Background: The wide variety of terms that converge in reverse logistics have been more evident and discussed in the current context of the literature, such as reverse channels, reverse supply chain, closed-loop supply chain, and circular supply chain. Regarding this, this paper aimed to investigate the level of uncertainty about the activities that make up the Reverse Logistics process in the opinion of professionals working in this area in Brazil, to develop a discussion relating to the sustainable development goals proposed by the UN and their importance for the future of logistics networks. Methods: Initially, through a detailed systematic review of the literature, the activities that make up the RL processes were identified. Then, a questionnaire was elaborated on regarding such activities, and a survey was developed with professionals in the area. The data obtained were analyzed through a descriptive analysis of means, calculation of Cronbach’s Alpha, and using the multicriteria decision technique TOPSIS. Results: It is possible to see that professionals involved with RL processes in Brazil still have many doubts regarding which activities belong to the RL process. In the opinion of Brazilian professionals, 10 of these activities have generated high levels of uncertainties about their belonging or not to the RL process. On the other hand, with a low level of uncertainty, 3 activities were not considered and 3 were considered to make up the RL process. Conclusions: It is believed that this study can contribute to the generation of knowledge by comparing basic information in the scientific literature with the practical knowledge of professionals belonging to the reverse logistics sector working in the Brazilian context.

Background: This study aimed to identify the most widespread performance objectives for the vehicle routing problem, the degree of comparative importance attributed to each of these performance objectives in the opinion of professionals in the logistics area working in Brazil and also relate them to aspects of sustainability considering environmental, economic and social issues. Methods: To this end, a literature review was carried out in the area and a survey was developed with professionals through a structured questionnaire. The collected data were treated using the TOPSIS multi-criteria technique. Results: The results indicate that the performance objectives in route plans “level of service”, “total number of vehicles” and “total distance travelled” are the ones that, in the opinion of the professionals participating in the research, have greater importance in the planning and elaboration of plan routes and that such objectives directly impact the sustainable results of a given organization. Conclusions: The results can serve as a basis for researchers in the area who aim to broaden the debates on this topic and for logistics operations managers who work directly with planning and elaboration of route plans and who aim to make their operations more sustainable. Therefore, this research addresses the literature gap by identifying which performance objectives should be considered in the elaboration of route plans and how they relate to sustainability guidelines. It is noteworthy that no other study with a similar objective was identified in the literature.

Background: The interest in the topic of performance assessment in reverse supply chains (RSC) is increasing, although the body of research is still in its early stages. As this is a developing field, it is crucial to expand discussions on topics that have not yet been thoroughly examined, such as the intrinsic bias of indicators and metrics that may be associated with specific operational, economic, environmental perspectives, etc. Such perspectives should be considered in the decision-making process within the context of reverse logistics (RL) and waste management (WM). The aim of this research was to identify different perspectives employed in the development of proposed models in the literature. Methods: A systematic literature review was conducted to analyze thirty papers from Scopus, Web of Science, and Science Direct databases without time restrictions. Results: The review identified various ways in which authors grouped perspectives, including qualitative and quantitative, sustainability, and operational perspectives, among others. Conclusions: This study revealed several gaps in the field, including limited studies on RSC performance assessment and a lack of studies linking performance assessment to decision-making components.

This research aimed to analyze the importance of artificial intelligence and sustainability in higher education according to the literature in the field and to present the relationships of this context with the United Nations Sustainable Development Goals (SDGs). The adopted research strategies included bibliometric analysis using VOSviewer software and literature review, considering the Web of Science scientific database. The bibliometric analysis resulted in the clustering of four groups. The blue cluster highlighted the emergence of interest in studies on AI and sustainability in higher education following the Covid-19 pandemic. The green cluster emphasized the importance of more efficient teaching methods adapted to the demands of higher education, as well as the need to empower teachers to use artificial intelligence in developing students' skills and competencies, emphasizing sustainability. The yellow cluster indicated the presence of artificial intelligence in higher education based on the triad of sustainable education and innovation, aiming to prepare students for future challenges. The red cluster emphasized the impact of artificial intelligence in higher education, focusing on student learning, efficiency, and sustainable performance. Finally, the literature analysis identified the main AI technologies in higher education and their relationship with the United Nations SDGs. The reflections presented here can contribute to expanding discussions on the relationship between artificial intelligence and sustainability in higher education. From a practical standpoint, it can serve as a foundation for university managers aiming to promote the integration of AI into their teaching processes, considering the context of sustainability.