Evaluation Based On Distance From Average Solution Research Articles

Assessing the sustainable energy storage technologies using single-valued neutrosophic decision-making framework with divergence measure

Energy storage technologies (ESTs) facilitate to handle intermittency of energy resources by storage surplus energy to utilize when it is required. Due to influence of numerous quantitative and qualitative attributes, evaluation of ESTs can be treated as a critical and vague multi-attribute decision-making (MADM) problem. As a type of neutrosophic set, single-valued neutrosophic set (SVNS) has been proven as one of the valuable and flexible ways to handle the imprecise, indeterminate and inconsistent information arises in real applications. Considering the uncertainty in ESTs selection process, this paper aims to propose an integrated MADM methodology by combining the MEthod based on the Removal Effects of Criteria (MEREC) and Evaluation based on Distance from Average Solution (EDAS) techniques with SVNSs. In this methodology, the score function and divergence measure-based procedure is proposed to derive the decision experts’ weights. In this respect, an innovative single-valued neutrosophic (SVN)-divergence measure is introduced with its desirable characteristics. Furthermore, the MEREC technique is extended under SVNS context to determine objective weights of the attributes. Later, an extended EDAS technique is introduced with the combination of SVN-divergence measure and MEREC model to assess and prioritize the alternatives under SVNS environment. To confirm the capability of introduced MADM methodology, a case study of ESTs selection is presented within the context of SVNS. Moreover, the merits of the proposed framework in terms of flexibility and robustness are shown by comparative analysis. Finally, the sensitivity analysis proves the stability of our obtained results.

Analisis Dalam Pendukung Keputusan Seleksi Content Creator Mahasiswa Terbaik Menerapkan Metode EDAS dan ROC

One of the uses of technology is social media, which is an application that can convey information to many people. Around 49 percent of Indonesia's population are active users of social media because the use of social media as a means of promotion is considered easier and more efficient to get a wider target audience. In creating content on social media, a quality content creator is needed. The selection of student content creators at Budi Darma University in Medan is important to be able to get students who are creative and have the potential to become social media content creators. In selecting content creators, students at Budi Darma University have several predetermined criteria, namely the number of social media, the number of tiktok followers, the number of Instagram followers, content production per day, content creativity and content design updates. In the process of selecting student content creators, a system is needed that can speed up and simplify the process of selecting student content creators, namely the SPK (Decision Support System). In this study the EDAS (Evaluation Based on Distance from Average Solution) method and the Rank Oder Centroid (ROC) method used are expected to help the selection process for accepting student content creators. So the best alternative to become a student content creator lies in alternative A8 on behalf of Arsyillah with a score of 182001

Application of Multi-Criteria Decision-Making Methods in Warehouse: A Brief Review

This review paper investigates the application of various multi-criteria decision-making (MCDM) methods in the context of warehouse management, considering papers published from 2010 to date. Warehouses often face many challenges, including efficient inventory management, space optimization, proper resource allocation, and optimal supplier selection. This paper focuses on several well-known MCDM methods that are often used in the context of warehouse management. Full consistency method (FUCOM), analytic hierarchy process (AHP), the technique for order of preference by similarity to ideal solution (TOPSIS), weighted aggregated sum product assessment (WASPAS), criteria importance through intercriteria correlation (CRITIC), measurement of alternatives and ranking according to the compromise solution (MARCOS), best worst method (BMW), evaluation based on distance from average solution (EDAS), correlation coefficient and the standard deviation (CCSD), indifference threshold-based attribute ratio analysis (ITARA), and simple additive weighting (SAW) are some of the methods reviewed in the paper, as well as certain fuzzy versions of the methods. This review paper provides a brief comprehensive overview of the application of these methods in the context of warehouse management. Data collection leads to results that tell us that the methods are mainly used in solving problems during the selection of warehouse location, the selection of warehouse equipment, and also in the management of the warehouse itself and the performance of its management. It has also been seen that the methods are useful even in "green" logistics, as well as in inventory management.

