Research and Practice on Strategic Planning and Resource Allocation Optimisation Model of Off-site Research Institute

• Published in: Applied mathematics and nonlinear sciences Vol. 9; no. 1
• Main Authors: Zhao, Yingtao, Hua, Zhilei, Liu, Yiying, Hao, Liqun
• Format: Journal Article
• Language: English
• Published: Beirut Sciendo 01.01.2024; De Gruyter Poland
• Subjects: 68-02; Algorithms; Efficiency; Linear programming; Multi-objective optimization model; Off-site research institute; Optimization; Particle swarm algorithm; Research centers; Resource allocation; Simulated annealing algorithm; Strategic planning
• ISSN: 2444-8656; 2444-8656
• DOI: 10.2478/amns-2024-1518

Off-site research institutes serve as crucial platforms for universities to facilitate the translation and application of their scientific research outcomes. Additionally, these institutes act as vital intermediaries for local governments to harmonize scientific inputs with economic development and play a key role in the industrial transformation and elevation of regional science and innovation levels. Drawing on the triple helix theory, this paper outlines strategic planning for off-site research institutes and develops a multi-objective linear programming model aimed at optimizing resource allocation. This model focuses on enhancing both the efficiency of resource utilization and the efficiency of resource allocation at these institutes. To address the issue of local minima commonly encountered in optimization algorithms, this study employs a simulated annealing algorithm to refine the performance of the particle swarm optimization algorithm. The resulting hybrid algorithm termed the simulated annealing particle swarm algorithm, is applied to solve the proposed model and investigate the determinants of optimal resource allocation. The findings indicate a significant improvement in resource allocation efficiency, with the coefficient for heterogeneous research institutes decreasing from an average of 0.84 in 2020 to 0.68. This optimization has led to a more effective and rational distribution of resources, better meeting the needs of the institutes. Furthermore, the analysis reveals that financial support and talent introduction and development account for approximately 69.7% of the variance in the optimized development of resource allocation at these institutes. The study provides actionable insights that could guide the optimal development of off-site research institutes, offering valuable references for future applications.