Dr.K.KALAIARASI KALAICHELVAN

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

Scopus Publications

Exploring novel extensions and applications of KM-algebras: insights into negative-valued functions and fuzzy ideals




The Analysis of Pentagonal Fuzzy Numbers in a Neutrosophic Fuzzy Inventory Management Modelling with Minimal Insufficient Supply Required and Fuzzy Consumption
, admin admin, , , , Nasreen Kausar, Said Broumi, and Tonguc Cagin
American Scientific Publishing Group (ASPG) LLC
The fuzzy stock administration demonstrates displayed in this work employments neutrosophic set hypothesis, pentagonal fuzzy numbers, and the Graded mean Integration Representation (GMIR) strategy for defuzzification. Request rates, arrange amounts, utilization rates, holding costs, setup costs, and deficiency costs are all spoken to as fuzzy parameters within the demonstrate to account for the inborn instability and vacillation. To reduce by and large costs, the whole cost work is calculated, taking setup, holding, and shortage costs into consideration. In arrange to speak to the combined impacts of a few fetched components, the overall taken a toll work is rearranged and the ideal arrange amount is built up beneath fuzzy conditions utilizing pentagonal fuzzy parameters. The demonstrate is assessed beneath different degrees of instability through a case-based investigation, advertising an exhaustive system for making choices on stock administration in equivocal and dubious circumstances. The results appear how versatile and capable the show is for improving fetched advancement and stock control.


OPTIMIZATION OF STOCK MANAGEMENT SYSTEM WITH DEFICIENCIES THROUGH FUZZY RATIONALE WITH SIGNED DISTANCE METHOD IN SEABORN PROGRAMING TOOL




Optimizing the Economic Order Quantity Using Fuzzy Theory and Machine Learning Applied to a Pharmaceutical Framework
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.


DEVELOPMENT AND EVALUATION OF A CENTROID-BASED EOQ MODEL FOR ITEMS SUBJECT TO DEGRADATION AND SHORTAGES




Incorporating Machine Learning Methods for Predictive Maintenance and Fuzzy Inventory Optimization
S. Shobana, Mahesh Sahebrao Wavare, K. Kalaiarasi, T. Bhaskar, M. Clement Joe Anand, and N. Sindhuja
Springer Nature Switzerland



Fuzzy Inventory Implementation of Minimum Value Unused Storing Profitable by Python Execution
K. Iyappan, M. Clement Joe Anand, K. Kalaiarasi, N. Sindhuja, G. Sumathi, and Mohit Tiwari
Springer Nature Switzerland



Fuzzy Inventory Grinding Production Process Optimization for Rotor Manufacturing Using Python and MATLAB
K. Iyappan, Om M. Teraiya, K. Kalaiarasi, S. Swathi, Parul Sharda, and M. Clement Joe Anand
Springer Nature Switzerland



Green engineering of NiO nanoparticles decorated with Arachis hypogaea shell extract for biomedical applications
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.


Energizing Inventory Management to Optimize Energy Consumption of Handling Shortages by Neutrosophic Fuzzy Trapezoidal Number
, 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.


Optimization of Neutrosophic Vendor-Buyer Economic Order Quantity Model Using Particle Swarm Optimization
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.


Fuzzy Vendor–Buyer Trade Credit Inventory Model-Pentagonal Numbers in Permissible Limits Delay in Account Settlement with Supervised Learning
K. Kalaiarasi, S. Swathi, and Sardar M. N. Islam
Springer Nature Singapore



Image Caption Generation Using Recurrent Convolutional Neural Network




Sustainable Medical Supply Chain Network during Pandemic by Using Fuzzy Inventory
Kalaiarasi Kalaichelvan, Trichy Venugopal Shriprakash, and Sivagama Sundari Ashokkumar
AIP Publishing



Conormal Product for Intutionistic Anti-Fuzzy Graphs
K. Kalaiarasi, L. Mahalakshmi, Nasreen Kausar, and A. B. M. Saiful Islam
Korean Institute of Intelligent Systems



An investigation of inventory optimization by using geometric programming technique with decagonal fuzzy number representation




Fuzzy C-Means Clustering with MAIRCA -MCDM Method in Classifying Feasible Logistic Suppliers of Electrical Products
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.