A novel MAGDM-based methodology with SVNSs and applications to collaborative innovation ability training mode selection of virtual teaching and research platforms

The virtual teaching and research platform is a cognitive and practical activity for relevant teachers engaged in education and teaching research to improve and improve the quality of education and teaching, relying on the unique functions and resource advantages of the network, using information technology and various network tools to carry out teaching, feedback, etc., and to carry out education and teaching exchange, discussion and other education and teaching research with other administrative organizations or teachers in space regions. It can break the constraints of time, space, organization and other factors. It is an effective form of educational and teaching activities carried out for common goals. It integrates teaching, guidance, discussion, exchange, resource sharing and other functions, and is an important part of the “double training plan”. The collaborative innovation ability training mode selection of virtual teaching and research platforms as a multi-attribute group decision making (MAGDM) problem. In this paper, the Evaluation Based on Distance from Average Solution (EDAS) method is extended to the single-valued neutrosophic sets (SVNSs) and single-valued neutrosophic EDAS method (SVN-EDAS) is proposed to solve MAGDM problem. Finally, the collaborative innovation ability training mode selection of virtual teaching and research platforms is given to show the SVN-EDAS model and some comparative analysis is conduct to depict the advantages of SVN-EDAS.

A survey on blockchain envisioned attribute based access control for internet of things: Overview, comparative analysis, and open research challenges

The Internet of Things (IoT) network is rapidly expanding due to sudden technological advancements, which enable objects to become intelligent and contribute to the network. Before permitting a newly added IoT device to communicate with the network, it is essential to provide access and authenticate the device’s legitimacy by ensuring that it has not been tampered. Recently, blockchain technology has been integrated into Attribute Based Access Control (ABAC) protocols to supply a more robust security mechanism for access control in IoT. The IoT and blockchain-based ABAC serve as the foundation of this survey, offering a comprehensive introduction to both topics. Additionally, several security concerns and vulnerabilities associated with Blockchain Envisioned ABAC (BE-ABAC) are presented. A Comparison with related surveys has also been made. Besides, we also present a comparative analysis based on evaluation based on Distance from Average Solution (EDAS) to rank the best schemes among the suggested BE-ABAC schemes. In conclusion, we discuss some open research challenges in an IoT network that uses blockchain to manage access control.

Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood risk

Flood risk assessment is a key step in flood management and mitigation, and flood risk maps provide a quantitative measure of flood risk. Therefore, integration of deep learning – an updated version of machine learning techniques – and multi-criteria decision making (MCDM) models can generate high-resolution flood risk maps. In this study, a novel integrated approach has been developed based on multiplicative long short-term memory (mLSTM) deep learning models and an MCDM ensemble model to map flood risk in the Minab-Shamil plain, southern Iran. A flood hazard map generated by the mLSTM model is based on nine critical features selected by GrootCV (distance to the river, vegetation cover, variables extracted from DEM (digital elevation model) and river density) and a flood inventory map (70% and 30% data were randomly selected as training and test datasets, respectively). The values of all criteria used to assess model accuracy performance (except Cohens kappa for train dataset = 86, and for test dataset = 84) achieved values greater than 90, which indicates that the mLSTM model performed very well for the generation of a spatial flood hazard map. According to the spatial flood hazard map produced by mLSTM, the very low, low, moderate, high and very high classes cover 26%, 35.3%, 20.5%, 11.2% and 7% of the total area, respectively. Flood vulnerability maps were produced by the combinative distance-based assessment (CODAS), the evaluation based on distance from average solution (EDAS), and the multi-objective optimization on the basis of simple ratio analysis (MOOSRA), and then validated by Spearman's rank correlation coefficients (SRC). Based on the SRC, the three models CODAS, EDAS, and MOOSRA showed high-ranking correlations with each other, and all three models were then used in the ensemble process. According to the CODAS-EDAS-MOOSRA ensemble model, 21.5%, 34.2%, 23.7%, 13%, and 7.6% of the total area were classified as having a very low to very high flood vulnerability, respectively. Finally, a flood risk map was generated by the combination of flood hazard and vulnerability maps produced by the mLSTM and MCDM ensemble model. According to the flood risk map, 27.4%, 34.3%, 14.8%, 15.7%, and 7.8% of the total area were classified as having a very low, low, moderate, high, and very high flood risk, respectively. Overall, the integration of mLSTM and the MCDM ensemble is a promising tool for generating precise flood risk maps and provides a useful reference for flood risk management.

