Performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm

Abstract

Purpose

The purpose of this paper is to deal with the performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm. It provides the optimum unit availability level for different combinations of failure and repair rates of the subsystems of the stock preparation unit of the paper plant concerned.

Design/methodology/approach

In this paper, efforts have been made to develop performance models based on real situations for the stock preparation unit. The performance in terms of availability has been evaluated on the basis of Markov birth‐death process. After that, the performance optimization using genetic algorithm is done, which gives the optimum unit availability levels for different combinations of failure and repair rates of the subsystems of stock preparation units for enhancing the overall performance of the paper plant.

Findings

The effect of genetic algorithm parameters, namely number of generations, population size and crossover probability on the unit performance i.e. availability has been analyzed and discussed with the concerned paper plant management. It is found that these results are highly beneficial to the maintenance engineers for the purpose of effective maintenance planning to enhance the overall performance (availability) of the stock preparation unit of the paper plant.

Originality/value

Most of the researchers have confined their work to the development and analysis of theoretical models which has little practical significance. To fulfill this deficiency, efforts have been made in the present work to develop a model based on real situations for the stock preparation unit.

Citation

Khanduja, R., Tewari, P.C. and Chauhan, R.S. (2011), "Performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm", International Journal of Quality & Reliability Management, Vol. 28 No. 6, pp. 688-703. https://doi.org/10.1108/02656711111141238