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Communication Dans Un Congrès Année : 2022

Weighted-QMIX-based optimization for maintenance decision-making of multi-component systems

Van-Thai Nguyen
  • Fonction : Auteur
  • PersonId : 1323034
  • IdRef : 270817352
Phuc Do Van
Alexandre Voisin
Benoît Iung

Résumé

It is well-known that maintenance decision optimization for multi-component systems faces the curse of dimensionality. Specifically, the number of decision variables needed to be optimized grows exponentially in the number of components causing computational expensive for optimization algorithms. To address this issue, we customize a multi-agent deep reinforcement learning algorithm, namely Weighted QMIX, in the case where system states can be fully observed to obtain cost-effective policies. A case study is conducted on a 13- component system to examine the effectiveness of the customized algorithm. The obtained results confirmed its performance.

Dates et versions

hal-03784752 , version 1 (23-09-2022)

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Citer

Van-Thai Nguyen, Phuc Do Van, Alexandre Voisin, Benoît Iung. Weighted-QMIX-based optimization for maintenance decision-making of multi-component systems. 7th European Conference of the Prognostics and Health Management Society, PHME22, Jul 2022, Turin, Italy. ⟨10.36001/phme.2022.v7i1.3319⟩. ⟨hal-03784752⟩
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