Dr.K.KALAIARASI KALAICHELVAN
@cauverycollege.ac.in
ASSISTANT PROFESSOR OF MATHEMATICS
Cauvery College for women Autonomous
I was born in Tiruchirappalli, Tamil, Tamil Nadu, India Tamil Nadu, India on February 25, 1981. completed B.Sc., and M.Sc., Mathematics from Holy Cross College Autonomous affiliated to Bharathidasan University, Tiruchirappalli, M.Phil Mathematics in Nadu India on February 25, 1981. I completed B.Sc., and M.Sc., Mathematics from Holy Cross College Autonomous affiliated to Bharathidasan University, Tiruchirappalli, M.Phil Mathematics in College Autonomous affiliated to Bharathidasan University, Thiruchirappalli, and completed Ph.D. in Mathematics from Manonmanium University, Thirunelveli. I am having more than 16 years of experience and presently I am working as a Professor in PG and Research Department of Mathematics, Cauvery college for women, Trichy. My research interests include Fuzzy Inventory Models, Operations Research, Data Science, Machine Learning, I have more than 90 publications in a total of which are 80 journal publications includes Scopus, Web of Scien
EDUCATION
Ph.D. (Mathematics) from Manonmanium Sundaranar University, Thirunelveli, TN. Have awarded in the year 12th, August 2013.
Passed SET(State level Eligibility Test) in the year 2017.
M.Phil., (Mathematics) from St. Joseph College, Bharathidasan University, Trichy, TN with 91% in the year March 2005.
M.Sc., (Mathematics) from Holy Cross College, Bharathidasan University, Trichy, TN with 87% in the year April 2003.
B.Sc., (Mathematics) from Holy Cross College, Bharathidasan University, Trichy, TN with 86% in the year April 2001.
RESEARCH INTERESTS
I was born in Tiruchirappalli, Tamil, Tamil Nadu, India Tamil Nadu, India on February 25, 1981. completed B.Sc., and M.Sc., Mathematics from Holy Cross College Autonomous affiliated to Bharathidasan University, Tiruchirappalli, M.Phil Mathematics in Nadu India on February 25, 1981. I completed B.Sc
FUTURE PROJECTS
FUZZY INVENTORY MODELS WITH MACHINE LEVEL LANGUAGE
Abstract. Trade Credit is an important service in modern business operation. Therefore to incorporate the concept of vendor-buyer integra- tion and ordersize, dependent trade credit, we present a stylized model to determine the optimal strategy for an integrate vendor-buyer inven- tory system under the condition of trade credit. This paper develops an approach to determine the optimum economic order quantity and total annual integrated cost for both vendor and buyer under the fuzzy arith- metical operations of function principle are proposed. A full fuzzy model is developed where the input parameters annual demand, production rate, set up cost, holding cost, purchase cost, transportation cost, order processing cost, carrying cost are fuzzy trapezoidal numbers. The optimal policy for the fuzzy production inventory model is determined using the algorithm of extension of the Lagrangean method for solving inequality constraint problem and graded mean integration method is used for def
Applications Invited
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Kalaiarasi Kalaichelvan, Soundaria Ramalingam, Prasantha Bharathi Dhandapani, Víctor Leiva, and Cecilia Castro
MDPI AG
In this article, we present a novel methodology for inventory management in the pharmaceutical industry, considering the nature of its supply chain. Traditional inventory models often fail to capture the particularities of the pharmaceutical sector, characterized by limited storage space, product degradation, and trade credits. To address these particularities, using fuzzy logic, we propose models that are adaptable to real-world scenarios. The proposed models are designed to reduce total costs for both vendors and clients, a gap not explored in the existing literature. Our methodology employs pentagonal fuzzy number (PFN) arithmetic and Kuhn–Tucker optimization. Additionally, the integration of the naive Bayes (NB) classifier and the use of the Weka artificial intelligence suite increase the effectiveness of our model in complex decision-making environments. A key finding is the high classification accuracy of the model, with the NB classifier correctly categorizing approximately 95.9% of the scenarios, indicating an operational efficiency. This finding is complemented by the model capability to determine the optimal production quantity, considering cost factors related to manufacturing and transportation, which is essential in minimizing overall inventory costs. Our methodology, based on machine learning and fuzzy logic, enhances the inventory management in dynamic sectors like the pharmaceutical industry. While our focus is on a single-product scenario between suppliers and buyers, future research hopes to extend this focus to wider contexts, as epidemic conditions and other applications.
