Design and optimization of research performance allocation scheme based on TOPSIS entropy weight Method

ACKNOWLEDGEMENTS: This work was Supported by Special Research Project on Teaching Reform(Grant No. 30120300100-23-yb-jgkt03).

Abstract

Technology is the foundation of national strength, and technological innovation is the strategic support for improving social productivity and comprehensive national strength. It is the driving force for promoting social progress. Higher education institutions occupy a core position in the national innovation system, and their scientific research evaluation is not only the core strategy for controlling university scientific and technological activities, but also plays a crucial role in the top-level planning and resource allocation of higher education. For the convenience of analyzing the problem, we first use principal component analysis to evaluate the annual scientific research achievement rewards of employees; Using the entropy weight method again, TOPSIS scores the national level science and technology awards, national standards/specifications, provincial or industry standards/specifications, publication of works, number of current graduate students, newly approved national level projects, Chinese core, academic part-time jobs, and horizontal funding for employees. In addition, the entropy weight method is first used to analyze the weight of various indicators for each team. Each team selects 20 achievements for the school's annual performance evaluation ranking, and the total prize pool is allocated according to the ranking. Then, based on the number and weight of individual submitted works, a tiered allocation is carried out to reasonably calculate the performance distribution results for each member of the team. To achieve the optimal overall performance of the target group while balancing internal balance and fairness, the given formula is first used to quantitatively evaluate the annual individual achievements of each member, and the overall technological achievement score of the team is summarized. Then, based on these data, a performance allocation optimization model is constructed, and a genetic algorithm is used to set reasonable constraints to ensure fairness, motivation, and relative balance within the team, in order to develop a scientific and fair performance allocation plan and maximize the overall performance of the team.