Article: Optimisation of resource scheduling in English translation teaching platform based on greedy heuristic task migration algorithm and corpus Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.48 pp.63 - 87 Abstract: This study tackles inefficient resource allocation in concurrent English translation teaching platforms by proposing the C-GHM model. This model integrates a greedy heuristic task migration algorithm with multi-dimensional corpus features. It constructs a priority evaluation system (using vectors like task professionalism and syntactic complexity) and a simulated annealing optimisation layer for intelligent computing resource allocation. Experimental results show C-GHM significantly outperforms traditional algorithms: reducing average task completion time to 125.3 seconds, increasing throughput to 45.2 tasks/second, and optimising load imbalance to 0.15. It also excels in robustness, energy efficiency, and scalability tests. Its core contribution is a transferable, collaborative scheduling framework that synergistically combines greedy heuristics, corpus features, and simulated annealing, achieving superior performance in heterogeneous task environments, with potential applications beyond translation platforms. Inderscience Publishers - linking academia, business and industry through research

Title: Optimisation of resource scheduling in English translation teaching platform based on greedy heuristic task migration algorithm and corpus

Authors: Jiali Min

Addresses: School of Foreign Languages, Nanchang Institute of Technology, Nanchang, 330044, China

Abstract: This study tackles inefficient resource allocation in concurrent English translation teaching platforms by proposing the C-GHM model. This model integrates a greedy heuristic task migration algorithm with multi-dimensional corpus features. It constructs a priority evaluation system (using vectors like task professionalism and syntactic complexity) and a simulated annealing optimisation layer for intelligent computing resource allocation. Experimental results show C-GHM significantly outperforms traditional algorithms: reducing average task completion time to 125.3 seconds, increasing throughput to 45.2 tasks/second, and optimising load imbalance to 0.15. It also excels in robustness, energy efficiency, and scalability tests. Its core contribution is a transferable, collaborative scheduling framework that synergistically combines greedy heuristics, corpus features, and simulated annealing, achieving superior performance in heterogeneous task environments, with potential applications beyond translation platforms.

Keywords: greedy heuristic task migration algorithm; English translation teaching platform; resource scheduling optimisation; corpus-driven; multi-objective optimisation.

DOI: 10.1504/IJICT.2026.153547

International Journal of Information and Communication Technology, 2026 Vol.27 No.48, pp.63 - 87

Received: 07 Jan 2026
Accepted: 01 Feb 2026
Published online: 13 May 2026 *

Title: Optimisation of resource scheduling in English translation teaching platform based on greedy heuristic task migration algorithm and corpus

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