S. Shobana, Mahesh Sahebrao Wavare, K. Kalaiarasi, T. Bhaskar, M. Clement Joe Anand, and N. Sindhuja
Springer Nature Switzerland
K. Iyappan, M. Clement Joe Anand, K. Kalaiarasi, N. Sindhuja, G. Sumathi, and Mohit Tiwari
Springer Nature Switzerland
K. Iyappan, Om M. Teraiya, K. Kalaiarasi, S. Swathi, Parul Sharda, and M. Clement Joe Anand
Springer Nature Switzerland
Sharmila Saminathan, Gowri Sundaram, Aarthi Jayapal, Faiyaz Shakeel, Sivaranjani Rajalingam, Shandhiya Murugan, Kalaiarasi Kalaichelvan, and Md. Faiyazuddin
Walter de Gruyter GmbH
Abstract We attempted to synthesize nickel oxide nanoparticles (NiO-NPs) utilizing waste Arachis hypogaea (peanut) shell extract and studied their structural, morphological, and biological performance for biomedical applications. The green engineered NiO-NPs possessed a face-centered cubic structure with an average particle size of 20 nm in highly crystalline form. NiO-NPs were shown to have an optical resonance peak at 327 nm with 3 eV as the optical band gap according to the UV–visible spectra, and the stretching band between Ni–O were evidenced from the FTIR and Raman spectrum. Utilizing green approach the stable nanoparticles were obtained with average particle size of 31 nm from SEM analysis; zeta potential value of −17.6 mV, and PDI as 0.68, revealed the formation of spherical nanoparticles with distinct morphologies without aggregation. XPS analysis confirmed the oxidation states of the elements Ni (2p) and O (1s). This approach may help to increase the surface area, increasing the possibility of nanoparticles interacting with bacterial cells. Furthermore, the presence of nickel and the oxygen oxidation state were confirmed by XPS. Proteus vulgaris, Streptococcus oralis, Bacillus subtilis, and Escherichia coli were found to be susceptible to the antibacterial action of the produced NiO-NPs, with a maximal zone of inhibition of 10.25 mm at 500 μg/ml for P. vulgaris. For P. vulgaris and E. coli, the minimum inhibitory concentrations of NiO were 5.36 and 12.55 %, respectively, at 31.25 μg mL−1. We hereby claim that green engineered NiO NPs decorated with A. hypogaea shell extract have great potential for pharmaceutical and biomedical applications.
, K. K., , , , , M. Santoshi Kumari, K. Kalaiarasi, Manjula G.. J., and Shrivalli H.. Y.
ASPG Publishing LLC
Embarking on the exploration of integrating environmental sustainability principles and neutrosophic fuzzy theory in inventory management, this study aims to effectively tackle shortages. It underscores the vital balance between economic efficiency and ecological responsibility in contemporary inventory management practices. Neutrosophic fuzzy theory emerges as a robust tool for navigating the inherent uncertainties in inventory optimization, offering a versatile framework for modelling intricate problems. Strategies for optimizing resource consumption and minimizing waste generation within inventory management are scrutinized, emphasizing the imperative of harmonizing economic objectives with environmental concerns. Introducing a novel framework that melds neutrosophic fuzzy with environmental metrics, the research aims to optimize inventory management processes while mitigating environmental impacts. Furthermore, it delves into the challenges of managing energy consumption, advocating for innovative approaches to address fluctuating energy prices, data limitations, and evolving regulatory requirements. Neutrosophic sets are introduced for energy consumption analysis and cost evaluation, showcasing their efficacy in managing uncertainty and variability in real-world scenarios. The study concludes with a Python-based analysis of neutrosophic mean in energy consumption, offering insights into central tendencies and uncertainties associated with energy-related costs. Utilizing visualization techniques to enhance comprehension and decision-making in energy management, this research contributes to advancing inventory management practices by integrating environmental sustainability principles and sophisticated mathematical techniques, thereby fostering more resilient and sustainable supply chain operations.
K. K., , , , , N. Anitha, S. Swathi, and B. Ranjitha
ASPG Publishing LLC
This research introduces the Neutrosophic Vendor-Buyer Economic Order Quantity (EOQ) model, integrating Neutrosophic Set Theory and Particle Swarm Optimization (PSO) for advanced inventory management. Addressing uncertainties in demand and costs, Neutrosophic Sets quantify truth, indeterminacy, and falsity degrees for key parameters. The model, employing PSO inspired by collective behaviour in nature, aims to minimize the combined total cost (C) encompassing vendor and buyer expenses. A grocery store scenario illustrates the approach, demonstrating substantial total cost reduction through the optimization of decision variables. MATLAB R2015a visualizations include a mesh plot depicting cost changes across varying EOQ and demand variability values, emphasizing optimal solutions. A bar chart compares initial and optimized total costs, showcasing efficiency gains. Cost breakdowns and pie charts detail the impact on vendor and buyer expenses. Sensitivity analysis systematically explores variable influences, aiding decision-makers in understanding trade-offs and optimal ranges by using Python. This comprehensive framework contributes empirical insights for practical implementation, enabling businesses to make informed decisions and enhance adaptive inventory strategies efficiently.
