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

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

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

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

Operations Planning Accounting & Control

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	Proceedings of the INFORMS Annual Meeting, November 4-7, 2007, Seattle, Washington
Place of Publication	Seattle
Publisher	INFORMS Institute for Operations Research and the Management Sciences
Publication status	Published - 2009
Event	INFORMS Annual Meeting 2007 - Seattle, United States Duration: 3 Nov 2007 → 6 Nov 2007

Conference

Conference	INFORMS Annual Meeting 2007
Country/Territory	United States
City	Seattle
Period	3/11/07 → 6/11/07
Other	INFORMS Annual Meeting 2007

Cite this

@inproceedings{21a0553202a041b6a5d5a2de412361e6,

title = "An optimal stopping rule for sensor-driven replacement and spare parts inventory decision models",

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{\textquoteright} lifetimes.",

author = "A.M.H. Elwany and K. Kaiser and N.Z. Gebraeel",

year = "2009",

language = "English",

booktitle = "Proceedings of the INFORMS Annual Meeting, November 4-7, 2007, Seattle, Washington",

publisher = "INFORMS Institute for Operations Research and the Management Sciences",

address = "United States",

note = "INFORMS Annual Meeting 2007 ; Conference date: 03-11-2007 Through 06-11-2007",

}

Elwany, AMH, Kaiser, K & Gebraeel, NZ 2009, An optimal stopping rule for sensor-driven replacement and spare parts inventory decision models. in Proceedings of the INFORMS Annual Meeting, November 4-7, 2007, Seattle, Washington. INFORMS Institute for Operations Research and the Management Sciences, Seattle, INFORMS Annual Meeting 2007, Seattle, Washington, United States, 3/11/07.

An optimal stopping rule for sensor-driven replacement and spare parts inventory decision models. / Elwany, A.M.H.; Kaiser, K.; Gebraeel, N.Z.
Proceedings of the INFORMS Annual Meeting, November 4-7, 2007, Seattle, Washington. Seattle: INFORMS Institute for Operations Research and the Management Sciences, 2009.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - An optimal stopping rule for sensor-driven replacement and spare parts inventory decision models

AU - Elwany, A.M.H.

AU - Kaiser, K.

AU - Gebraeel, N.Z.

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

M3 - Conference contribution

BT - Proceedings of the INFORMS Annual Meeting, November 4-7, 2007, Seattle, Washington

PB - INFORMS Institute for Operations Research and the Management Sciences

CY - Seattle

T2 - INFORMS Annual Meeting 2007

Y2 - 3 November 2007 through 6 November 2007

ER -

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

Abstract

Conference

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