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This is a Quantitative Study study (also classified as: Experimental Study).

October 1, 2025ElectronicsOpen Access

A Multi-Constrained Knapsack Approach for Educational Resource Allocation: Genetic Algorithm with Category- Specific Optimization

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This research demonstrates a genetic algorithm for educational resource allocation, suggesting effective decision frameworks for complex constraints.

The proposed genetic algorithm improved solution quality by 11.5% while ensuring 100% feasibility for resource allocation.
Statistical tests confirmed significant advantages of the genetic algorithm over the baseline methods, enhancing decision-making efficacy.
Experiments involved realistic datasets representing multiple academic categories, refining approaches to resource allocation challenges.
Tabu Search demonstrated strong performance with a mean fitness of 1557.3 compared to the GA's 1533.2, highlighting competitive local search methods.

Tsamis et al. (2025) studied this question.

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This research demonstrates a genetic algorithm for educational resource allocation, suggesting effective decision frameworks for complex constraints.