K. Kalaiarasi, S. Swathi, and Sardar M. N. Islam
Springer Nature Singapore
Kalaiarasi Kalaichelvan, Trichy Venugopal Shriprakash, and Sivagama Sundari Ashokkumar
AIP Publishing
K. Kalaiarasi, L. Mahalakshmi, Nasreen Kausar, and A. B. M. Saiful Islam
Korean Institute of Intelligent Systems
M. Clement Joe Anand, K. Kalaiarasi, Nivetha Martin, B. Ranjitha, S. Sujitha Priyadharshini, and Mohit Tiwari
IEEE
The sustainability of the electrical industries and persistent production runs are dependent on their suppliers. Logistic supplier selection is an indispensable one for electrical products manufacturing concerns. The identification of feasible logistic suppliers is essential and very significant before employing the ranking methods of determining the optimal suppliers. This paper proposes a hybrid decision-making approach that integrates the Fuzzy c-means clustering (FCM) algorithm and the multi-criteria decision-making method of MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis). The hybrid model is two phases in which the interface of the machine learning algorithm performs the task of classifying the logistic suppliers of electrical products based on their feasibility in the first phase. The MAIRCA method is applied in the second phase of ranking the suppliers of electrical products. The efficacy of the hybrid method is tested by comparing the ranking outcomes of the alternatives of logistic suppliers with and without the interference of fuzzy c-means clustering, it results that the integrated MCDM method with fuzzy c-means clustering seems to be more time and cost-efficient. The results of the proposed hybrid method are more convincing and the efficacy of the method is measured in terms of time and cost efficiency.
M. Clement Joe Anand, C. Balakrishna Moorthy, S. Sivamani, S. Indrakumar, K. Kalaiarasi, and Amir Barhoi
IEEE
In many areas, carbon cap-and-trade and carbon offsets are frequent and significant mechanisms for reducing carbon emissions. Furthermore, particular capital investments in green technologies can efficiently cut carbon emissions from corporate activities. However, such capital investments are expensive, and not all businesses/company can afford them. As a result, if all members of a supply chain agree to share facility investments, the supply chain can cut carbon emissions while also increasing profit. This study utilized crisp and fuzzy models to fix perishable products in the production process. The suggested model, for study is solved using python in machine learning. To cut carbon emissions and increase the total value of the supply chain system, we planned to integrate manufacturing, distribution, replenishment, and technology. Several cases are simulated, and the key parameters are subjected to sensitivity analysis. Under various carbon emission rules, the optimal solutions and joint total profit are also compared. Companies should be able to share risks by co-investing and building sustainable supply chains as a part of the future carbon emission control trend.
K Kalaiarasi, N Sindhuja, and Sardar M. N. Islam
IEEE
A tumor-tumor is a mass of abnormal cells in the body out of control, raising the pressure inside the skull known as pulmonary hypertension. Human brain tumors have recently emerged as one of the leading causes of death for a large number of people. The much more difficult and cutting-edge field is medical image processing, particularly when it comes to using neuroimaging (MRI) to find brain cancers in people. Furthermore, early discovery can treat serious conditions and save lives. Additionally, classified into non imaging method that generates high-quality MR images that are ideal for detecting aberrant growth, such as a brain tumor. This study suggested a model for detecting brain cancers that combines the K-means algorithms and an enhanced perceptron classifier. It demonstrates an effective technique for automatically segmenting brain tumors to remove tumors in mice from MR images. For greater performance, segmentation is done in this procedure utilizing the K-means-based approach. When compared to other clustering protocols, this improves the tumor borders more and is quite quick. The suggested method yields great outcomes.
A. S. Prakaash, K. Sivakumar, B. Surendiran, S. Jagatheswari, and K. Kalaiarasi
Springer Science and Business Media LLC
Kalaichelvan Kalaiarasi, L. Mahalakshmi, Nasreen Kausar, Sajida Kousar, and Parameshwari Kattel
Hindawi Limited
Fuzzy soft graphs are efficient numerical tools for simulating the uncertainty of the real world. A fuzzy soft graph is a perfect fusion of the fuzzy soft set and the graph model that is widely used in a variety of fields. This paper discusses a few unique notions of perfect fuzzy soft tripartite graphs (PFSTG), as well as the concepts of complement of perfect fuzzy soft tripartite graphs (CPFSTGs). Because soft sets are most useful in real-world applications, the newly developed concepts of perfect soft tripartite fuzzy graphs will lead to many theoretical applications by adding extra fuzziness in analysing. We look at some of their properties and come up with a few results that are related to these concepts. Furthermore, we investigated some fundamental theorems and illustrated an application of size of perfect fuzzy soft tripartite graphs in employee selection for an institution using the perfect fuzzy soft tripartite graph.
