Qualification: 
Ph.D

Dr. R. Krishankumar currently serves as Assistant Professor in the Department of Computer Science and Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore. Prior to joining Amrita, he served as a Research Fellow in the junior and senior grade by acquiring funds from University Grants Commission India. He completed his UG and Ph.D. in 2014 and 2020, respectively. His research interest includes multi-criteria decision-making and soft computing. He has published papers in SCIE indexed journals with Q1/Q2 ranks.

He mainly focuses on providing solutions to decision problems involving multiple stakeholders. He has developed novel frameworks for decision-making under uncertainty by collaborating with professionals within and outside India.

Publications

Publication Type: Journal Article

Year of Publication Title

2021

Xindong Peng, R. Krishankumar, and K. S. Ravichandran, “A novel interval-valued fuzzy soft decision-making method based on CoCoSo and CRITIC for intelligent healthcare management evaluation”, Soft Computing, vol. 25, no. 6, pp. 4213 - 4241, 2021.[Abstract]


The intelligent healthcare management is of great concern to mobilize the enthusiasm of individuals and groups, and effectively use limited resources to achieve maximum health improvement by AI technology. When considering the intelligent healthcare management evaluation, the primary issues involve many uncertainties. Interval-valued fuzzy soft set, depicted by membership degree with interval form, is a more resultful means for capturing uncertainty. In this paper, the comparison issue in interval-valued fuzzy soft environment is disposed of by proposing novel score function. Later, some new properties for interval-valued fuzzy soft matrix are investigated in detail. Moreover, the objective weight is calculated by CRITIC (Criteria Importance Through Inter-criteria Correlation) method. Meanwhile, the combined weight is determined by reflecting both subjective weight and the objective weight. Then, interval-valued fuzzy soft decision-making algorithm-based CoCoSo (Combined Compromise Solution) is developed. Lastly, the validity of algorithm is expounded by the healthcare management industry evaluation issue, along with their sensitivity analysis. The main characteristics of the presented algorithm are: (1) without counterintuitive phenomena; (2) no division by zero problem; (3) have strong ability to distinguish alternatives.

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2020

R. Krishankumar, K. S. Ravichandran, Manish Aggarwal, and Sanjay K. Tyagi, “Extended hesitant fuzzy linguistic term set with fuzzy confidence for solving group decision-making problems”, vol. 32, no. 7, pp. 2879 - 2896, 2020.[Abstract]


This paper presents a new extension of the hesitant fuzzy linguistic term set (HFLTS) called intuitionistic fuzzy confidence-based HFLTS that associates an intuitionistic fuzzy value (IFV) with each linguistic term. The resulting term set is termed as intuitionistic fuzzy confidence hesitant fuzzy linguistic term set (IFCHFLTS). The previous studies on the linguistic decision making have emphasized little upon the preference and non-preference for each of the linguistic terms. This information, however, is crucial in multi-criteria decision making under uncertainty. In this regard, we find IFV particularly useful for qualifying each of the linguistic terms with the agent’s degree of preference, non-preference, and hesitation values. Besides, a new aggregation operator named intuitionistic fuzzy confidence linguistic simple weighted geometry (IFCLSWG) is also proposed to fuse decision makers’ linguistic preferences. Further, the criteria weights are estimated using a new method called intuitionistic fuzzy confidence linguistic standard variance. An approach is also suggested for ranking the given alternatives by adapting VIKOR under the proposed IFCHFLTS context. Finally, the practicality and usefulness of the proposal are demonstrated through two real-world problems in green supplier selection for manufacturing industry, and medical diagnosis. The strengths and weaknesses of the proposal are also highlighted by drawing upon a comparison with similar methods.

