Inter-company comparison using modified TOPSIS with objective weights
Computers and Operations Research
Customer evaluation for order acceptance using a novel class of fuzzy methods based on TOPSIS
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Evaluation of the competitiveness of high-tech industry is a technical decision-making issue involving multiple criteria. It is also a practical path to promote a country's competitiveness. However, the competitiveness indicators in high-tech industry often act and react upon one another. Moreover, different dimensions and indicator weights also affect the evaluation results. In this paper, the Mahalanobis distance is used to improve the traditional technique for order preference by similarity to ideal solution (TOPSIS). The improved TOPSIS method has the following properties: (1) an improved relative closeness which is invariant after non-singular linear transformation, and (2) the weighted Mahalanobis distance is the same as the weighted Euclidean distance when the indicators are uncorrelated. The new method is applied to evaluate the competitiveness of the Chinese high-tech industry using data from 2011. Consideration of the correlation between indicators improves the evaluation results (in terms of sorting and closeness) to a certain extent compared to the traditional TOPSIS method. The top five provinces are: Guangdong, Jiangsu, Shanghai, Beijing, and Shandong. This finding reflects the practical linkage among provinces and softens the closeness value, consistent with reality.