The optimality representation of a finite-life inventory model with exiguous defectives with Hessian matrix approach




The shortest path problem on chola period built temples with Dijkstra's algorithm in intuitionistic triangular neutrosophic fuzzy graph




Fuzzy Intelligence Inventory Decision Optimization Model of Sustainability and Green Technologies for Mixed Uncertainties of Carbon Emission
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.


Diagnosing the Abnormalities in Brain Tumors with the Technique of K-Means Clustering with Knowledge Acquisition
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.


Design and Development of Modified Ensemble Learning with Weighted RBM Features for Enhanced Multi-disease Prediction Model
A. S. Prakaash, K. Sivakumar, B. Surendiran, S. Jagatheswari, and K. Kalaiarasi
Springer Science and Business Media LLC



The Characterization of Fuzzy and Anti Fuzzy Ideals in AG-groupoid




n-DOMINATION IN VERTEX SQUARED DOUBLE DIVIDE INTERVAL-VALUED FUZZY GRAPHS




Perfect Fuzzy Soft Tripartite Graphs and Their Complements
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.

RECENT SCHOLAR PUBLICATIONS

Incorporating Machine Learning Methods for Predictive Maintenance and Fuzzy Inventory Optimization
S Shobana, MS Wavare, K Kalaiarasi, T Bhaskar, MCJ Anand, N Sindhuja
International Conference on Intelligent and Fuzzy Systems, 666-678 2024



Fuzzy Inventory Implementation of Minimum Value Unused Storing Profitable by Python Execution
K Iyappan, MCJ Anand, K Kalaiarasi, N Sindhuja, G Sumathi, M Tiwari
International Conference on Intelligent and Fuzzy Systems, 429-442 2024



Fuzzy Inventory Grinding Production Process Optimization for Rotor Manufacturing Using Python and MATLAB
K Iyappan, OM Teraiya, K Kalaiarasi, S Swathi, P Sharda, MCJ Anand
International Conference on Intelligent and Fuzzy Systems, 443-456 2024



Energizing Inventory Management to Optimize Energy Consumption of Handling Shortages by Neutrosophic Fuzzy Trapezoidal Number.
N Sindhuja, MS Kumari, K Kalaiarasi, M GJ, S HY
International Journal of Neutrosophic Science (IJNS) 24 (1) 2024



Optimization of Neutrosophic Vendor-Buyer Economic Order Quantity Model Using Particle Swarm Optimization.
K Kalaiarasi, N Anitha, S Swathi, B Ranjitha
International Journal of Neutrosophic Science (IJNS) 23 (4) 2024



DEVELOPMENT AND EVALUATION OF A CENTROID-BASED EOQ MODEL FOR ITEMS SUBJECT TO DEGRADATION AND SHORTAGES
K KALAIARASI, S SWATHI
Journal of applied mathematics & informatics 42 (5), 1063-1076 2024



Fuzzy Inventory Implementation of Minimum Value Unused Storing Profitable by Python
K Iyappan, MCJ Anand, K Kalaiarasi, N Sindhuja
Intelligent and Fuzzy Systems: Intelligent Industrial Informatics and 2024



Optimization of stock management system with deficiencies through fuzzy rationale with signed distance method in Seaborn programming tool
K Kalaiarasi, N Sindhuja
Journal of Applied Mathematics & Informatics 42 (2), 379-390 2024



Arithmetic Operations of Trigonal Fuzzy Numbers Using Alpha Cuts: A Flexible Approach to Handling Uncertainty
K Kalaiarasi, S Swathi
Indian Journal of Science and Technology 16 (47), 4585-4593 2023



