Evaluation of Enterprise Performance Based on FLI-GA Model

• Main Authors: Shouyi Zhang, Xiaomin Zhu, Jiandong Geng, Runtong Zhang
• Format: Article
• Language: English
• Published: OmniaScience 2014-05-01
• Series: Journal of Industrial Engineering and Management
• Subjects: FLI-GA model; performance evaluation; Genetic Algorithm
• Online Access: http://www.jiem.org/index.php/jiem/article/view/959
• ISSN: 2013-8423; 2013-0953

<p class="Abstract"><strong><em><span lang="EN-US">Purpose:</span></em></strong><span lang="EN-US"> There are many </span><span lang="EN-US">kinds of </span><span lang="EN-US">methods to evaluate the performance of enterprise, but they still have some </span><span lang="EN-US">distinct </span><span lang="EN-US">shortcomings. In order to </span><span lang="EN-US">achieve</span><span lang="EN-US"> a better evaluation result, we put forward a new model named F</span><span lang="EN-US">LI-</span><span lang="EN-US">G</span><span lang="EN-US">A</span><span lang="EN-US"> (Fuzzy logic inference &amp; Genetic algorithm). </span></p> <p class="Abstract"><strong><em><span lang="EN-US">A</span><span lang="EN-US">pproach:</span></em></strong><span lang="EN-US"> This model</span><span lang="EN-US">, </span><span lang="EN-US">mainly based on the fuzzy logic inference </span><span lang="EN-US">method, </span><span lang="EN-US">uses fuzzy rule-based system (FRBS)</span><span lang="EN-US"> to </span><span lang="EN-US">avoid the drawbacks of FRBS</span><span lang="EN-US">. G</span><span lang="EN-US">enetic algorithm is applied in this model. </span></p> <p class="Abstract"><strong><em><span lang="EN-US">Findings:</span></em></strong><span lang="EN-US"> </span><span lang="EN-US">FLI-GA model can be used to evaluate certain enterprise performance, and its evaluation results are more accurate than fuzzy logic inference method. </span></p> <p class="Abstract"><strong><em><span lang="EN-US">Originality:</span></em></strong><span lang="EN-US"> </span><span lang="EN-US">T</span><span lang="EN-US">his model combines the genetic algorithm with the unclear reasoning methods so as to make the appraisal results more reasonable and more satisfying.</span></p>