K. Kalaiarasi, S. Daisy, and M. Sumathi
Elsevier BV
K. Kalaiarasi, MARY HENRIETTA H, M. Sumathi, and A. Stanley Raj
College of Education - Aliraqia University
The technique of limiting expenditure plays a critical part in an organization's ability to govern the smooth operation of its management system. The economic order quantity (EOQ) is calculated by solving a nonlinear problem, and the best solution is investigated in a fuzzy and intuitionistic fuzzy environment. The overall cost is made up of several factors, such as demand, holding, and ordering costs. The demand and stock-out characteristics were both fuzzified using fuzzy and intuitionistic fuzzy numbers. The numerical analysis shows the comparison between the two fuzzy numbers through sensitivity analysis.
M. Nagamani and K. Kalaiarasi
Elsevier BV
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
Publications
Optimization of Fuzzy Integrated Vendor-Buyer Inventory Models, Annals of Fuzzy Mathematics and Informatics, Volume 2, Number 2, October 2011, .
Optimization of Fuzzy Integrated Two Stage Vendor-Buyer Inventory System, International Journal of Mathematical Sciences and Applications, Volume.1, Number 2 (May 2011).
Optimization of EOQ model on the boundaries of the fillrate, International Mathematical Forum, Volume 6, Number 63, 2011, pp 3101-3110.
Optimization of a Multiple Vendor Single Buyer Integrated Inventory Model with a Variable number of Vendors, International Journal of Mathematical Sciences and Engineering Applications (IJMSEA) ISNN 0973-9424, Vol.5, Nov (Sep 2011), .
Optimization of Single Supplier Multiple Cooperative Retailers Inventory Model with Quantity Discount and Permissible Delay in Payments, International Journal of Advance in Mathematical Sciences, ISSN: 0973-5798, Volume 1, Number 61 (Jan-June 2011).
Fuzzy EOQ Model with the Impact of Stochastic Leadtime Reduction on Inventory Cost under Order Crossover, Fuzzy Sets, Rough Sets and Multivalued operations and Applications, Serial Publications, July- Dec 2011.
Optimization of EOQ Inventory Models with Two Backorders, International Journal of Mathematics, IJAM Issues, Vol.2, Number, 2010-2011, IJAM , Issue 01, .
An Entropic EOQ with imperfect quality inventory control dynamic programming, IJAIR,Vol. 2 ,Issue 3 ,ISSN: 2278-7844, 2013, IJAIR.
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
The optimization of inventory EOQ concept was developed to calculate replenishment order size for a single item inventory system without space constraints. The basic inventory EOQ model determines the order quantity considering the trade-off between order cost and inventory cost.
Chapter I deal with the fundamental concepts and a brief historical note on fuzzy inventory model.
Chapter II deals with the annual integrated total cost for both the vendor and the buyer and the total cost of the integrated two stage inventory system for the relationship of vendor-buyer.
Chapter III explains an inventory model with Taguchi’s cost of poor quality and on the boundaries of the fill rate in a fuzzy situation by employing the signed distance method which is triangular.
Chapter IV deals with the impact of stochastic leadtime reduction on inventory cost under order crossover by using Yager’s method.
Chapter V discusses a sequential optimization method by using Kuhn-Tucker conditions with a variable number of vendors and quality discount and permissible delay in payments.
Chapter VI provides a closed form optimal solution to the integrated vendor-buyer inventory systems with backlogging level considering both linear and fixed backorder costs.
Chapter VII contains the fuzzy cooperation in a multi-client distribution
network via fuzzy geometric programming.
Industry, Institute, or Organisation Collaboration
Working as an Assistant Professor in PG and Research Department of Mathematics at Cauvery College of women (Autonomous), Trichy from Feb 2016 to till date
Worked as a Professor in Department of Mathematics at Vel Tech University, Avadi,Chennai from Dec-2014 to Feb 2016.
Worked as an Associate Professor in Department of Mathematics at Cambridge Institute of technology , K.R.Puram, Bangalore from Dec-2013 to Dec 2014
Worked as an Assistant Professor in Department of Mathematics at CMRIT College, K.R.Puram, Bangalore from Dec-2010 to Dec-2013
Worked as a Lecturer in Department of Mathematics at SEA College, K.R.Puram Bangalore from Dec-2008 to June-2009
Worked as a Lecturer in Department of Mathematics at Lowry Memorial College, K.R.Puram Bangalore from Dec-2006 to June-2007.
Worked as a Lecturer in Department of Mathematics at Urumu Dhanalakshmi College, Trichy, TN from Dec-2003 to Mar-2005.
Worked as a Lecturer in Department of Mathematics at Holy Cross College, Trichy, TN from Jun-2003 to Oct-2003.