An investigation of inventory optimization by using geometric programming technique with decagonal fuzzy number representation.
HM Henrietta, K Kalaiarasi, M Sumathi, VN Jayamani, AS Raj
Nonlinear Studies 30 (4) 2023



Fuzzy C-Means Clustering with MAIRCA-MCDM Method in Classifying Feasible Logistic Suppliers of Electrical Products
MCJ Anand, K Kalaiarasi, N Martin, B Ranjitha, SS Priyadharshini, ...
2023 First International Conference on Cyber Physical Systems, Power 2023



Fuzzy Vendor–Buyer Trade Credit Inventory Model-Pentagonal Numbers in Permissible Limits Delay in Account Settlement with Supervised Learning
K Kalaiarasi, S Swathi, SMN Islam
International Conference on Emergent Converging Technologies and Biomedical 2023



Diagnosing the Abnormalities in Brain Tumors with the Technique of K-Means Clustering with Knowledge Acquisition
K Kalaiarasi, N Sindhuja, SMN Islam
2023 International Conference on Advancement in Computation & Computer 2023



The optimality representation of a finite-life inventory model with exiguous defectives with Hessian matrix approach
K Kalaiarasi, H Mary Henrietta, M Sumathi, A Stanley Raj
Nonlinear Studies 30 (2), 393-398 2023



Conormal Product for Intutionistic Anti-Fuzzy Graphs
K Kalaiarasi, L Mahalakshmi, N Kausar, ABMS Islam
International Journal of Fuzzy Logic and Intelligent Systems 23 (1), 79-90 2023



Negative Valued Ideals of Fuzzy KM-Subalgebras
K Kalaiarasi, V Manimozhi
Indian Journal of Science and Technology 16 (12), 872-883 2023



Fuzzy intelligence inventory decision optimization model of sustainability and green technologies for mixed uncertainties of carbon emission
MCJ Anand, CB Moorthy, S Sivamani, S Indrakumar, K Kalaiarasi, ...
2023 International Conference on Information Management (ICIM), 78-82 2023



An integrated approach of ant colony optimization (aco) machine learning (ml) and fuzzy logic for revolutionizing inventory management in modern supply chains
K Swathi, K Kalaiarasi
Tuijin Jishu/Journal of Propulsion Technology 44 (3), 2023 2023



Neutrosophic Inventory Model With Quick Return For Damaged Materials And Python-Analysis
K Kalaiarasi, S Swathi
2023 Neutrosophic SuperHyperAlgebra And New Types of Topologies 219 2023



The Optimization Machine Learning Approach Of Sensitive Analysis Of Fuzzy Inventory Germination Of Plants Generates One-Year-Old-Seeds And Two-Year-Old-Seeds With Fuzzy Inventory
S Shunmugapriya, V Sundhari, K Kalaiarasi
Journal of Survey in Fisheries Sciences 10 (2), 344-355 2023

MOST CITED SCHOLAR PUBLICATIONS

Optimization of fuzzy assignment model with triangular fuzzy numbers using Robust Ranking technique
K Kalaiarasi, S Sindhu, M Arunadevi
International Journal of Innovative Science, Engg. Technology 1 (3), 10-15 2014
Citations: 25



Stochastic lead time reduction for replenishment python-based fuzzy inventory order eoq model with machine learning support
K Kalaiarasi, R Gopinath
International Journal of Advanced Research in Engineering and Technology 11 2020
Citations: 20



Fuzzy inventory eoq optimization mathematical model
K Kalaiarasi, R Gopinath
International Journal of Electrical Engineering and Technology 11 (8), 169-174 2020
Citations: 19



Design and development of modified ensemble learning with weighted rbm features for enhanced multi-disease prediction model
AS Prakaash, K Sivakumar, B Surendiran, S Jagatheswari, K Kalaiarasi
New Generation Computing 40 (4), 1241-1279 2022
Citations: 16



