Research on Human Resource Performance Evaluation and Optimization Decision-Making Based on Entropy Weight - TOPSIS Method

In technology-intensive enterprises, particularly those engaged in electromagnetic engineering, wireless communication, and antenna-related research and development, performance evaluation plays a critical role in talent allocation, innovation sustainability, and long-term organizational competitiveness. However, the complexity of R&D activities and the nonlinear characteristics of engineering outputs render conventional qualitative assessment approaches inadequate. This study proposes a hybrid multi-criteria decision-making framework that integrates the Entropy Weight Method (EWM) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to achieve objective and reproducible performance evaluation. The EWM is employed to determine indicator weights through statistical dispersion analysis, thereby reducing subjective bias in weighting assignment, while TOPSIS ranks personnel by measuring their relative closeness to the positive and negative ideal solutions. An empirical analysis involving ten R&D engineers demonstrates that the proposed framework provides highly discriminative and stable performance rankings. The results indicate that innovation-oriented indicators contribute most significantly to personnel differentiation, whereas operational indicators mainly reflect baseline organizational requirements. Sensitivity analysis further confirms the robustness of the ranking under varying weighting scenarios. The proposed method provides a scientific and quantitative decision-support tool for human resource optimization and offers practical reference value for research team management and innovation capability assessment in advanced electromagnetic engineering and communication technology development.