Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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PSS Evaluation Based on Vague Assessment Big Data: Hybrid Model of Multi-Weight Combination and Improved TOPSIS by Relative Entropy

  • Received : 2020.05.21
  • Accepted : 2021.03.05
  • Published : 2024.06.30
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Abstract

Driven by the vague assessment big data, a product service system (PSS) evaluation method is developed based on a hybrid model of multi-weight combination and improved TOPSIS by relative entropy. The index values of PSS alternatives are solved by the integration of the stakeholders' vague assessment comments presented in the form of trapezoidal fuzzy numbers. Multi-weight combination method is proposed for index weight solving of PSS evaluation decision-making. An improved TOPSIS by relative entropy (RE) is presented to overcome the shortcomings of traditional TOPSIS and related modified TOPSIS and then PSS alternatives are evaluated. A PSS evaluation case in a printer company is given to test and verify the proposed model. The RE closeness of seven PSS alternatives are 0.3940, 0.5147, 0.7913, 0.3719, 0.2403, 0.4959, and 0.6332 and the one with the highest RE closeness is selected as the best alternative. The results of comparison examples show that the presented model can compensate for the shortcomings of existing traditional methods.

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Acknowledgement

This paper is funded by National Natural Science Foundation of China (No. 52165061).

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