Multi Objective Decision Support Model and Dynamic Path Planning Strategy for Improving Research Efficiency in Universities

With the rapid growth of China's higher education sector, universities are continuously increasing their investment in scientific research. Maximizing the management efficiency and benefits of research funding and personnel allocation is an important factor in promoting scientific innovation and improving the quality of education. This article focuses on building a multi-objective decision support system and dynamic path planning strategy for universities aimed at enhancing research efficiency, with the aim of providing new solutions to the complexity and uncertainty challenges faced by research funding and personnel management. This article proposes a multiobjective decision support model that integrates reinforcement learning (RL) and genetic algorithm (GA). With the support of neural networks, RL enables intelligent agents to have stronger decision-making capabilities. This model can comprehensively consider the diverse objectives of scientific research projects, utilize RL mechanisms, and combine GA's global optimization capabilities to explore the optimal allocation of funds and personnel. The results show that the model can effectively improve the management efficiency of research funding and personnel allocation, providing a certain degree of support for the improvement of research efficiency in universities.