Evaluation of startup companies using multicriteria decision making based on hesitant fuzzy linguistic information envelopment analysis models
Evaluation of startup companies using multicriteria decision making based on hesitant fuzzy linguistic information envelopment analysis models
复制标题
基于犹豫模糊语言信息包络分析模型的多标准决策评估初创公司
DOI：
10.1002/int.22379
复制
发表时间：
2021-03-02
影响因子：
7
通讯作者：
Fujita, Hamido
中科院分区：
文献类型：
--
作者：
Lin, Mingwei;Chen, Zheyu;Fujita, Hamido
创业公司评价是科技企业孵化器的一个重要管理过程，也是一个典型的多目标决策问题。解决多准则决策问题的方法多种多样，但这些方法在很大程度上依赖于准确的准则权重值。这些方法的决策结果不稳定。此外，他们不能为非最优初创公司提供改进建议。为了克服这两个缺点，我们提出了一种新的犹豫模糊语言决策方法来解决初创公司的评估问题。为此，针对犹豫不决的模糊语言术语集，提出了一种基于专家心理和评分值与偏离度之比的语义比较方法。然后，提出了犹豫模糊语言信息包络效率的新定义，并在此基础上提出了一种新的犹豫模糊语言信息包络分析模型和一种新的偏好模型。通过求解这些模型，可以对所有方案进行排序，并对非最优方案进行改进。最后，通过数值分析说明了所提模型的适用性，并对所提模型进行了稳健性分析。同时，将它们与以往的犹豫模糊语言决策方法进行了比较。
Evaluating startup companies is an important management process for technology business incubators and it is also a typical multicriteria decision-making (MCDM) problem. There exist various methods that have proposed to solve MCDM problems, but these methods heavily depend on the exact criteria weight values. The decision results of these methods are unstable. Moreover, they cannot provide the improvement suggestions for the nonoptimal startup companies. To overcome these two drawbacks, we propose a novel hesitant fuzzy linguistic decision-making method to solve the problem of evaluating startup companies. To this end, a novel semantic comparison method based on the experts' psychology and the ratio of score value to deviation degree is proposed to compare the hesitant fuzzy linguistic term sets. Then, a novel definition of hesitant fuzzy linguistic information envelopment efficiency (HFLIEE) is proposed, based on which, a novel hesitant fuzzy linguistic information envelopment analysis (HFLIEA) model and a novel preference model are proposed. By solving these models, all the alternatives can be ranked and nonoptimal alternatives can be improved. Finally, the numerical analysis is given to illustrate the applicability of the proposed models and the robustness analyses of the proposed models are provided. At the same time, they are compared with the previous hesitant fuzzy linguistic decision-making methods.