An extended hybrid fuzzy multi-criteria decision model for sustainable and resilient supplier selection.

The formalization and solution of supplier selection problems (SSPs) based on sustainable (economic, environmental, and social) indicators have become a fundamental tool to perform a strategic analysis of the whole supply chain process and maximize the competitive advantage of firms. Over the last decade, sustainability issues have been often considered in combination with resilient indexes leading to the study of sustainable-resilient supplier selection problems (SRSSPs). The current research on sustainable development, particularly concerned with the strong impact that the recent COVID-19 pandemic has had on supply chains, has been paying increasing attention to the resilience concept and its role within SSPs. This study proposes a hybrid fuzzy multi-criteria decision making (MCDM) method to solve SRSSPs. The fuzzy best-worst method is used first to determine the importance weights of the selection criteria. A combined grey relational analysis and the technique for order of preference by similarity to ideal solution (TOPSIS) method is used next to evaluate the suppliers in a fuzzy environment. Triangular fuzzy numbers (TFNs) are used to express the weights of criteria and alternatives to account for the ambiguity and uncertainty inherent to subjective evaluations. However, the proposed method can be easily extended to other fuzzy settings depending on the uncertainty facing managers and decision-makers. A real-life application is presented to demonstrate the applicability and efficacy of the proposed model. Sixteen evaluation criteria are identified and classified as economic, environmental, social, or resilient. The results obtained through the case study show that "pollution control," "environmental management system," and "risk awareness" are the most influential criteria when studying SRSSPs related to the manufacturing industry. Finally, three different sensitivity analysis methods are applied to validate the robustness of the proposed framework, namely, changing the weights of the criteria, comparing the results with those of other common fuzzy MCDM methods, and changing the components of the principal decision matrix.

A hybrid data envelopment analysis-artificial neural network prediction model for COVID-19 severity in transplant recipients.

In an overwhelming demand scenario, such as the SARS-CoV-2 pandemic, pressure over health systems may outburst their predicted capacity to deal with such extreme situations. Therefore, in order to successfully face a health emergency, scientific evidence and validated models are needed to provide real-time information that could be applied by any health center, especially for high-risk populations, such as transplant recipients. We have developed a hybrid prediction model whose accuracy relative to several alternative configurations has been validated through a battery of clustering techniques. Using hospital admission data from a cohort of hospitalized transplant patients, our hybrid Data Envelopment Analysis (DEA)-Artificial Neural Network (ANN) model extrapolates the progression towards severe COVID-19 disease with an accuracy of 96.3%, outperforming any competing model, such as logistic regression (65.5%) and random forest (44.8%). In this regard, DEA-ANN allows us to categorize the evolution of patients through the values of the analyses performed at hospital admission. Our prediction model may help guiding COVID-19 management through the identification of key predictors that permit a sustainable management of resources in a patient-centered model. Supplementary Information: The online version contains supplementary material available at 10.1007/s10462-021-10008-0.

Modeling patients as decision making units: evaluating the efficiency of kidney transplantation through data envelopment analysis.

The main applications of Data Envelopment Analysis (DEA) to medicine focus on evaluating the efficiency of different health structures, hospitals and departments within them. The evolution of patients after undergoing a medical procedure or their response to a given treatment are not generally studied through this programming technique. In addition to the difficulty inherent to the collection of this type of data, the use of a technique that is mainly applied to evaluate the efficiency of decision making units representing industrial and production structures to analyze the evolution of human patients may seem inappropriate. In the current paper, we illustrate how this is not actually the case and implement a decision engineering approach to model kidney transplantation patients as decision making units. As such, patients undergo three different phases, each composed by specific as well as interrelated variables, determining the potential success of the transplantation process. DEA is applied to a set of 12 input and 6 output variables - retrieved over a 10-year period - describing the evolution of 485 patients undergoing kidney transplantation from living donors. The resulting analysis allows us to classify the set of patients in terms of the efficiency of the transplantation process and identify the specific characteristics across which potential improvements could be defined on a per patient basis.