Maintenance and Repair Decision Making for Infrastructure Facilities without a Deterioration Model
Publication: Journal of Infrastructure Systems
Volume 10, Issue 1
Abstract
In the existing approach to maintenance and repair decision making for infrastructure facilities, policy evaluation and policy selection are performed under the assumption that a perfect facility deterioration model is available. The writer formulates the problem of developing maintenance and repair policies as a reinforcement learning problem in order to address this limitation. The writer explains the agency-facility interaction considered in reinforcement learning and discuss the probing-optimizing dichotomy that exists in the process of performing policy evaluation and policy selection. Then, temporal-difference learning methods are described as an approach that can be used to address maintenance and repair decision making. Finally, the results of a simulation study are presented where it is shown that the proposed approach can be used for decision making in situations where complete and correct deterioration models are not (yet) available.
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Information & Authors
Information
Published In
Journal of Infrastructure Systems
Volume 10 • Issue 1 • March 2004
Pages: 1 - 8
Copyright
Copyright © 2004 American Society of Civil Engineers.
History
Received: Oct 25, 2002
Accepted: Jul 29, 2003
Published online: Feb 19, 2004
Published in print: Mar 2004
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