Optimizing Job Allocation using Reinforcement Learning with Graph Neural Networks

• URL: http://arxiv.org/abs/2501.19063v1
• Date: Fri, 31 Jan 2025 11:50:04 GMT
• Title: Optimizing Job Allocation using Reinforcement Learning with Graph Neural Networks
• Authors: Lars C. P. M. Quaedvlieg,
• Abstract summary: Efficient job allocation in complex scheduling problems poses significant challenges in real-world applications.<n>We propose a novel approach that leverages the power of Reinforcement Learning (RL) and Graph Neural Networks (GNNs) to tackle the Job Allocation Problem (JAP)<n>Our approach enables learning of adaptive policies through trial-and-error interactions with the environment while exploiting the graph-structured data of the problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient job allocation in complex scheduling problems poses significant challenges in real-world applications. In this report, we propose a novel approach that leverages the power of Reinforcement Learning (RL) and Graph Neural Networks (GNNs) to tackle the Job Allocation Problem (JAP). The JAP involves allocating a maximum set of jobs to available resources while considering several constraints. Our approach enables learning of adaptive policies through trial-and-error interactions with the environment while exploiting the graph-structured data of the problem. By leveraging RL, we eliminate the need for manual annotation, a major bottleneck in supervised learning approaches. Experimental evaluations on synthetic and real-world data demonstrate the effectiveness and generalizability of our proposed approach, outperforming baseline algorithms and showcasing its potential for optimizing job allocation in complex scheduling problems.

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