Forward Management of Spare Parts Stock Shortages Via Causal Reasoning Using Reinforcement Learning - Archive ouverte HAL Accéder directement au contenu
Proceedings/Recueil Des Communications Année : 2015

Forward Management of Spare Parts Stock Shortages Via Causal Reasoning Using Reinforcement Learning

Nouha Ghorbel
  • Fonction : Auteur
Sid-Ali Addouche
Abderrahman El Mhamedi

Résumé

Our role in this paper was to search the appropriate means serving as a decision support tool for the choice of a policy and procurement planning of spare parts, contributing to the maintenance in operational conditions of industrial equipment and enabling to avoid stock outs and all at a lower cost. For this, we present a generic Bayesian model of consumption of spare parts in a replenishment policy type (T, s, S) adapted (mT, s *, S). The originality of this research is the fact that we characterize the process of consumption of spare parts by a set of typical scenarios, called "consumption configurations" and identified by a system of performance indicators using variables state in a Bayesian model. After defining all these indicators, the research enchain to deploy a Bayesian network which allow, through Bayesian simulation, obtaining a replenishment planning indicating the optimal combination by period: the durations of these replenishment periods, quantities to purchased, types of SP (new or revalorized), costs and associated risk of rupture, purchasing costs and induced storage.

Dates et versions

hal-01230171 , version 1 (17-11-2015)

Identifiants

Citer

Nouha Ghorbel, Sid-Ali Addouche, Abderrahman El Mhamedi. Forward Management of Spare Parts Stock Shortages Via Causal Reasoning Using Reinforcement Learning. IFAC-PapersOnLine, 15th IFAC Symposium on “Information Control Problems in Manufacturing,INCOM 2015, Canada. 48 (3), 2015, ⟨10.1016/j.ifacol.2015.06.224⟩. ⟨hal-01230171⟩
44 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More