A GA-PCA approach for power sector performance ranking based on machine productivity

The objective of this paper is to present a framework for ranking of power sector's performance based on machinery productivity indicators. To rank this sector of industry, the combination of Genetic Algorithm (hereunder GA), Principle Component Analysis (hereunder PCA) and Numerical Taxonomy (hereunder NT) are efficiently used for all branches (sub sectors) of the power sector. In other words, all of useful and influential points of the mentioned methods are utilized to measure the power sector's performance. In this study, validity of the GA is verified by PCA and NT. Furthermore, two non-parametric correlation methods, Spearman correlation experiment and Kendall Tau, are used to determine the correlation among the findings of GA, PCA and NT. As a result, a great degree of correlation is shown. To achieve the objectives of this study, a comprehensive study was conducted to recognize all economic and technical indicators (indices) which have great influences upon machine performance. These indicators are related to machine productivity, efficiency, effectiveness and profitability. Standard factors such as down time, time to repair, mean time between failure, operating time, value added and production value were considered as shaping factors. According to ISIC (International Standard Industrial Classified) codes, all of economic activities in this industry are identified to two, three and four-digit codes. By these codes, all of branches in the power sector are classified from two to four-digit codes hierarchically. This paper presents an integrated approach for ranking of power sector based on machine productivity. Furthermore, it is shown how total machine productivity is obtained through a multivariate approach. The results of such studies would help not only top managers to have better understanding of weak and strong points in their systems' performance but also help experts and researchers to determine the satisfactory levels of each sub sectors' performances in supplying energy among demands. Also, this integrated method could be applied in power deregulation area, a worldwide hot topic, in which optimal allocation of several energy suppliers satisfying various economical, technical and environmental objectives is required. Moreover, the developed approach of this study could be used for continuous assessment and improvement of power sector's performance in supplying energy with respect to overall productivity and reliability aspects (expected energy not supplied).