An optimal stopping rule for sensor-driven replacement and spare parts inventory decision models

A.M.H. Elwany, K. Kaiser, N.Z. Gebraeel

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

A sensor-driven decision model is proposed to determine and dynamically revise optimal replacement and inventory policies for partially degraded components given real-time sensory information. An optimal stopping rule is then developed to decide when to stop updating and implement the most recently updated decision policy. The presented real-world case study demonstrates that this results in better decisions and reduced costs due to the improved prediction accuracy of components’ lifetimes.
Original languageEnglish
Title of host publicationProceedings of the INFORMS Annual Meeting, November 4-7, 2007, Seattle, Washington
Place of PublicationSeattle
PublisherINFORMS Institute for Operations Research and the Management Sciences
Publication statusPublished - 2009
EventINFORMS Annual Meeting 2007 -
Duration: 1 Jan 2007 → …

Conference

ConferenceINFORMS Annual Meeting 2007
Period1/01/07 → …
OtherINFORMS Annual Meeting 2007

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