The distribution of scientific project funds model based on adaptive similarity fitting and NSGA-II

Boze Li (), Yandong He (), Yuxuan Xiu (), Bokui Chen () and Wai Kin Victor Chan ()
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Boze Li: Tsinghua University
Yandong He: Shenzhen Research Institute of Big Data
Yuxuan Xiu: China Southern Power Grid Co., Ltd.
Bokui Chen: Tsinghua University
Wai Kin Victor Chan: Tsinghua University

Scientometrics, 2024, vol. 129, issue 12, No 6, 7585-7622

Abstract: Abstract The distribution of scientific project funds is usually based on manual allocation, which is inefficient. Other automatic allocation methods are difficult to balance projects in different fields. In this study, we first utilize the adaptive similarity fitting method, leveraging historical project data to construct an input–output fitting model. Subsequently, we align the input–output model with data from projects awaiting funding through the application of scaling factors. Viewing project funds distribution as a multi-objective optimization problem, we employ the NSGA-II algorithm for optimization. Cases in a certain region illustrate the efficacy of our approach in the efficient distribution of research project funds, addressing the diverse preferences of decision-makers. After applying our method to reassign funds for a research grant project in a certain region, while keeping the total funding amount unchanged, the research and talent output in the region for the year 2020 are expected to increase by 10.63% and 6%, respectively. Similarly, for the year 2021, the increases in research and talent output are 6.09% and 6.64%. The total funding amount for the year 2020 can be reduced by 11.67% with the output stays the same, and for 2021, the funding amount can be reduced by 7%.

Keywords: Multi-objective optimization; Funds distribution model; Adaptive similarity fitting; NSGA-II (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11192-024-05190-1

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