Optimization of fuzzy integrated vendor-buyer inventory models
W Ritha, R Kalaiarasi, YB Jun
Annals of Fuzzy Mathematics and Informatics 2 (2), 239-257 2011
Citations: 15



Determining the efficiency of fuzzy logic EOQ inventory model with varying demand in comparison with Lagrangian and Kuhn-Tucker method through sensitivity analysis
K Kalaiarasi, M Sumathi, HM Henrietta, AS Raj
Journal of Model Based Research 1 (3), 1-12 2020
Citations: 12



Optimization of the average monthly cost of an EOQ inventory model for deteriorating items in machine learning using PYTHON
K Kalaiarasi, R Soundaria, N Kausar, P Agarwal, H Aydi, H Alsamir
Thermal Science 25 (Spec. issue 2), 347-358 2021
Citations: 11



An introduction to fuzzy strong graphs,fuzzy soft graphs,complement of fuzzy strong and soft graphs
LM K .Kalaiarasi
Global journal of pure and applied mathemtics 13 (6), 2235-2254 2018
Citations: 11



Solutions of fuzzy transportation problem using best candidates method and different ranking techniques
A Christi
International Journal of Mathematical and Computational Sciences 11 (4), 182-187 2017
Citations: 10



The economic order quantity in a fuzzy environment for a periodic inventory model with variable demand
K Kalaiarasi, M Sumathi, AS Raj
Iraqi Journal For Computer Science and Mathematics 3 (1), 102-107 2022
Citations: 9



WITHDRAWN: Optimization of unconstrained multi-item (EPQ) model using fuzzy geometric programming with varying fuzzification and defuzzification methods by applying python
K Kalaiarasi, MS Begum, M Sumathi
Materials today: proceedings 2021
Citations: 9



Optimization of fuzzy integrated two-stage vendor-buyer inventory system
R Kalaiarasi, W Ritha
International Journal of Mathematical Sciences and Applications 1 (2), 650-670 2011
Citations: 9



Fuzzy intelligence inventory decision optimization model of sustainability and green technologies for mixed uncertainties of carbon emission
MCJ Anand, CB Moorthy, S Sivamani, S Indrakumar, K Kalaiarasi, ...
2023 International Conference on Information Management (ICIM), 78-82 2023
Citations: 8



Optimization of fuzzy inventory model for EOQ using Lagrangian method
K Kalaiarasi, M Sumathi, HM Henrietta
Malaya Journal of Matematik 7 (03), 497-501 2019
Citations: 8



The shortest path on minimal spanning tree with triangular single-valued neutrosophic intuitionistic fuzzy graph
K Kalaiarasi, R Divya
Parishodh journal 9, 779-791 2020
Citations: 7



Optimization of fuzzy economic production quantity inventory control
K Kalaiarasi, M Sumathi, MS Begum
Journal of Advanced Research in Dynamical and Control System 11, 241-248 2019
Citations: 7



Optimizing EOQ using geometric programming with varying fuzzy numbers by applying Python
K Kalaiarasi, M Sumathi, H Mary Henrietta
Journal of Critical Reviews 7 (18), 596-603 2020
Citations: 6



optimization of fuzzy EOQ model with unit time dependent constant demand and shortages
D K.Kalaiarasi,Sumathi
International journal of Mechanical Engineering and Technology 9 (11), 1520-1527 2018
Citations: 6



Optimization of grafting parameters of nylon6-graft-acrylic acid copolymer using ceric ammonium nitrate as an initiator
B Sheela, K Kalaiarasi, K Vijayalakshmi, PN Sudha
International Journal of Applied and Advanced Scientific Research 1 (2 2016
Citations: 6



Optimization of economic order quantity model on the boundaries of the fill rate
R Kalaiarasi, W Ritha
International Mathematical Forum 6 (63), 3101-3110 2011
Citations: 6

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.