Neutrosophic Data Envelopment Analysis A Comprehensive Review And Current Trends

The concept of a neutrosophic set is a comprehensive extension of both fuzzy sets and Intuitionistic Fuzzy Sets (IFSs). It allows decision makers to assign three distinct membership degrees, enabling a more precise representation of uncertainty. Neutrosophic Data Envelopment Analysis (Neu-DEA) is an extended version of the Data Envelopment Analysis (DEA) and Fuzzy DEA (FDEA) concepts. It aims to assess the performance of Decision Making Units (DMUs) within a neutrosophic environment. Neu-DEA specifically tackles the challenges associated with evaluating performance when the input and output data are incomplete, ambiguous, or unsure. As a result, the Neu-DEAs have attracted substantial attention from scholars and academics. This article aims to provide an academic overview of the present state, development patterns, and future research directions of the Neu-DEA research. To do this, the study examines relevant publications using two analytical approaches: description analysis and literature review.

Data Envelopment Analysis is one of the paramount mathematical methods to compute the general performance of organizations, which utilizes similar sources to produce similar outputs. Original DEA schemes involve crisp information of inputs and outputs that may not always be accessible in real-world applications. Nevertheless, in some cases, the values of the data are information with indeterminacy, impreciseness, vagueness, inconsistent, and incompleteness. Furthermore, the conventional DEA models have been originally formulated solely for desirable outputs. However, undesirable outputs may additionally be present in the manufacturing system, which wishes to be minimized. To tackle the mentioned issues and in order to obtain a reliable measurement that keeps original advantage of DEA and considers the influence of undesirable factors under the indeterminate environments, this paper presents a neutrosophic DEA model with undesirable outputs.

The best-worst method (BWM) is a multiple criteria decision-making (MCDM) method for evaluating a set of alternatives based on a set of decision criteria where two vectors of pairwise comparisons are used to calculate the importance weight of decision criteria. The BWM is an efficient and mathematically sound method used to solve a wide range of MCDM problems by reducing the number of pairwise comparisons and identifying the inconsistencies derived from the comparison process. In spite of its simplicity and efficiency, the BWM does not consider the decision-makers’ (DMs’) confidence about their pairwise comparisons. We propose a neutrosophic enhancement to the original BWM by introducing two new parameters as the DMs’ confidence in the best-to-others preferences and the DMs’ confidence in the others-to-worst preferences. We present two real-world cases to illustrate the applicability of the proposed neutrosophic enhanced BWM (NE-BWM) by considering confidence rating levels of the DMs.

N-Norm and N-conorm are extended in Neutrosophic Logic/Set.

The analytic hierarchy process (AHP) is recognised as one of the most commonly applied methods in the multiple attribute decision-making (MADM) literature. In the AHP, encompassing uncertainty feature necessitates using suitable uncertainty theories, since dealing efficiently with uncertainty in subjective judgements is of great importance in real-world decision-making problems. The neutrosophic set (NS) theory and grey systems are two reliable uncertainty theories which can bring considerable benefits to uncertain decision-making. Thea im of this study is to improve uncertain decision-making by incorporating advantages of the NS and grey systems theories with the AHP in investigating sustainability through agility readiness evaluation in large manufacturing plants.

In this study, we give some concepts concerning the neutrosophic sets, single valued neutrosophic sets, interval-valued neutrosophic sets, bipolar neutrosophic sets, neutrosophic hesitant fuzzy sets, inter-valued neutrosophic hesitant fuzzy sets, refined neutrosophic sets, bipolar neutrosophic refined sets, multi-valued neutrosophic sets, simplified neutrosophic linguistic sets, neutrosophic over/off/under sets, rough neutrosophic sets, rough bipolar neutrosophic sets, rough neutrosophic hyper-complex set, and their basic operations.

This book introduces readers to the use of R codes for optimization problems. First, it provides the necessary background to understand data envelopment analysis (DEA), with a special emphasis on fuzzy DEA. It then describes DEA models, including fuzzy DEA models, and shows how to use them to solve optimization problems with R. Further, it discusses the main advantages of R in optimization problems, and provides R codes based on real-world data sets throughout. Offering a comprehensive review of DEA and fuzzy DEA models and the corresponding R codes, this practice-oriented reference guide is intended for masters and Ph.D. students in various disciplines, as well as practitioners and researchers.