The need for effective freight and human transportation systems has consistently increased during the last decades, mainly due to factors such as globalization, e-commerce activities, and mobility requirements. Traditionally, transportation systems have been designed with the main goal of reducing their monetary cost while offering a specified quality of service. During the last decade, however, sustainability concepts are also being considered as a critical component of transportation systems, i.e., the environmental and social impact of transportation activities have to be taken into account when managers and policy makers design and operate modern transportation systems, whether these refer to long-distance carriers or to metropolitan areas. This paper reviews the existing work on different scientific methodologies that are being used to promote Sustainable Transportation Systems (STS), including simulation, optimization, machine learning, and fuzzy sets. This paper discusses how each of these methodologies have been employed to design and efficiently operate STS. In addition, the paper also provides a classification of common challenges, best practices, future trends, and open research lines that might be useful for both researchers and practitioners.

The prevailing need for a more sustainable management of natural resources depends not only on the decisions made by governments and the will of the population, but also on the knowledge of the role of energy in our society and the relevance of preserving natural resources. In this sense, critical work is being done to instill key concepts—such as the circular economy and sustainable energy—in higher education institutions. In this way, it is expected that future professionals and managers will be aware of the importance of energy optimization, and will learn a series of computational methods that can support the decision-making process. In the context of higher education, this paper reviews the main trends and challenges related to the concepts of circular economy and sustainable energy. Besides, we analyze the role of simulation and serious games as a learning tool for the aforementioned concepts. Finally, the paper provides insights and discusses open research opportunities regarding the use of these computational tools to incorporate circular economy concepts in higher education degrees. Our findings show that, while efforts are being made to include these concepts in current programs, there is still much work to be done, especially from the point of view of university management. In addition, the analysis of the teaching methodologies analyzed shows that, although their implementation has been successful in favoring the active learning of students, their use (especially that of serious games) is not yet widespread.

From airplanes to electric vehicles and trains, modern transportation systems require large quantities of energy. These vast amounts of energy have to be produced somewhere—ideally by using sustainable sources—and then brought to the transportation system. Energy is a scarce and costly resource, which cannot always be produced from renewable sources. Therefore, it is critical to consume energy as efficiently as possible, that is, transportation activities need to be carried out with an optimal intake of energetic means. This paper reviews existing work on the optimization of energy consumption in the area of transportation, including road freight, passenger rail, maritime, and air transportation modes. The paper also analyzes how optimization methods—of both exact and approximate nature—have been used to deal with these energy-optimization problems. Finally, it provides insights and discusses open research opportunities regarding the use of new intelligent algorithms—combining metaheuristics with simulation and machine learning—to improve the efficiency of energy consumption in transportation.

A symbiotic simulation system (S3) enables interactions between a physical system and its computational model representation. To support operational decisions, an S3 uses real-time data from the physical system, which is gathered via sensors and saved in an enterprise data storage system (EDSS). Both real-time and historical data are then used as inputs to the different components of an S3. This paper proposes a generic system architecture for an S3 and discusses its integration within EDSSs. The paper also reviews the literature on S3 and analyses how these systems can be used for real-time decision-making.

Despite its economic importance, the textile industry is one of the most polluting sectors. This study addresses the critical gap in understanding sustainable decision‐making in textile SMEs, where decision processes often remain informal and lack structured frameworks. It seeks to identify the decisions made in productive environments that incorporate environmental, economic, and social sustainability, while also exploring those that overlook these dimensions. Through a systematic literature review, the research examines key themes such as (i) decision‐making for sustainable development in SMEs; (ii) sustainable supply chain management in the textile industry; (iii) sustainable practices to achieve the SDGs; and (iv) environmental, social, and economic sustainability in textile SMEs. The analysis (i) explores the most commonly used sustainability pillars and practices related to products, processes, and organizations; (ii) categorizes aspects, strategies, indicators, and barriers faced by textile SMEs; and (iii) identifies types of decisions, methodologies, and criteria that can guide these companies toward sustainability. The findings highlight the predominance of the environmental dimension, with life cycle assessment, circular economy, and sustainable business models being the most well‐defined practices. Decision‐making methodologies often rely on multiattribute decision‐making (MADM), particularly the analytic hierarchy process (AHP). However, the use of mathematical programming models remains limited. This paper provides a valuable resource for improving sustainability initiatives in textile SMEs by connecting categorized elements with production environments and sustainability dimensions.