Journal article
A Modeling Approach to Maintenance Decisions Using Statistical Quality Control and Optimization
Quality and reliability engineering international, Vol.21(4), pp.355-366
06/2005
DOI: 10.1002/qre.616
Abstract
Maintenance concerns impact systems in every industry and effective maintenance policies are important tools. We present a methodology for maintenance decision making for deteriorating systems under conditions of uncertainty that integrates statistical quality control (SQC) and partially observable Markov decision processes (POMDPs). We use simulation to develop realistic maintenance policies for real-world environments. Specifically, we use SQC techniques to sample and represent real-world systems. These techniques help define the observation distributions and structure for a POMDP. We propose a simulation methodology for integrating SQC and POMDPs in order to develop and evaluate optimal maintenance policies as a function of process characteristics, system operating and maintenance costs. A two-state machine replacement problem is used as an example of how the method can be applied. A simulation program developed using Visual Basic for Excel yields results on the optimal probability threshold and on the accuracy of the decisions as a function of the initial belief about the condition of the machine. This work lays a foundation for future research that will help bring maintenance decision models into practice. Copyright © 2005 John Wiley & Sons, Ltd.
Details
- Title: Subtitle
- A Modeling Approach to Maintenance Decisions Using Statistical Quality Control and Optimization
- Creators
- Julie Simmons Ivy - School of Business Administration, University of Michigan, Ann Arbor, MI 48109, U.S.AHarriet Black Nembhard - Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, U.S.A
- Resource Type
- Journal article
- Publication Details
- Quality and reliability engineering international, Vol.21(4), pp.355-366
- Publisher
- John Wiley & Sons, Ltd
- DOI
- 10.1002/qre.616
- ISSN
- 0748-8017
- eISSN
- 1099-1638
- Number of pages
- 12
- Language
- English
- Date published
- 06/2005
- Academic Unit
- Industrial and Systems Engineering; Engineering Administration
- Record Identifier
- 9984121867802771
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