A multi-attribute decision-making fusion model for stock trading with customizable investor personality traits in a picture fuzzy environment

In this paper, a fuzzy logic-based machine learning (ML) algorithm is introduced. This proposed ML algorithm accepts picture fuzzy sets (PFS) as the fuzzified input and incorporates genetic algorithm (GA) during the training process. The proposed ML algorithm is then incorporated into two well-known decision-making methods, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Evaluation Based on Distance from Average Solution (EDAS). These two decision-making methods and the proposed ML algorithm are then applied to solve a multi-attribute decision-making (MADM) problem related to the evaluation and ranking of public listed companies based on their stock performance, in accordance with investors’ personalities. The actual daily closing stock price of five public listed companies from the big market capitalization (Big Cap) category traded in the Kuala Lumpur Stock Exchange (KLSE) for a period of 10 years is used as the datasets for this study. Monte Carlo simulation is used to verify the accuracy of the results. In addition, a comprehensive comparative study of some recent PFS-based decision-making methods in the existing literature and the proposed methods is conducted, and all the typical instances of the investors’ personalities are observed. The results obtained through this comparative study corroborates the results obtained via the proposed methods, and this proves the effectiveness of the proposed methods. The differences in the results obtained via the different methods are analyzed and discussed, and this again proves that the results obtained via the proposed methods are effective and consistent with the judgments of human experts.

Modified EDAS method for spherical fuzzy multiple attribute group decision making and applications to English classroom teaching quality evaluation

Classroom teaching is an important link related to the quality of teaching and talent cultivation. In the implementation of classroom teaching, we should fully attach importance to the main position of students, the role of educational technology and information technology in teaching activities, make use of the latest educational ideas and educational concepts, and combine the actual situation of college English teaching and college English teaching in China, and attach importance to foreign language teaching theories and practices at home and abroad, Establish monitoring indicators and monitoring system for college English teaching quality. Under the guidance of effective monitoring indicators, teachers’ teaching concepts can be updated and improved in real time to achieve better teaching results. At the same time, the quality assurance and monitoring system of college English teaching can be continuously improved to make it more perfect. The English classroom teaching quality evaluation could be deemed as a classic multiple attribute group decision making (MAGDM) problem. Spherical fuzzy sets (SFSs) can excavate the uncertainty and fuzziness in MAGDM more effectively and deeply. This article we first present a novel score function to compare spherical fuzzy numbers (SFNs) more directly and efficiently. Then, on basis of evaluation based on distance from average solution (EDAS), a novel spherical fuzzy EDAS (SF-EDAS) method is built for dealing with MAGDM. Moreover, when the attribute weights are completely unknown, the MEthod based on the Removal Effects of Criteria (MEREC) is extended to spherical fuzzy environment (SFE) to reasonably acquire the attribute weights. Finally, SF-EDAS approach is used for English classroom teaching quality evaluation to prove practicability of the developed method and compare SF-EDAS method with existing methods to further demonstrate its legitimacy and superiority.

