Intelligent Scheduling Algorithms and Resource Allocation Methods in Library Digitization Management

Abstract

Addressing the issue of intelligent scheduling and resource allocation optimization for library digital resources, this paper first designs a cloud-service-based library database resource model framework and studies the required multi-objective combinatorial optimization system based on the knapsack model. Then, an optimization configuration model based on a multi-agent genetic algorithm is designed, combining a multi-agent system with local perception characteristics with a globally efficient genetic algorithm. The fitness of individuals is selected as the energy of the multi-agent system to achieve multi-objective collaborative optimization of digital resource allocation. Finally, library digital resource data from X University Library from 2016 to 2024 is selected for empirical analysis. The empirical analysis results indicate that the proposed model can reasonably perform multi-objective collaborative optimization of resource allocation quantities for X University Library. The multi-objective evaluation value of digital resource allocation at the library increased by 0.22 between 2016 and 2024. Additionally, the proposed model demonstrates significant advantages in terms of digital library resource allocation efficiency, enhancing the collaborative level and efficiency between multi-objective evaluations of digital resources. This lays the foundation for future research on digital resource allocation in other domains of higher education institutions.