Currently, reverse logistics and sustainability are recognized as strategies to enhance the performance of supply chain processes and customer service and to reduce environmental impact, which is reflected in the planning of and reduction in costs throughout the production process. This article developed a bibliometric review that considered the growth of reverse logistics and sustainability from the perspective of different organizations, authors, thematic subareas, countries, journals, keywords, and volume of citations and publications. For this, a methodology was developed that consisted of reviewing previous research, obtaining the Scopus data set, applying the analysis with Microsoft Excel 365 and VOSviewer version 1.6.18 to determine the applications and trends of future research, and identifying the global impact in the last six years on organizations. The search equation with the application of filters resulted in 22,625 articles. The Sustainability Switzerland journal provided the most significant number of contributions in terms of publications, and the Journal of Cleaner Production stood out for its number of citations. Tseng, M.L. and Govindan, K. were the most active authors. China, the United States, and the United Kingdom were the most notable countries. Chinese Academy of Sciences and the Ministry of Education of the People’s Republic of China were the most influential institutions. The main findings were the recognition of the potential research lines and industry 4.0 technologies applied in supply chains and the development of sustainable processes with the fusion of reverse logistics, sustainability, and circular economy.

This paper presents a bibliometric analysis of inventory models in a sustainable supply chain. The methodology contains reviewing previous research with a performance evaluation, network analysis, and science mapping to identify the applications, trends, and future research topics. Scientific mapping examines the periods and volumes of publications, authors, journals, countries, regions, organizations, subject areas, and citation analyses. The dataset was obtained with the Scopus database and analyzed using MS Excel and VOSviewer. The search equation identified 335 research papers, which resulted in 131 significant manuscripts on the subject after being screened and filtered. The most notable countries in developing research were Iran, India, China, the United States, Canada, Taiwan, France, the United Arab Emirates, Turkey, and Denmark. Saha, S., Ajay, S.Y., and Baboli, A. were the most cited authors. The journals that publish the most research were Sustainability, the Journal of Cleaner Production, and the International Journal of Production Economics. Some research focuses on reducing carbon emissions and polluting agents applied in different industries in China, Brazil, India, and others. The main findings were the number of industry sectors researching this topic, increasing the number of publications, and promoting the proper use of resources within a sustainable supply chain. There are many investigations of theoretical models that have applications in real-life cases. There is also evidence of the high importance of promoting sustainable development. The emissions regulations in a green supply chain applied to agricultural products have allowed for more actions to achieve responsible production and consumption, as seen in applied research in the pulp and paper industry.

This study employs a novel fuzzy logic-based framework to address multi-attribute group decision-making problems commonly encountered in modern astronomy. Our approach utilizes the probabilistic linguistic q-rung orthopair fuzzy set (PLq-ROFS) to handle the inherent uncertainties associated with astronomical data. The PLq-ROFS offers significant advantages over existing fuzzy sets like probabilistic hesitant, linguistic intuitionistic, and linguistic Pythagorean fuzzy sets, which comprise both stochastic and non-stochastic uncertainties simultaneously. To aggregate the probabilistic linguistic decision information effectively, we propose two novel operators: the PLq-ROF weighted power average (PLq-ROFWPA) and the PLq-ROF weighted power geometric (PLq-ROFWPG). These operators form the foundation of a novel method within the PLq-ROF environment. Furthermore, this study integrates the PLq-ROF framework with the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) model, a widely used decision-making (DM) tool known for its ability to balance group utility maximization with individual regret minimization. This integration leads to the PLq-ROF-VIKOR model, a novel approach for ranking alternative solutions based on the subjective preferences of decision-makers. The effectiveness of the proposed method is demonstrated through a real-world case study in astronomy, accompanied by both parameter and comparative analyses. These analyses highlight the efficiency and accuracy of the PLq-ROF-VIKOR model, ultimately leading to the conclusion that cosmology is the most optimal key finding in this case study.

During extreme events such as tropical cyclones, the precision of sensors used to sample the meteorological data is vital to feed weather and climate models for storm path forecasting, quantitative precipitation estimation, and other atmospheric parameters. For this reason, periodic data comparison between several sensors used to monitor these phenomena such as ground-based and satellite instruments, must maintain a high degree of correlation in order to issue alerts with an accuracy that allows for timely decision making. This study presents a cross-evaluation of the radar reflectivity from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement Mission (GPM) and the U.S. National Weather Service (NWS) Next-Generation Radar (NEXRAD) ground-based instrument located in the Caribbean island of Puerto Rico, USA, to determine the correlation degree between these two sensors’ measurements during extreme weather events and normal precipitation events during 2015–2019. GPM at Ku-band and Ka-band and NEXRAD at S-band overlapping scanning regions data of normal precipitation events during 2015–2019, and the spiral rain bands of four extreme weather events, Irma (Category 5 Hurricane), Beryl (Tropical Storm), Dorian (Category 1 hurricane), and Karen (Tropical Storm), were processed using the GPM Ground Validation System (GVS). In both cases, data were classified and analyzed statistically, paying particular attention to variables such as elevation angle mode and precipitation type (stratiform and convective). Given that ground-based radar (GR) has better spatial and temporal resolution, the NEXRAD was used as ground-truth. The results revealed that the correlation coefficient between the data of both instruments during the analyzed extreme weather events was moderate to low; for normal precipitation events, the correlation is lower than that of studies that compared GPM and NEXRAD reflectivity located in other regions of the USA. Only Tropical Storm Karen obtained similar results to other comparative studies in terms of the correlation coefficient. Furthermore, the GR elevation angle and precipitation type have a substantial impact on how well the rain reflectivity correlates between the two sensors. It was found that the Ku-band channel possesses the least bias and variability when compared to the NEXRAD instrument’s reflectivity and should therefore be considered more reliable for future tropical storm tracking and tropical region precipitation estimates in regions with no NEXRAD coverage.