An integrated multi-criteria decision-making approach for overcoming barriers to green supply chain management and prioritizing alternative solutions

Green practices are no longer a wish but a must in business. Despite that, businesses interested in implementing green supply-chain practices encounter several barriers. This study aims to analyze the barriers and solution proposals related to green supply chain practices in the manufacturing sector. Face-to-face interviews with eleven decision-makers in the manufacturing industry who know about green supply chain practices provided the data for the study. In the analysis phase, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was used to analyze the barriers that are effective in implementing green supply chain practices. Combining it with fuzzy logic and the Evaluation based on Distance from Average Solution (EDAS) and Complex Proportional Assessment (COPRAS) methods were used to evaluate solution proposals. The Copeland method was used to combine the results and find the final rankings. The three most important barriers are identified as the lack of technological hardware and software infrastructure, fear of failure, and non-adoption of technological improvements. The solutions that can be used in the implementation phase are training employees, collaborating with other businesses, and improving government support and incentives.

A Novel Integrated Neutrosophic Cosine Operator Based Linear Programming ANP-EDAS MCGDM Strategy to Select Anti-Pegasus Software

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.

Numerical investigations and evaluation of a puga geothermal reservoir with horizontal wells using a fully coupled thermo-hydro-geomechanical model (THM) and EDAS associated with AHP

Puga geothermal reservoir in India gives promising results for the extraction of geothermal energy. On the other hand, no systematic studies were conducted to analyze the Puga geothermal reservoir's thermo-hydro-geomechanical behavior while heat extraction. Most of the researchers concentrated more on the geological and geophysical aspects of the Puga hot spring and provided basic geological data. This information is now being examined as input towards the construction of geothermal reservoirs. In this study, the THM model is enhanced by including additional dynamic fluid, rock, and fracture features, and an integrated assessment approach is anticipated to improve the heat extraction possibilities of the Puga geothermal reservoir. The variation in rock temperature in the vicinity of the production well is influenced less compared to the vicinity of the injection well. The low-temperature region is initially befalling in the vicinity of hydraulic fractures with high conductivity, flow rate, and strong heat convection further it extends to the rock matrix. Twelve injection-production scenarios were proposed for the Puga geothermal reservoir to extract heat energy with single and dual production wells and single injection well. Scenario-2 and scenario-8 are significantly showing high production temperatures when compared to other proposed scenarios. A multi-index-based evaluation system that consists of geothermal reservoir life, thermal breakthrough time, reservoir impedance, heat power, and heat recovery is employed to examine the geothermal extraction from the Puga geothermal reservoir. An integrated evaluation technique was projected for geothermal extraction which is created by coupling the analytical hierarchy process (AHP) and evaluation based on distance from average solution (EDAS) techniques. The performance of twelve injection-production scenarios of the Puga geothermal reservoir are evaluated using the integrated evaluation technique. The results show that scenario-11 and scenario-9 were found to be the top two scenarios for the extraction of heat from the Puga geothermal reservoir. The results demonstrate that the proposed integrated evaluation system and improved THM model can be used for the evaluation of geothermal reservoirs effectively.

Multi-criteria group decision-making method in disposal of municipal solid waste based on cubic Pythagorean fuzzy EDAS approach with incomplete weight information

Municipal solid waste management (MSWM) has always been a typical issue in India. The rapid population growth, impetuous adoption of modern lifestyle, and fast urbanization resulted in India’s swift generation of complicated municipal solid waste (MSW). Consequently, municipalities of different cities in India face various difficulties in MSW collection, treatment and disposal methodology. Inefficient or poorly managed MSW causes numerous troubles, including air pollution, soil fouling, contamination in drinking water and health hazard, and it also impacts socio-economic deterioration. MSWM technique selection is based on several criteria, which are generally imprecise, uncertain, and vague. This paper uses the cubic Pythagorean fuzzy number to comprise the fuzzy characteristics of the criteria value concerning the five alternatives (Thermochemical methods in MSW treatment and disposal). The best MSW treatment and disposal method is selected amongst the five alternatives under the novel multi-criteria group decision-making (MCGDM) approach, the cubic Pythagorean fuzzy EDAS (Evaluation based on Distance from Average Solution) in the Indian context. The weight information of the criteria has yet to be wholly known. A non-linear optimization scheme is proposed to determine the criteria weights that are partially known to maximize the generalized total distance measure between alternatives corresponding to all criteria. The present method is explained with a case study. Finally, a comparative analysis is drawn between the present and some existing methods, showing that the present method is consistent and robust enough to apply in practice.