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2020

Sivagami Ramadass, R. Krishankumar, Kattur Soundarapa Ravichandran, Huchang Liao, Samarjit Kar, and Enrique Herrera-Viedma, “Evaluation of cloud vendors from probabilistic linguistic information with unknown/partial weight values”, Applied Soft Computing, vol. 97, p. 106801, 2020.[Abstract]


As IT industries grow at a faster pace, cloud technology becomes inevitable. Attracted by the scope, many cloud vendors (CVs) arise. A rational/systematic selection is an urge to tackle the scalability of CVs. To circumvent the issue, in this paper, a framework is proposed for CV selection under with probabilistic linguistic term sets (PLTSs). The PLTS is a flexible structure that allows partial ignorance of occurring probabilities. Initially, attributes’ weights are calculated using a programming model, which uses partial information effectively. Later, decision-makers’ (DMs’) weights are computed by integrating evidence theory with Bayes approximation. Preferences from DMs are aggregated by proposing a two-way operator, which aggregates linguistic preferences using the rule-based method and occurring probabilities using Maclaurin symmetric mean. Moreover, CVs are ranked by using an integrated PROMETHEE–Borda method under the PLTS. Further, to test the validity of the framework, a case study on CV selection is presented for a small-scale company. Finally, the advantages and limitations of the proposed framework are investigated by comparison with other methods and the results infer that (i) the proposed framework is 63.67% robust even after adequate changes are made to the alternatives; (ii) the proposed framework is 87.67% robust even after adequate changes are made to the attributes; (iii) from partial adequacy test, the robustness is determined as 77.67% and 92.33%; and (iv) from the broadness test, the proposed framework produces an average deviation of 9% among their rank values, which is better than the extant models that produce an average deviation close to 7.8%.

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2020

R. Krishankumar, Y. Gowtham, Ifjaz Ahmed, K. S. Ravichandran, and Samarjit Kar, “Solving green supplier selection problem using q-rung orthopair fuzzy-based decision framework with unknown weight information”, Applied Soft Computing, vol. 94, p. 106431, 2020.[Abstract]


<p>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.</p>

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2019

Xindong Peng, R. Krishankumar, and Kattur Soundarapa Ravichandran, “Generalized orthopair fuzzy weighted distance-based approximation (WDBA) algorithm in emergency decision-making”, International Journal of Intelligent Systems, vol. 34, pp. 2364-2402, 2019.[Abstract]


With the intensification of global warming trends, the frequent occurrence of natural disasters has brought severe challenges to the sustainable development of society. Emergency decision-making (EDM) in natural disasters is playing an increasingly important role in improving disaster response capacity. In the case of EDM evaluation, the essential problem arises serious incompleteness, impreciseness, subjectivity, and incertitude. The q-rung orthopair fuzzy set (q-ROFS), disposing the indeterminacy portrayed by membership and nonmembership with the sum of qth power of them, is a more viable and effective means to seize indeterminacy. The aim of paper is to present a new score function of q-rung orthopair fuzzy number (q-ROFN) for solving the failure problems when comparing two q-ROFNs. Firstly, we introduce some basic set operations for q-ROFS. The properties of these operations are also discussed in detail. Later, we propose a q-rung orthopair fuzzy decision-making method based on weighted distance-based approximation (WDBA), in which the weights of decision-makers are obtained from a nonliner optimization model according to the deviation-based method. Finally, some examples are investigated to illustrate the feasibility and validity of the proposed approach. The salient features of the proposed method, compared to the existing q-rung orthopair fuzzy decision-making methods, are as follows: (a) it can obtain the optimal alternative without counterintuitive phenomena and (b) it has a great power in distinguishing the optimal alternative.

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2018

R. Krishankumar, K. S. Ravichandran, K. K. Murthy, and A. B. Saeid, “A scientific decision-making framework for supplier outsourcing using hesitant fuzzy information”, Soft Computing, vol. 22, no. 22, pp. 7445 - 7461, 2018.[Abstract]


Supply chain management (SCM) is an attractive area for research which has seen tremendous growth in the past decades. From the literature we observe that, supplier outsourcing (SO) is a highly explored research field in SCM which lacks significant scientific contribution. The major concern in SO is the decision makers’ (DMs) viewpoint which are often vague and imprecise. To better handle such imprecision, in this paper, we propose a new two-stage decision-making framework called TSDMF, which uses hesitant fuzzy information as input. In the first stage, the DMs’ preferences are aggregated using a newly proposed simple hesitant fuzzy-weighted geometry operator, which uses hesitant fuzzy weights for better understanding the importance of each DM. Following this, in the second stage, criteria weights are estimated using newly proposed hesitant fuzzy statistical variance method and finally, a new ranking method called three-way hesitant fuzzy VIKOR (TWHFV) is proposed by extending the VIKOR ranking method to hesitant fuzzy environment. This ranking method uses three categories viz., cost, benefit and neutral along with Euclid distance for its formulation. The practicality of the proposed TSDMF is verified by demonstrating a supplier outsourcing example in an automobile factory. The robustness of TWHFV is realized by using sensitivity analysis and other strengths of TSDMF are discussed by comparison with another framework.

