Table of Contents
Journal of Industrial Mathematics
Volume 2014, Article ID 593176, 9 pages
http://dx.doi.org/10.1155/2014/593176
Research Article

Application of Markov Process in Performance Analysis of Feeding System of Sugar Industry

Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee-247667, Uttarakhand, India

Received 24 January 2014; Accepted 30 March 2014; Published 27 April 2014

Academic Editor: Qiguang Miao

Copyright © 2014 S. P. Sharma and Yashi Vishwakarma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

To analyse the performance measures of complex repairable systems having more than two states, that is, working, reduced and failed, it is essential to model suitably their states so that the system governs a stochastic process. In this paper, the application of time-homogeneous Markov process is used to express reliability and availability of feeding system of sugar industry involving reduced states and it is found to be a powerful method that is totally based on modelling and numerical analysis. The selection of appropriate units/components in designing a system with different characteristics is necessary for the system analyst to maintain the failure-free operation. Keeping this concept in this study, the steady state availability of concern system is analysed and optimized by using a popular search technique, genetic algorithm. The objective of this paper is to consider the system operative process as Markov process and find its reliability function and steady state availability in a very effective manner and also to obtain an optimal system designing constituents which will allow a failure-free operation for long time period as required for maximum system productivity. The system performance measures and optimized design parameters are described and obtained here by considering an illustrative example.