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Learning Precedences for Scheduling Problems with Graph Neural Networks

Authors Hélène Verhaeghe , Quentin Cappart , Gilles Pesant , Claude-Guy Quimper

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Author Details

Hélène Verhaeghe
  • DTAI, KU Leuven, Belgium
Quentin Cappart
  • Polytechnique Montréal, Canada
Gilles Pesant
  • Polytechnique Montréal, Canada
Claude-Guy Quimper
  • Université Laval, Quebec, Canada

Funding

This research received funding from IVADO and the Canada First Research Excellence Fund / Apogée fund PostDoc-2022-2378128196 and in part by NSERC Discovery Grants 218028/2017. It also received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 101070149, project Tuples.

Acknowledgements

We thank the anonymous reviewers for their constructive criticism which helped us improve the original version of the paper.

Cite AsGet BibTex

Hélène Verhaeghe, Quentin Cappart, Gilles Pesant, and Claude-Guy Quimper. Learning Precedences for Scheduling Problems with Graph Neural Networks. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 30:1-30:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.CP.2024.30

Abstract

The resource constrained project scheduling problem (RCPSP) consists of scheduling a finite set of resource-consuming tasks within a temporal horizon subject to resource capacities and precedence relations between pairs of tasks. It is NP-hard and many techniques have been introduced to improve the efficiency of CP solvers to solve it. The problem is naturally represented as a directed graph, commonly referred to as the precedence graph, by linking pairs of tasks subject to a precedence. In this paper, we propose to leverage the ability of graph neural networks to extract knowledge from precedence graphs. This is carried out by learning new precedences that can be used either to add new constraints or to design a dedicated variable-selection heuristic. Experiments carried out on RCPSP instances from PSPLIB show the potential of learning to predict precedences and how they can help speed up the search for solutions by a CP solver.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Artificial intelligence
  • Computing methodologies → Machine learning
  • Mathematics of computing → Combinatorial optimization
Keywords
  • Scheduling
  • Precedence graph
  • Graph neural network

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