Kattur Soundarapandian Ravichandran
Bio: Kattur Soundarapandian Ravichandran is an academic researcher from Shanmugha Arts, Science, Technology & Research Academy. The author has contributed to research in topics: Fuzzy logic & Probabilistic logic. The author has an hindex of 14, co-authored 72 publications receiving 462 citations.
TL;DR: In this paper, an integrated decision-making framework for the systematic selection of a renewable energy source (RES) from a set of RESs based on sustainability attributes is proposed, and the results are compared with state-of-the-art methods.
Abstract: This paper proposes an integrated decision-making framework for the systematic selection of a renewable energy source (RES) from a set of RESs based on sustainability attributes. A real case study of RES selection in Karnataka, India, using the framework is demonstrated, and the results are compared with state-of-the-art methods. The main reason for developing this framework is to handle uncertainty and vagueness effectively by reducing human intervention. Systematic selection of RESs also reduces inaccuracies and promotes rational decision-making. In this paper, q-rung orthopair fuzzy information is adopted to minimize subjective randomness by providing a flexible and generalized preference style. Further, the study found systematic approaches for imputing missing values, calculating attributes’ and decision-makers’ weights, aggregation or preferences, and prioritizing RESs, which are integrated into the framework. . Comparing the proposed framework with state-of-the-art-methods shows that (i) biomass and solar are suitable RESs for the process under consideration in Karnataka, (ii) the proposed framework is consistent with state-of-the-art methods, (iii) the proposed framework is sufficiently stable even after weights of attributes and decision makers are altered, and (iv) the proposed framework produces broad and sensible rank values for efficient backup management. These results validate the significance of the proposed framework.
TL;DR: A novel framework based on COPRAS (Complex Proportional Assessment) method and SWARA (Step-wise Weight Assessment Ratio Analysis) approach is proposed to evaluate and select the desirable sustainable supplier within the HFSs context and is more consistent and powerful than other existing approaches.
Abstract: The selection of sustainable supplier is an extremely important for sustainable supply chain management (SSCM). The assessment process of sustainable supplier selection is a complicated task for decision experts due to involvement of several qualitative and quantitative criteria. As the uncertainty is commonly occurred in sustainable supplier selection problem and hesitant fuzzy set (HFS), an improvement of Fuzzy Set (FS), has been proved as one of the efficient and superior ways to express the uncertain information arisen in practical problems. The present study proposes a novel framework based on COPRAS (Complex Proportional Assessment) method and SWARA (Step-wise Weight Assessment Ratio Analysis) approach to evaluate and select the desirable sustainable supplier within the HFSs context. In the proposed method, an extended SWARA method is employed for determining the criteria weights based on experts’ preferences. Next, to illustrate the efficiency and practicability of the proposed methodology, an empirical case study of sustainable supplier selection problem is taken under Hesitant Fuzzy (HF) environment. Further, sensitivity analysis is performed to check the stability of the presented methodology. At last, a comparison with existing methods is conducted to verify the strength of the obtained result. The final outcomes confirm that the developed framework is more consistent and powerful than other existing approaches.
TL;DR: In this paper, the authors used the hesitant fuzzy decision-making (DM) method to select the best antiviral therapy to treat the mild symptom of COVID-19, which is a new disease spread by a virus of the corona family called a novel coronavirus.
Abstract: The whole world is presently under threat from Coronavirus Disease 2019 (COVID-19), a new disease spread by a virus of the corona family, called a novel coronavirus To date, the cases due to this disease are increasing exponentially, but there is no vaccine of COVID-19 available commercially However, several antiviral therapies are used to treat the mild symptoms of COVID-19 disease Still, it is quite complicated and uncertain decision to choose the best antiviral therapy to treat the mild symptom of COVID-19 Hesitant Fuzzy Sets (HFSs) are proven effective and valuable structures to express uncertain information in real-world issues Therefore, here we used the hesitant fuzzy decision-making (DM) method This study has chosen five methods or medicines to treat the mild symptom of COVID-19 These alternatives have been ranked by seven criteria for choosing an optimal method The purpose of this study is to develop an innovative Additive Ratio Assessment (ARAS) approach to elucidate the DM problems Next, a divergence measure based procedure is developed to assess the relative importance of the criteria rationally To do this, a novel divergence measure is introduced for HFSs A case study of drug selection for COVID-19 disease is considered to demonstrate the practicability and efficacy of the developed idea in real-life applications Afterward, the outcome shows that Remdesivir is the best medicine for patients with mild symptoms of the COVID-19 Sensitivity analysis is presented to ensure the permanence of the introduced framework Moreover, a comprehensive comparison with existing models is discussed to show the advantages of the developed framework Finally, the results prove that the introduced ARAS approach is more effective and reliable than the existing models
TL;DR: A new decision framework with minimum subjective randomness is proposed under q-ROFS context and the strengths and weaknesses of the framework are discussed by using comparative analysis with other methods.
