R. Uthayakumar

Bio: R. Uthayakumar is an academic researcher from Gandhigram Rural Institute. The author has contributed to research in topics: Total cost & Economic order quantity. The author has an hindex of 19, co-authored 75 publications receiving 1293 citations.

Pharmaceutical supply chain and inventory management strategies: Optimization for a pharmaceutical company and a hospital

Abstract: A high level of service for medical supplies and effective inventory policies are essential objectives for all health care industries. Medicine shortages and improper use of pharmaceuticals can not only lead to financial losses but also have a significant impact on patients. Many health systems and hospitals experience difficulties in achieving these goals as they have not addressed how medicines are managed, supplied, and used to save lives and improve health. Studies are essential to understand operations in health care industries and to offer decision support tools that improve health policy, public health, patient safety, and strategic decision-making in the pharmaceutical supply chain. We present an inventory model that integrates continuous review with production and distribution for a supply chain involving a pharmaceutical company and a hospital supply chain. The model considers multiple pharmaceutical products, variable lead time, permissible payment delays, constraints on space availability, and the customer service level (CSL). We develop a procedure for determining optimal solutions for inventory lot size, lead time, and the number of deliveries to achieve hospital CSL targets with a minimum total cost for the supply chain. A numerical example illustrates the model application and behavior.

Optimal inventory management strategies for pharmaceutical company and hospital supply chain in a fuzzy–stochastic environment

Abstract: Pharmaceutical plays a crucial role in the healthcare industries due to the significant costs of the products and their storage and control requirements. It can be expensive to purchase and distribute. An effective management of pharmaceutical is required to ensure the 100% product availability at the right time, at the right cost, in good condition to right customers. Uthayakumar and Priyan (2013) proposed an integrated inventory model for pharmaceutical products in a two-echelon supply chain consisting of a pharmaceutical company and a hospital. They offered strategic decision-making to achieve the target customer service level of the hospital at minimum supply chain cost. In this paper we extend their model to reflect the following three facts: (i) fuzzify the hospital’s expiry rate ( d b i ) and holding cost ( h b i ) , and the pharmaceutical company’s production rate ( P i ) , screening rate ( r s i ) , holding cost ( h w i ) and selling price ( s d i ) for the i th product as the triangular fuzzy numbers in the total cost, (ii) hospital’s quantity received does not necessarily match with the ordered quantity due to various reasons, i.e., the received quantity is uncertain, but it is a random variable following a normal distribution, and (iii) the lead time L consists of m mutually independent components. We then used the signed distance method to defuzzify the fuzzy total cost of the system and Uthayakumar and Priyan’s (2013) Lagrangian multiplier approach to determine the optimal solution of the proposed model. Numerical example is given to highlight the differences between crisp and the fuzzy cases.

Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions

Abstract: We propose new features for classification of epileptic seizure EEG signals.Features were extracted from PSR of IMFs of EEG signals.We define ellipse area of 2D PSR and IQR of Euclidian distance of 3D PSR as features.LS-SVM classifier has been used for classification with the proposed features.Results were compared with other existing methods studied on the same EEG dataset. Epileptic seizure is the most common disorder of human brain, which is generally detected from electroencephalogram (EEG) signals. In this paper, we have proposed the new features based on the phase space representation (PSR) for classification of epileptic seizure and seizure-free EEG signals. The EEG signals are firstly decomposed using empirical mode decomposition (EMD) and phase space has been reconstructed for obtained intrinsic mode functions (IMFs). For the purpose of classification of epileptic seizure and seizure-free EEG signals, two-dimensional (2D) and three-dimensional (3D) PSRs have been used. New features based on the 2D and 3D PSRs of IMFs have been proposed for classification of epileptic seizure and seizure-free EEG signals. Two measures have been defined namely, 95% confidence ellipse area for 2D PSR and interquartile range (IQR) of the Euclidian distances for 3D PSR of IMFs of EEG signals. These measured parameters show significant difference between epileptic seizure and seizure-free EEG signals. The combination of these measured parameters for different IMFs has been utilized to form the feature set for classification of epileptic seizure EEG signals. Least squares support vector machine (LS-SVM) has been employed for classification of epileptic seizure and seizure-free EEG signals, and its classification performance has been evaluated using different kernels namely, radial basis function (RBF), Mexican hat wavelet and Morlet wavelet kernels. Simulation results with various performance parameters of classifier, have been included to show the effectiveness of the proposed method for classification of epileptic seizure and seizure-free EEG signals.

The joint economic lot size problem: a review

Abstract: This article reviews lot-size models which focus on coordinated inventory replenishment decisions between buyer and vendor and their impact on the performance of the supply chain. These so-called joint economic lot size (JELS) models determine order, production and shipment quantities from the perspective of the supply chain with the objective of minimizing total system costs. This paper first describes the problem studied, introduces the methodology of the review and presents a descriptive analysis of the selected papers. Subsequently, papers are categorized and analyzed with respect to their contribution to the coordination of different echelons in the supply chain. Finally, the review highlights gaps in the existing literature and suggests interesting areas for future research.

DWT Based Detection of Epileptic Seizure From EEG Signals Using Naive Bayes and k-NN Classifiers

Abstract: Electroencephalogram (EEG) comprises valuable details related to the different physiological state of the brain. In this paper, a framework is offered for detecting the epileptic seizures from EEG data recorded from normal subjects and epileptic patients. This framework is based on a discrete wavelet transform (DWT) analysis of EEG signals using linear and nonlinear classifiers. The performance of the 14 different combinations of two-class epilepsy detection is studied using naive Bayes (NB) and k-nearest neighbor (k-NN) classifiers for the derived statistical features from DWT. It has been found that the NB classifier performs better and shows an accuracy of 100% for the individual and combined statistical features derived from the DWT values of normal eyes open and epileptic EEG data provided by the University of Bonn, Germany. It has been found that the computation time of NB classifier is lesser than k-NN to provide better accuracy. So, the detection of an epileptic seizure based on DWT statistical features using NB classifiers is more suitable in real time for a reliable, automatic epileptic seizure detection system to enhance the patient’s care and the quality of life.