Ali Azadeh - Department of Industrial Engineering and Institute of Energy Management and Planning Faculty of Engineering, University of Tehran, Iran
Vahid Ebrahimipour - System Analysis Laboratory, Department of Systems Engineering, Okayama University
Kazuhiko Suzuki - System Analysis Laboratory, Department of Systems Engineering, Okayama University

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 a non-deterministic method, Genetic Algorithm (hereunder GA), and two deterministic methods, 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 nonparametric 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 2, 3 and 4-digit codes. By these codes, all of branches in the power sector are classified from 2 to 4–digit codes hierarchically. In this study, the data-base used to measure the 10 indicators are formed based on ISIC codes and collected from power sector in a developing country. Then through GA, the best array of branches (DMUS, Decision Making Units,) among the generations produced is selected. This array is the rank of power sub sectors which optimizes the fitness function in GA. Moreover, by PCA the major impacts of each 10 indicators on the performance are identified. Finally, the result is analyzed to promote the total system performance. 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).

Genetic Algorithms, Principle Component Analysis, Numerical Taxonomy, Machine Indicators, Productivity