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The ubiquity of data, and in particular in MCDM situations, makes it challenging for the Decision Makers (DM) to figure out a way of making proper use of data. This paper presents a three-stage decision framework for DMs to consider the performance range of alternatives holistically. The framework consists of (i) data preparation, (ii) two distance-based Gray Multi-Criteria Decision-Making (MCDM-G) methods using gray interval data to rank the alternatives and (iii) a decision analysis template. For comparison, gray Evaluation based on Distance from Average Solution (EDAS) and gray Multi-Attributive Border Approximation area Comparison (MABAC) methods that rely on arithmetic and geometric mean respectively are used to generate the ranks. The mean-based ranking methods produce stable and efficient ranks in comparison to extremum-based comparison methods, due to their innate nature. The correlation of ranks is analyzed to conclude that the stability of ranks is better when gray interval data is considered. As an example, this paper considers performance range of the 10 criteria used in computing Ease of Doing Business (EDB) index as the gray interval. The sample performance of the G20 countries during the period 2004 to 2020 was used to illustrate the calculations. Further, a general analytic template based on the rank deviation on account of differences in upper and lower bounds of performance helped in classifying the economies as stable leaders, predictable middle and volatile followers. The paper contributes a suitable MCDM and analysis approach when the DM is presented with a gray interval as the alternatives’ performance.

E-learning platforms (ELPs) are revolutionizing the higher education system, as they offer tremendous potential to fulfill instructional plans, safeguard students’ learning rights, and improve teaching quality. Higher education institutions (HEIs) have to consider the selection of ELPs from an organizational perspective, which involves multiple stakeholders like university administrators, students, and educators. Therefore, one can take the ELP selection as a complex multi-criteria decision-making (MCDM) problem. This research proposes a new hybrid MCDM approach on the basis of the fuzzy analytic hierarchy process (FAHP) and the evaluation based on distance from the average solution (EDAS) method to select the best ELPs. First, from the organizational perspective, we extend the technology-organization-environment (TOE) framework by adding an auxiliary resource (AR) dimension and define and prioritize 4 criteria and 14 criteria for evaluating and selecting ELPs. Second, the study uses the fuzzy set theory to process the language evaluation of experts, the FAHP method to determine criteria and sub-criteria weights, and EDAS to rank the alternatives. Finally, based on the importance-performance analysis (IPA) method, we identify the most appropriate strategic options for enhancing competitiveness. Three practical cases of China’s ELP evaluation validate the proposed model’s applicability and superiority by comparing to the two other classic evaluation models. In addition, through analysis, we obtain some encouraging results that provide some references for universities to formulate ELP selection policies and offer suggestions for platform producers to further optimize the design.

In this paper, we have established Cosine Trigonometric Operational Law (CTOL) and constructed Cosine Trigonometric Neutrosophic Weighted Averaging (CT-NWA) operator in the domain of generalized neutrosophic environment, which has been endorsed in a Multi-Criteria Group Decision Making (MCGDM) technique to select the best Anti-Pegasus software. In this study, the expertize recommendations and public opinion are well taken in context of debatable Pegasus issue in the Indian political, bureaucratic and democratic aspects. The problem has been addressed and resolved by using the integrated methodology of Analytical Network Process (ANP) and Evaluation based on Distance from Average Solution (EDAS) strategies. Here, we have optimized the weight factors and the gradation values of the decision makers as well as the sub-criteria of each criterion using Linear Programming (LP) model. Finally, we have performed the comparative analysis from various aspects to justify the reliability of our results.

Polymethylmethacrylate (PMMA) is commonly known as bone cement, having good biocompatibility, mechanical qualities. It is extensively used in the biomedical sector as a synthetic bone material, orthopedic surgery and dental applications. However, some primary machining is required to achieve the tailored shape, size and finish before application in the human body. This study focuses on the machining (drilling) behavior of the developed PMMA-based Hydroxyapatite (PMMA-HA) bio-nano- composites. The machining efficiency and parametric control were estimated using a combined principal component analysis (PCA) module and evaluation based on distance from average solution (EDAS). The Hydroxyapatite (HA) weight percentage (wt.%), spindle speed (SPEED) and tool material (TOOL) viz. HSS, Carbide and TiAlN are chosen according to the Taguchi-based experimental array. The objective is to get the best possible machining responses, such as the material removal rate (MRR), mean surface roughness (Ra) and circularity error ($C_{\mathrm{\text{er}}})$ using the PCA-EDAS hybrid module. The optimal condition is found as the HSS drilling bit, 10%wt.%, SPEED-1428rpm with an improvement of 30.53%, 21.15% and 41.9% in MRR, Ra and $C$-ERROR, respectively. The microstructural investigation scanning electron microscope (SEM) shows the excellent morphology and quality of the drilled hole in the proposed composites. Also, an X-ray diffraction (XRD) analysis of the prepared sample was done to ensure the proper reinforcement. The flexural test shows a significant expansion in the mechanical property due to the presence of HA in PMMA