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Deterministic and Non-deterministic Methods for Power Sector Performance Ranking Based on Machine Productivity

机译：基于机器生产率的电力扇区性能排名确定性和非确定性方法

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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 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 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 (DMU{sub}s, 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.
机译：本文的目的是提供一种基于机械生产率指标的电力部门性能排名的框架。为了对该行业进行排名，非确定性方法的组合，遗传算法（下次GA）和两个确定性方法，原理分量分析（下在PCA）和数值分类（下面是NT）的所有分支机构（Sub电力部门的部门。换句话说，所提到的方法的所有有用和有影响力的点用于测量电力部门的性能。在本研究中，GA的有效性由PCA和NT验证。此外，两种非参数相关方法，Spearman相关实验和Kendall Tau用于确定Ga，PCA和NT的结果之间的相关性。结果，示出了大程度的相关性。为实现本研究的目标，进行了全面的研究，以认识到所有经济和技术指标（指标）对机器绩效产生巨大影响。这些指标与机器生产力，效率，有效性和盈利能力有关。标准因素如停机时间，修复时间，故障，操作时间，增值与生产值之间的平均时间被认为是塑造因子。根据ISIC（国际标准工业分类）代码，该行业的所有经济活动都被确定为2,3和4位数的代码。通过这些代码，电源扇区中的所有分支分层分层分类为2到4位数代码。在该研究中，用于测量10个指示器的数据库是基于ISIC代码形成并从发展中国家的电力界收集。然后，选择所生成的一代人中最好的分支（DMU {Sub}，决策单位）的最佳阵列。该阵列是优化Ga中的适合度函数的功率子扇区等级。此外，通过PCA识别每个10个指标对性能的主要影响。最后，分析了结果以促进整体系统性能。

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