Dynamic scheduling of maintenance tasks in the petroleum industry: A reinforcement approach

Authors:
N. Aissani;B. Beldjilali;D. Trentesaux
Affiliations:
Department of Computer sciences, University of Oran ES-Sénia, BP 1524 El M'naour, Oran, Algeria;Department of Computer sciences, University of Oran ES-Sénia, BP 1524 El M'naour, Oran, Algeria;LAMIH-CNRS, Department of Production Systems, University of Valenciennes & Hainaut-Cambréésis, Le mont Houy, F-59313 Valenciennes cedex 09, France
Venue:
Engineering Applications of Artificial Intelligence
Year:
2009

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Abstract

Petroleum industry production systems are highly automatized. Maintenance of such systems is vital, not only to maintain production efficiency but also to insure minimal safety levels. Maintenance task scheduling is difficult since some tasks are already identified because they must be done repeatedly, and other tasks need to be identified dynamically. In this paper, we present a multi-agent approach for the dynamic maintenance task scheduling for a petroleum industry production system. Agents simultaneously insure effective maintenance scheduling and the continuous improvement of the solution quality by means of reinforcement learning, using the SARSA algorithm. Reinforcement learning allows the agents to adapt, learning the best behaviors for their various roles without reducing the performance or reactivity. To demonstrate the innovation of our approach, we include a computer simulation of our model and the results of experimentation applying our model to an Algerian petroleum refinery.