On Solving Parametric Optimization Problem of an End Milling Process for Machining of Al 1070 using MCDM Techniques: A Comparative Analysis

ABSTRACT Determining the optimal intermix of various input parameters for an end milling process is an important task to explore its fullest performance along with the achievement of the desired responses. Several multi-criteria decision-making techniques, like technique for order preference by similarity to ideal solution (TOPSIS), grey relational analysis and multiobjective optimisation on the basis of ratio analysis (MOORA), have been applied for solving parametric optimisation problems of milling processes while assigning equal importance to the considered responses. In this paper, the optimal combinations of cutting speed, feed rate and depth of cut of an end milling process during the machining of Al 1070 alloy are determined using the weighted sum model (WSM), weighted product model (WPM), weighted aggregated sum product assessment (WASPAS), MOORA, TOPSIS, evaluation based on distance from average solution (EDAS), additive ratio assessment (ARAS) and complex proportional assessment (COPRAS) methods with the allocation of unequal weights to the responses determined by standard deviation and entropy techniques. The derived solutions are also compared when the same importance is assigned to the responses. From the results, a parametric combination of 255 rpm cutting speed, 82 mm/min feed rate and 0.75 depth of cut is identified by WSM, WPM, WASPAS, COPRAS, TOPSIS and ARAS methods to achieve the optimal values of material removal rate (MRR) and average surface roughness (Ra) as 595.23 mm3/min and 13.79 µm, respectively. It is also noticed that both feed rate and depth of cut significantly influence MRR and Ra during the machining of Al 1070 alloy.

Real-World Driver Stress Recognition and Diagnosis Based on Multimodal Deep Learning and Fuzzy EDAS Approaches.

Mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in damage to humans, vehicles, and infrastructure. Likewise, persistent mental stress could lead to the development of mental, cardiovascular, and abdominal disorders. Preceding research in this domain mostly focuses on feature engineering and conventional machine learning approaches. These approaches recognize different levels of stress based on handcrafted features extracted from various modalities including physiological, physical, and contextual data. Acquiring good quality features from these modalities using feature engineering is often a difficult job. Recent developments in the form of deep learning (DL) algorithms have relieved feature engineering by automatically extracting and learning resilient features. This paper proposes different CNN and CNN-LSTSM-based fusion models using physiological signals (SRAD dataset) and multimodal data (AffectiveROAD dataset) for the driver's two and three stress levels. The fuzzy EDAS (evaluation based on distance from average solution) approach is used to evaluate the performance of the proposed models based on different classification metrics (accuracy, recall, precision, F-score, and specificity). Fuzzy EDAS performance estimation shows that the proposed CNN and hybrid CNN-LSTM models achieved the first ranks based on the fusion of BH, E4-Left (E4-L), and E4-Right (E4-R). Results showed the significance of multimodal data for designing an accurate and trustworthy stress recognition diagnosing model for real-world driving conditions. The proposed model can also be used for the diagnosis of the stress level of a subject during other daily life activities.

An Effective Analysis of Risk Assessment and Mitigation Strategies of Photovoltaic Power Plants Based on Real Data: Strategies, Challenges, Perspectives, and Sustainability