Abstract: As a powerful generalization to intuitionistic fuzzy set (IFS), q-rung orthopair fuzzy set (q-ROFS) is proposed by Yager, which can effectively mitigate the weakness of IFS and provide wider space for preference elicitation. Based on the literature analysis on q-ROFS, a comprehensive decision framework for promoting rational decision-making is lacking. Motivated by the superiority of q-ROFS and to circumvent the issue, in this paper, a new decision framework with minimum subjective randomness is proposed under q-ROFS context. Initially, decision makers’ (DMs’) relative importance is systematically calculated by extending evidence-based Bayes approximation to q-ROFS. Later, a new operator is proposed for aggregating DMs’ preferences by extending generalized Maclaurin symmetric mean (GMSM) to q-ROFS context. Attributes’ weight values are calculated by using newly proposed q-rung orthopair fuzzy statistical variance (q-ROFSV) method and objects are prioritized by extending the popular VIKOR method to q-ROFS context. Finally, the practical use of the proposed decision framework is validated by using a green supplier selection problem and the strengths and weaknesses of the framework are discussed by using comparative analysis with other methods.
TL;DR: A new decision framework is proposed, which provides scientific methods for multi-attribute group decision-making (MAGDM) and the superiorities and weaknesses of the framework are discussed in comparison with state-of-the-art methods.
Abstract: As an attractive generalization of the intuitionistic fuzzy set (IFS), q-rung orthopair fuzzy set (q-ROFS) provides the decision makers (DMs) with a wide window for preference elicitation. Previous studies on q-ROFS indicate that there is an urge for a decision framework which can make use of the available information in a proper manner for making rational decisions. Motivated by the superiority of q-ROFS, in this paper, a new decision framework is proposed, which provides scientific methods for multi-attribute group decision-making (MAGDM). Initially, a programming model is developed for calculating weights of attributes with the help of partially known information. Later, another programming model is developed for determining the weights of DMs with the help of partially known information. Preferences from different DMs are aggregated rationally by using the weights of DMs and extending generalized Maclaurin symmetric mean (GMSM) operator to q-ROFS, which can properly capture the interrelationship among attributes. Further, complex proportional assessment (COPRAS) method is extended to q-ROFS for prioritization of objects by using attributes’ weight vector and aggregated preference matrix. The applicability of the proposed framework is demonstrated by using a renewable energy source prioritization problem from an Indian perspective. Finally, the superiorities and weaknesses of the framework are discussed in comparison with state-of-the-art methods.
01 Jan 2002
TL;DR: An attempt to benchmark selected Multi-Criteria Decision Analysis (MCDA) methods with detailed influence of values of particular parameters on the final form and a similarity of the final rankings obtained by different MCDA methods is undertaken.
