Oil Refining Enterprise Performance Evaluation Based on DEA and SVM

Jiekun Song, Zaixu Zhang
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引用次数: 6

Abstract

Enterprise performance evaluation is an important means of enterprise management, which can diagnose the whole development status of enterprise. Data envelopment analysis (DEA) is one of the most frequently used evaluation methods and support vector machine (SVM) is a novel method of data mining, which can be used for prediction and regression. Based on DEA and SVM, the paper proposes a method for evaluating and predicting enterprise performance. First, DEA method is used to evaluate DEA efficiency of all the oil refining enterprises performance. Then the input/output data and results of some decision making units (DMUs) are selected as the learning examples to train the SVM network and the others are used as the test examples to test the network. If the SVM network is testified well, a synthetic evaluation formula can be given to predict the DEA efficiency of a new DMU. A real example testifies the efficiency, practicability and intellectual ability of this method.
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基于DEA和SVM的炼油企业绩效评价
企业绩效评价是企业管理的重要手段,可以诊断企业的整体发展状况。数据包络分析(DEA)是最常用的评价方法之一,支持向量机(SVM)是一种新颖的数据挖掘方法,可以用于预测和回归。本文提出了一种基于DEA和SVM的企业绩效评价与预测方法。首先,采用DEA方法对各炼油企业绩效的DEA效率进行了评价。然后选取决策单元(dmu)的输入/输出数据和结果作为学习样例训练支持向量机网络,其余作为测试样例对网络进行测试。如果支持向量机网络验证良好,则可以给出一个综合评价公式来预测新的DMU的DEA效率。实例验证了该方法的有效性、实用性和智能性。
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