Due to the extensive usage of fossil fuels such as coal, oil, and gas, the energy crisis and environmental pollution issues have garnered global attention, making the creation of clean, renewable energy an unavoidable option. Solar photovoltaic energy production is regarded as one of the most promising technologies owing to its safety, dependability, and lack of environmental impact. However, the adoption of photovoltaic systems comes with some risks that may affect their deployment. This paper examines the risks of sustainable photovoltaic power plants through a realistic case study. A comprehensive approach is presented through which consultants can use linguistic variables to express their opinions about risks, priorities, and strategies for dealing with them. Evaluating and prioritizing risk assessment is a complex task that requires consideration of multiple criteria. Therefore, this paper proposes a hybrid multicriteria decision-making (MCDM) approach to deal with risks and their priorities. The risks of five aspects of sustainability were considered: the economic, technical, institutional, social, and environmental aspects. The developed approach consists of two decision-making methods: the criteria importance through intercriteria correlation (CRITIC) method, which is used to determine the weights of the criteria, and the evaluation based on distance from average solution (EDAS) method, which is used to arrange risks according to their priorities. The developed approach is performed in a neutrosophic environment and uses type-2 neutrosophic numbers (T2NN) to address uncertainty. Also, the paper adopts a developed model to determine the consultants’ reputation weights instead of assuming them. In addition, the model was applied to classify the risks of each aspect of sustainability into three levels, which are high, medium, and normal risks. The results indicate that 52% of the risks in all aspects are high risks, 36% are medium risks, and 12% are usual risks. Also, sensitivity analysis was performed to prove the correctness and stability of the developed approach with differences in weights.

A novel linguistic decision making approach based on attribute correlation and EDAS method

One of characteristics of large-scale linguistic decision making problems is that decision information with respect to decision making attributes is derived from multi-sources information. In addition, the number of decision makers, alternatives or criteria of decision making problems in the context of big data are increasingly large. Correlation analysis between decision making attributes has become an important issue of large-scale linguistic decision making problems. In the paper, we concentrate on correlation analysis between decision making attributes to deal with large-scale decision making problems with linguistic intuitionistic fuzzy values. Firstly, we proposed a new similarity measure between two linguistic intuitionistic fuzzy sets to formally define correlation between decision making attributes. Then we propose linguistic intuitionistic fuzzy reducible weighted Maclaurin symmetric mean (LIFRWMSM) operator and linguistic intuitionistic fuzzy reducible weighted dual Maclaurin symmetric mean (LIFRWDMSM) operator to aggregate linguistic intuitionistic fuzzy value decision information of correlational decision making attributes, and analyze several important properties of the two operator. Inspired by evaluation based on distance from average solution (EDAS) method, we design a solution scheme and decision steps to deal with large-scale linguistic intuitionistic fuzzy decision making problems. To show the effectiveness and usefulness of the proposed decision method, we employ the choice of buying a house and the selection of travel destination to demonstrate our method and make comparative analysis with others aggregation operators or methods.

Comparison of main metal industry sub-business lines from occupational health and safety perspective using CIRITIC and EDAS methods

The basic metal industry is one of the most economically important business lines in the manufacturing industry. According to the Social Security Institution (SSI) 2020 data, the basic metal industry in Türkiye is represented by 16 sub-businesses lines. In this study, it is aimed to evaluate the risk levels of the sub-business lines of the basic metal industry class, which is included in the SSI economic activity classification. Occupational Health and Safety data included in the 2020 SSI statistics were used to determine risk levels. The number of employees who have an occupational disease, the number of deaths because of work accidents, the period of temporary incapacity for work (inpatient), the period of temporary incapacity for work (outpatient), and the number of employees who have had a work accident are the criteria selected from these data. In the evaluation made according to these criteria, Multi-Criteria Decision-Making methods were used. Criteria Importance Through Intercriteria Correlation (CRITIC) and Evaluation Based on Distance from Average Solution (EDAS) methods were used to determine and classify the importance levels of the criteria determined for 16 sectors. As a result of the analysis, it has been determined that the riskiest sector among the main metal industry sub-business lines is the “Manufacture of basic iron and steel products and ferrous alloys” and the most important criterion is the number of insured persons with occupational diseases.

