Ivana Budinska

The energy trilemma balances energy security, affordability, and environmental sustainability; adding social acceptability extends it to an energy quadrilemma. Industry 5.0 promotes human-centric, resilient, and sustainable industrial systems increasingly enabled by artificial intelligence (AI). Based on a structured search of Scopus and Web of Science (2015–2026), this narrative review uses the energy quadrilemma as an analytical lens to examine enabling technologies, Composite AI (integrating multiple AI paradigms), and the energy footprint and regulation of AI. The literature on the energy quadrilemma and Industry 5.0 appears to have developed largely independently. Identified field studies rely on data-driven AI alone, whereas multi-paradigm hybrids are documented mainly in simulation and pilot studies. The relationship is bidirectional: AI may support all four dimensions of the quadrilemma, yet the growing, geographically concentrated electricity demand of AI infrastructure, particularly data centres, can strain grid capacity, increase local electricity costs, and burden host communities. Energy and climate policies should therefore account for AI infrastructure as a source of electricity demand, rather than treating AI solely as an efficiency tool. The paper proposes a mapping between Industry 5.0 pillars and quadrilemma dimensions, introduces the concept of a net quadrilemma contribution, and outlines a research agenda.

The automotive family design is known as one of the most complex engineering design problems with multiple groups of stakeholders involved from different domains of interest and contradictory attributes. Taking into account all stakeholders’ preferences, which are generally symmetrical, non-deterministic distributions around a mean value, and determining the right value of attributes for each alternative are two basic challenges for these types of decision-making problems. In this research, the possibility to achieve a robust-reliable decision by focusing on the two aforementioned challenges is explored. In the proposed methodology, a random simulation technique is used to elicit stakeholders’ preferences and determine the relative importance of attributes. The decision space and values of attributes are determined using the Knowledge Discovery in Databases (KDD) technique, and to achieve a robust-reliable decision, statistical and sensitivity analyses are performed. By implementing this methodology, the decision-maker is assured that the preferences of all stakeholders are taken into account and the determined values for attributes are reliable with the least degree of uncertainty. The proposed methodology aims to select benchmark platforms for the development of an automotive family. The decision space includes 546 automobiles in 11 different segments based on 34 platforms. There are 6223 unique possible states of stakeholders’ preferences. As a result, five platforms with the highest degree of desirability and robustness to diversity and uncertainty in the stakeholders’ preferences are selected. The presented methodology can be implemented in complex decision-making problems, including a large and diverse number of stakeholders and multiple attributes. In addition, this methodology is compatible with many Multi-Attribute Decision-Making (MADM) techniques, including SAW, AHP, SWARA, and TOPSIS.

Today, small- and medium-sized enterprises (SMEs) play an important role in the economy of societies. Although environmental factors, such as COVID-19, as well as non-environmental factors, such as equipment failure, make these industries more vulnerable, they can be minimized by better understanding the concerns and threats these industries face. Only a few SMEs have the capacity to implement the innovative manufacturing technologies of Industry 4.0. The system must be highly adaptable to any equipment, have low costs, avoid the need of doing complex integrations and setups, and have future reliability due to the rapid growth of technology. The goal of this study was to provide an overview of past articles (2010–2020), highlighting the major expectations, requirements, and challenges for SMEs regarding the implementation of predictive maintenance (PdM). The proposed solutions to meet these expectations, requirements, and challenges are discussed. In general, in this study, we attempted to overcome the challenges and limitations of using smart manufacturing—PdM, in particular—in small- and medium-sized enterprises by summarizing the solutions offered in different industries and with various conditions. Moreover, this literature review enables managers and stakeholders of organizations to find solutions from previous studies for a specific category, with consideration for their expectations and needs. This can be significantly helpful for small- and medium-sized organizations to save time due to time-consuming maintenance processes.

This paper introduces a central ontology layer as the central part of a multiagent system, which offers unified access to the stored data, information and knowledge through a common interconnection layer for all agents in the system. This ontology layer gathers all knowledge on one place; this is useful in case of sharing and exchanging information and knowledge. This paper also proposes an example of the central ontology layer in power grid scheduling, where this layer integrates two ontologies, namely scheduling ontology and experience management ontology