Optimizing Job Allocation using Reinforcement Learning with Graph Neural Networks

TL;DR

This paper introduces a novel method combining Reinforcement Learning and Graph Neural Networks to improve job allocation in complex scheduling tasks, demonstrating superior performance over traditional algorithms.

Contribution

It presents a new RL-GNN based approach for job allocation that learns adaptive policies without manual annotations, applicable to real-world scheduling problems.

Findings

Outperforms baseline algorithms in synthetic data

Effective on real-world scheduling scenarios

Demonstrates generalizability across different datasets

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.