A multicriteria decision-making approach for prioritizing renewable energy resources for sustainable electricity generation in Benin

Benin’s rapid population, economic, and industrial growth will increase energy demand. Hence, Benin’s government is keen to increase its power capacity to meet future demand. Renewable energy sources are significant options that governments could explore when developing their sustainable energy strategies. Prioritizing energy sources for a country’s sustainable development requires a decision-making process. In this study, multicriteria decision-making (MCDM) methods are used to prioritize alternatives such as solar photovoltaic (PV), concentrated solar power (CSP), wind energy, hydropower, and bioenergy suitable for sustainable electrification in Benin. The study employed eighteen (18) criteria classified under the four pillars of sustainability (technical, social, economic, and environmental) to evaluate the alternatives. Thereafter, the criteria importance through inter-criteria correlation (CRITIC) and entropy methods, which were employed to compute criteria weights and incorporated into the evaluation based on distance from average solution (EDAS) method to rank the alternatives. The findings from the EDAS rankings show that solar PV is the optimal alternative, ranking in the first position, followed by wind energy in the second position. CSP, hydropower, and bioenergy rank in the third, fourth, and fifth positions. In addition, EDAS findings correlated fully with those of other MCDM approaches evaluated on the same decision matrix. This indicates that solar PV is Benin’s optimal technology for sustainable electricity generation. The study’s findings are critical for policymakers developing Benin’s renewable energy sector.

Evaluation of gamification techniques in learning abilities for higher school students using FAHP and EDAS methods.

The rapid development of information technology has made a wide range of cutting-edge technologies accessible, supporting the flourishing of human existence. Modern technology has made it possible for new computer-based technological strategies like gamification. The pedagogical framework is based on the "gamification" game format, which is one of the most recent teaching strategies and has an engaging component for students. Gamification, flipped learning, and problem-based learning are three examples of the technical aspect of escape rooms. In the academic setting, gamification aims to boost student engagement and motivation in order to produce a better user experience. Gamification has been found to increase levels of participation, foster it, and improve activity outcomes. Gamification is recommended in educational settings to improve students' achievement, focus, and contentment in light of these benefits. In order to establish an effective learning environment where students may effectively improve their learning capacities and boost their performance, it can be difficult to select a higher performing technique among the available techniques due to the ongoing use of gamification techniques. The fuzzy analytical hierarchy process (FAHP) and evaluation based on distance from average solution (EDAS) are applied in order to determine the criterion weighting and assess the techniques in order to make a good decision. The presented paper analyzed numerous game-based learning techniques along with their applications in the educational field. Additionally, ten criteria and eight gamification methodologies are used to assess and pick the prior pertinent works. By utilizing the suggested approaches, the decision problem has been resolved. The FAHP approach is used in the suggested analysis to evaluate the criteria and determine their weights. Then, using the EDAS method, places are assigned to the chosen procedures based on their evaluation score and criterion weighting. The results of the appraisal show that the gamification technique with the highest production takes first place and is regarded as the best-performing and most successful technique. On the other hand, it is clear that the technique with the lowest production takes the bottom spot and is referred to as the least expensive and lowest performing technique. In order to increase students' motivation, which could have a substantial impact on learning, it has been discovered that gamification is a feasible strategy.

Novel decision aid model for green supplier selection based on extended EDAS approach under pythagorean fuzzy Z-numbers

The main objective of this study is to identify the green suppliers that would most effectively assist manufacturing producers in implementing green manufacturing production while including uncertainty and reliability in their decision-making. For this firstly, we justify and manifest the idea of Pythagorean Fuzzy Z-numbers (PyFZNs). It has significant implications for improving the effectiveness of decision-making processes in several theories of uncertainty. It can more flexibly explain real-world data and human cognition due to its capacity to express imprecise and reliable information. Thus it is a more accurate mathematical tool for addressing accuracy and uncertainty. Secondly, we defined the Pythagorean fuzzy Z-number arithmetic aggregation operators and geometric aggregation operators. Thirdly, based on the proposed operators and EDAS (Evaluation based on distance from average solution) approach, a fast decision model is designed to deal with the issue of multi-criteria decision-making. Finally, using PyFZN data we also provide a numerical example to demonstrate the usability of the created multicriteria decision-making (MDM) approach. Moreover, a case study also proves its efficacy.