多准则决策分析
层次分析法
模糊逻辑
计算机科学
稳健性(进化)
模糊集
电力工业
环境经济学
运筹学
电
经济
工程类
人工智能
电气工程
基因
化学
生物化学
作者
Rong Li,Jun Dong,Dongxue Wang
摘要
The austere environmental and resource developing situation in China poses adverse conditions to power generation enterprises, especially thermal power enterprises. Moreover, the new round electric market reform is being pushed forward vigorously, which greatly affects the future developing path for power generation enterprises. A comprehensive competition performance and ability evaluation is significant for power generation enterprises' laudable and sustainable development in the current situation. In this paper, a novel hybrid multi-criteria decision making (MCDM) framework is established for the evaluation. To effectively deal with the hesitation and uncertainty frequently occurring in the evaluation process, we combine the fuzzy set theory and the hesitant fuzzy linguistic term set (HFLTS) with traditional MCDM methods, Analytic Hierarchy Process (AHP), and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The HFLTS-AHP method is used to determine the weight allocation of criteria, and fuzzy-VIKOR further gives performance rankings of the alternatives. The evaluation index system is established based on sustainable perspective and enterprise management, which contains ten sub-criteria from four aspects, “economy,” “environment and society,” “resource and technology,” and “sustainable development.” In empirical analysis, four large power generation enterprises are selected to perform the competition ability evaluation with the proposed framework. The results provide a reliable competition performance rank for the four alternatives and indicate that during the evaluation process, the sub-criteria “return on capital,” “electricity trading rate,” and “renewable energy installed capacity proportion” are more important. Finally, to verify the validity and robustness of the model, a set of sensitivity analyses are conducted. The proposed hybrid MCDM framework shows the advantages and practical value for competition ability evaluation of power generation enterprises.
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