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Evaluation of the Impact of Selected Weighting Methods on the Results of the TOPSIS Technique
International Journal of Information Technology & Decision Making ( IF 2.5 ) Pub Date : 2019-10-07 , DOI: 10.1142/s021962201950041x
Roman Vavrek 1
Affiliation  

The present research deals with selected multi-criteria methods identified according to available literature sources as a suitable instrument for the comprehensive evaluation of a set of alternatives. Further, it focuses on a group of methods including Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is often associated in practice with the use of several subjective and objective methods for the determination of the weights of selected indicators. Accordingly, five of these objective methods are used in this research. The main contribution consists in emphasizing the differences between the results achieved through various methods for the determination of the weights of input indicators. Moreover, we restate the fact that the choice of an adequate method to weigh indicators significantly affects the overall results of the TOPSIS technique. The Coefficient of Variance method clearly identifies subjects in prominent positions. Subsequently, the Mean Weight method does not consider the structure of data and their variability, while focusing on all the indicators being equal. The results obtained via the Standard Deviation and the Statistical Variance Procedure methods are comparable with the results obtained for the identical weights of individual indicators, i.e., the Mean Weight method. Therefore, based on the overall results of our research for the determination of the weights of input indicators for the purposes of the TOPSIS technique, we recommend the use of the Standard Deviation method.

中文翻译:

评估所选加权方法对 TOPSIS 技术结果的影响

本研究涉及根据现有文献来源确定的选定多标准方法,作为对一组替代方案进行综合评估的合适工具。此外,它侧重于一组方法,包括与理想解决方案相似的偏好顺序技术(TOPSIS),这在实践中通常与使用几种主观和客观的方法来确定选定指标的权重有关。因此,本研究使用了其中五种客观方法。主要贡献在于强调通过确定投入指标权重的各种方法所取得的结果之间的差异。而且,我们重申了这样一个事实,即选择合适的衡量指标的方法会显着影响 TOPSIS 技术的整体结果。方差系数方法清楚地识别出显着位置的对象。随后,平均权重法不考虑数据的结构及其可变性,而侧重于所有指标均等。通过标准偏差和统计方差程序方法获得的结果与单个指标的相同权重即平均权重方法获得的结果具有可比性。因此,基于我们为 TOPSIS 技术确定输入指标权重的研究的总体结果,我们建议使用标准差方法。方差系数方法清楚地识别出显着位置的对象。随后,平均权重法不考虑数据的结构及其可变性,而侧重于所有指标均等。通过标准偏差和统计方差程序方法获得的结果与单个指标的相同权重即平均权重方法获得的结果具有可比性。因此,根据我们为 TOPSIS 技术确定输入指标权重的研究的总体结果,我们建议使用标准差方法。方差系数方法清楚地识别出显着位置的对象。随后,平均权重法不考虑数据的结构及其可变性,而侧重于所有指标均等。通过标准偏差和统计方差程序方法获得的结果与单个指标的相同权重即平均权重方法获得的结果具有可比性。因此,根据我们为 TOPSIS 技术确定输入指标权重的研究的总体结果,我们建议使用标准差方法。同时关注所有指标是否相等。通过标准偏差和统计方差程序方法获得的结果与单个指标的相同权重即平均权重方法获得的结果具有可比性。因此,根据我们为 TOPSIS 技术确定输入指标权重的研究的总体结果,我们建议使用标准差方法。同时关注所有指标是否相等。通过标准偏差和统计方差程序方法获得的结果与单个指标的相同权重即平均权重方法获得的结果具有可比性。因此,基于我们为 TOPSIS 技术确定输入指标权重的研究的总体结果,我们建议使用标准差方法。
更新日期:2019-10-07
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