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Publication Type: Conference Proceedings

Year of Publication Title

2017

K. S. Ravichandran and R. Krishankumar, “New aggregation operator under IFS for balancing liberalization of decision maker weight constraint”, 2017 International Conference on Circuit ,Power and Computing Technologies (ICCPCT). 2017.[Abstract]


This paper focuses on an interesting and unresolved problem in the aggregation of decision makers' preferences. Scholars have rarely addressed this issue in group decision making process. The aggregation operators that use DMs' relative importance (weights) as one of their parameters for evaluation have strictly set a constraint that, the sum of relative importance must be equal to unity. This constraint does not fit well with decision making process as the weight values for the DMs are being forcibly estimated without offering actual freedom in such estimation. To relax this strict constraint from the aggregation process, in this paper, we make efforts to propose a new aggregation operator which considers no such constraint on DMs' weight values. This operator is an extension to simple intuitionistic fuzzy weighted geometry (SIFWG) operator. The reason for choosing SIFWG for extension is evident from the strength and simplicity of SIFWG operator. Finally, an illustrative example is demonstrated to realize the strength of the proposed aggregation operator.

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2017

R. Krishankumar, S R Arvinda, A Amrutha, J Premaladha, and K. S. Ravichandran, “A decision making framework under intuitionistic fuzzy environment for solving cloud vendor selection problem”, 2017 International Conference on Networks Advances in Computational Technologies (NetACT). 2017.[Abstract]


This paper proposes a new scientific ranking framework for optimal selection of cloud vendor(CV) for the organization. For this purpose, a set of target dimensions for cloud computing from customer point of view are defined, based on expert reviews, international literature reviews and cloud provider market analysis. This study proposes intuitionistic fuzzy group decision making (IF-GDM) approach with intuitionistic fuzzy analytic hierarchy process(IF-AHP) for effective cloud vendor selection. In the previous approaches, IF-AHP was mainly used to determine the criteria weights and ranking was performed using other intuitionistic fuzzy based ranking schemes. All these schemes ignored to verify the consistency of the decision matrices which led to unrealistic preference orders. To alleviate such issue, in this paper, a new ranking framework with IF-AHP is proposed to provide both pair-wise comparison as well as consistency check for decision matrices. A case study of cloud vendor selection is demonstrated to verify the strength of the proposed framework.

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2017

R. Krishankumar, R Ramprakash, J Premaladha, and K S Ravichandran, “Solving head nurse selection problem using hybrid VIKOR method under triangular fuzzy environment”, 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). 2017.[Abstract]


The Multi Criteria Decision Making (MCDM) problem is an interesting and dynamic area for research. Many researchers have contributed different methods for effective ranking of the alternatives. All these methods work with the background of crisp or fuzzy data. Recent research has widely used fuzzy data for representation and the popular among them is the Triangular Fuzzy Number (TFN). In this paper, we make efforts to solve the problem of selection of head nurse for health care using hybridized VIKOR method under TFN environment. The weights of the criteria are determined using Analytical Hierarchal Process (AHP) method. We now integrate AHP with VIKOR to form a hybrid framework for selecting a suitable head nurse for the health care. The strength of the framework is verified by comparing the proposed framework with other methods. Correlation inference shows that the proposed framework is consistent with the other state of the art schemes. We also infer that the proposed hybrid framework is a better choice for MCDM problems involving uncertainties and ambiguities.

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Seminars/Workshops

  1. Resource person of a workshop “Data Analytics & Machine Learning” conducted by SASTRA Deemed University.
  2. Attended five days workshop on Machine learning & Soft Computing in SASTRA Deemed University as a part of UKIERI (Indo-UK) project.