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Title of article :
Dynamic scheduling of maintenance tasks in the petroleum industry: A reinforcement approach
Author/Authors :
Aissani، نويسنده , , N. and Beldjilali، نويسنده , , B. and Trentesaux، نويسنده , , D.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2009
Pages :
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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.
Keywords :
On-line scheduling , reinforcement learning , Maintenance tasks , Multi-agent system , petroleum industry
Journal title :
Engineering Applications of Artificial Intelligence
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Link To Document :