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Documents authored by Pesant, Gilles

Document
DOI: 10.4230/LIPIcs.CP.2023.6
Optimization of Short-Term Underground Mine Planning Using Constraint Programming

Authors: Younes Aalian, Gilles Pesant, and Michel Gamache

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract
Short-term underground mine planning problems are often difficult to solve due to the large number of activities and diverse machine types to be scheduled, as well as multiple operational constraints. This paper presents a Constraint Programming (CP) model to optimize short-term scheduling for the Meliadine underground gold mine in Nunavut, Canada, taking into consideration operational constraints and the daily development and production targets of the mine plan. To evaluate the efficacy of the developed CP short-term planning model, we compare schedules generated by the CP model with the ones created manually by the mine planner for two real data sets. Results demonstrate that the CP model outperforms the manual approach by generating more efficient schedules with lower makespans.
Cite as

Younes Aalian, Gilles Pesant, and Michel Gamache. Optimization of Short-Term Underground Mine Planning Using Constraint Programming. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 6:1-6:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{aalian_et_al:LIPIcs.CP.2023.6,
  author =	{Aalian, Younes and Pesant, Gilles and Gamache, Michel},
  title =	{{Optimization of Short-Term Underground Mine Planning Using Constraint Programming}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{6:1--6:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.6},
  URN =		{urn:nbn:de:0030-drops-190430},
  doi =		{10.4230/LIPIcs.CP.2023.6},
  annote =	{Keywords: Mine planning, Constraint Programming, Short-term planning, Underground mine, Scheduling}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.30
Combining Reinforcement Learning and Constraint Programming for Sequence-Generation Tasks with Hard Constraints

Authors: Daphné Lafleur, Sarath Chandar, and Gilles Pesant

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
While Machine Learning (ML) techniques are good at generating data similar to a dataset, they lack the capacity to enforce constraints. On the other hand, any solution to a Constraint Programming (CP) model satisfies its constraints but has no obligation to imitate a dataset. Yet, we sometimes need both. In this paper we borrow RL-Tuner, a Reinforcement Learning (RL) algorithm introduced to tune neural networks, as our enabling architecture to exploit the respective strengths of ML and CP. RL-Tuner maximizes the sum of a pretrained network’s learned probabilities and of manually-tuned penalties for each violated constraint. We replace the latter with outputs of a CP model representing the marginal probabilities of each value and the number of constraint violations. As was the case for the original RL-Tuner, we apply our algorithm to music generation since it is a highly-constrained domain for which CP is especially suited. We show that combining ML and CP, as opposed to using them individually, allows the agent to reflect the pretrained network while taking into account constraints, leading to melodic lines that respect both the corpus' style and the music theory constraints.
Cite as

Daphné Lafleur, Sarath Chandar, and Gilles Pesant. Combining Reinforcement Learning and Constraint Programming for Sequence-Generation Tasks with Hard Constraints. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 30:1-30:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{lafleur_et_al:LIPIcs.CP.2022.30,
  author =	{Lafleur, Daphn\'{e} and Chandar, Sarath and Pesant, Gilles},
  title =	{{Combining Reinforcement Learning and Constraint Programming for Sequence-Generation Tasks with Hard Constraints}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{30:1--30:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.30},
  URN =		{urn:nbn:de:0030-drops-166594},
  doi =		{10.4230/LIPIcs.CP.2022.30},
  annote =	{Keywords: Constraint programming, reinforcement learning, RNN, music generation}
}
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