysis covers six technology families, including the Internet of Things, big data and analytics, artificial intelligence and machine learning, blockchain, digital twins, and extended reality, and examines their applications in smart charging, grid vehicle coordination, fleet optimization, and vehicle-to-grid services. The findings show that analytics and artificial intelligence consistently enhance operational reliability and efficiency, while digital twins are increasingly applied to infrastructure siting, grid impact assessment, and scenario analysis. Building on these results, the study proposes a three-layer analytical framework composed of physical, digital, and decision layers, together with a functional EV grid generation integration model that links technology readiness to system-level deployment. In addition, a transition timeline for the 2025–2040 period and a concise set of key performance indicators are introduced to support evaluation and comparison. Policy implications for Ecuador and Latin America emphasize interoperability, data governance, realistic cost assessment, and a phased approach to vehicle-to-grid deployment.

En este artículo se plantean algoritmos meta-heurísticos para el problema de programación de producción de un taller de flujo, considerado como un problema no polinómico completo debido a su complejidad. El estudio de la problemática es relevante dada su utilidad en la práctica, por ejemplo, en fábricas con líneas de ensamblaje o en la planificación de cadenas de suministro colaborativas. De esta manera, el objetivo del presente estudio consistió en evaluar dos algoritmos meta-heurísticos, GRASP y algoritmo genético. Se planteó un diseño experimental para determinar cuáles factores (método, número de trabajos y número de iteraciones) tienen un efecto estadísticamente significativo en el tiempo de finalización de programación de producción. Según los resultados se pudo observar que existe incidencia tanto de primer como de segundo y tercer orden de los factores, permitiendo caracterizar el desempeño de los dos algoritmos en entornos generados por la interacción de los tres factores analizados.

Innovative integration of virtual reality and metaverse to enhance real-time process monitoring, decision-making, and efficiency in smart factories.

This work presents a novel alternative to conventional linear homotopy with suboptimal settings for applications on object deformation. Proposed approach extends the linear mapping to exponential representations that provides smooth transitions when deforming objects while homotopy conditions are fulfilled. As well, we introduce a quality indicator based on the ratio between the coefficients curve of resultant homotopy and those of a non-realistic, reference homotopy. Experimental results are promising and show the applicability of exponential homotopy to interpolating images with soft changes and homotopic geometric objects.