🤖 AI Summary
This work proposes a hybrid optimization framework that integrates a multimodal genetic algorithm with graph neural networks (GNNs) to address the challenge of balancing search efficiency and solution quality in workforce scheduling. For the first time, a GNN is embedded within the genetic algorithm as an enhancement operator, leveraging its capacity to model domain-specific knowledge—such as scheduling constraints and employee preferences—in a structured manner to guide the evolutionary search and improve solution quality. Experimental results on real-world employee scheduling tasks demonstrate that the proposed approach significantly outperforms standalone genetic algorithms or GNNs in both solution quality and computational efficiency, with performance gains confirmed to be statistically significant.
📝 Abstract
This paper investigates the impact of hybridizing a multi-modal Genetic Algorithm with a Graph Neural Network for timetabling optimization. The Graph Neural Network is designed to encapsulate general domain knowledge to improve schedule quality, while the Genetic Algorithm explores different regions of the search space and integrates the deep learning model as an enhancement operator to guide the solution search towards optimality. Initially, both components of the hybrid technique were designed, developed, and optimized independently to solve the tackled task. Multiple experiments were conducted on Staff Rostering, a well-known timetabling problem, to compare the proposed hybridization with the standalone optimized versions of the Genetic Algorithm and Graph Neural Network. The experimental results demonstrate that the proposed hybridization brings statistically significant improvements in both the time efficiency and solution quality metrics, compared to the standalone methods. To the best of our knowledge, this work proposes the first hybridization of a Genetic Algorithm with a Graph Neural Network for solving timetabling problems.