Abstract: Multi-Criteria Decision-Analysis (MCDA) methods are successfully applied in different fields and disciplines. However, in many studies, the problem of selecting the proper methods and parameters for the decision problems is raised. The paper undertakes an attempt to benchmark selected Multi-Criteria Decision Analysis (MCDA) methods. To achieve that, a set of feasible MCDA methods was identified. Based on reference literature guidelines, a simulation experiment was planned. The formal foundations of the authors’ approach provide a reference set of MCDA methods ( Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Complex Proportional Assessment (COPRAS), and PROMETHEE II: Preference Ranking Organization Method for Enrichment of Evaluations) along with their similarity coefficients (Spearman correlation coefficients and WS coefficient). This allowed the generation of a set of models differentiated by the number of attributes and decision variants, as well as similarity research for the obtained rankings sets. As the authors aim to build a complex benchmarking model, additional dimensions were taken into account during the simulation experiments. The aspects of the performed analysis and benchmarking methods include various weighing methods (results obtained using entropy and standard deviation methods) and varied techniques of normalization of MCDA model input data. Comparative analyses showed the detailed influence of values of particular parameters on the final form and a similarity of the final rankings obtained by different MCDA methods.
TL;DR: This study introduces a new method, called MEREC (MEthod based on the Removal Effects of Criteria), to determine criteria’ objective weights, and conducts analyses to demonstrate that the MEREC is efficient to determine objective weights of criteria.
Abstract: The weights of criteria in multi-criteria decision-making (MCDM) problems are essential elements that can significantly affect the results. Accordingly, researchers developed and presented several methods to determine criteria weights. Weighting methods could be objective, subjective, and integrated. This study introduces a new method, called MEREC (MEthod based on the Removal Effects of Criteria), to determine criteria’ objective weights. This method uses a novel idea for weighting criteria. After systematically introducing the method, we present some computational analyses to confirm the efficiency of the MEREC. Firstly, an illustrative example demonstrates the procedure of the MEREC for calculation of the weights of criteria. Secondly, a comparative analysis is presented through an example for validation of the introduced method’s results. Additionally, we perform a simulation-based analysis to verify the reliability of MEREC and the stability of its results. The data of the MCDM problems generated for making this analysis follow a prevalent symmetric distribution (normal distribution). We compare the results of the MEREC with some other objective weighting methods in this analysis, and the analysis of means (ANOM) for variances shows the stability of its results. The conducted analyses demonstrate that the MEREC is efficient to determine objective weights of criteria.
01 Nov 2017
TL;DR: A systematic review of methodologies and applications with recent fuzzy developments of two new MCDM utility determining approaches including Step-wise Weight Assessment Ratio Analysis (SWARA) and the Weighted Aggregated Sum Product Assessment (WASPAS) and fuzzy extensions which discussed in recent years are presented.
Abstract: The Multiple Criteria Decision Making (MCDM) utility determining approaches and fuzzy sets are considered to be new development approaches, which have been recently presented, extended, and used by some scholars in area of decision making. There is a lack of research regarding to systematic literature review and classification of study about these approaches. Therefore; in the present study, the attempt is made to present a systematic review of methodologies and applications with recent fuzzy developments of two new MCDM utility determining approaches including Step-wise Weight Assessment Ratio Analysis (SWARA) and the Weighted Aggregated Sum Product Assessment (WASPAS) and fuzzy extensions which discussed in recent years. Regarding this, some major databases including Web of Science, Scopus and Google Scholar have been nominated and systematic and meta-analysis method which called “PRISMA” has been proposed. In addition, the selected articles were classified based on authors, the year of publication, journals and conferences names, the technique and method used, research objectives, research gap and problem, solution and modeling, and finally results and findings. The results of this study can assist decision-makers in handling information such as stakeholders’ preferences, interconnected or contradictory criteria and uncertain environments. In addition, findings of this study help to practitioners and academic for adopting the new MCDM utility techniques such as WASPAS and SWARA in different application areas and presenting insight into literature.
TL;DR: In this article, a reference point theory is used for multi-objective optimization by ratio analysis (MOORA), which takes care of different objectives with the objectives keeping their own units.
Abstract: Multi-Objective Optimization takes care of different objectives with the objectives keeping their own units. The internal mechanical solution of a Ratio System, producing dimensionless numbers, is preferred. The ratio system creates the opportunity to use a second approach: a Reference Point Theory, which uses the ratios of the ratio system. This overall theory is called MOORA (Multi-Objective Optimization by Ratio Analysis). The results are still more convincing if a Full Multiplicative Form is added forming MULTIMOORA. The control by three different approaches forms a guaranty for a solution being as non-subjective as possible. MULTIMOORA, tested after robustness, showed positive results.