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Maintenance strategy selection problem: an MCDM overview

Mahmood Shafiee (Cranfield University, Cranfield, UK)

Journal of Quality in Maintenance Engineering

ISSN: 1355-2511

Article publication date: 12 October 2015

3207

Abstract

Purpose

Maintenance strategy selection (MSS) is considered as a complex multiple-criteria decision-making (MCDM) problem. The purpose of this paper is to present a comprehensive review on the use and application of MCDM approach and its associated case studies in the field of MSS.

Design/methodology/approach

The paper systematically classifies the published literature of both researchers and practitioners and then analyzes and reviews it methodically.

Findings

This paper outlines the important issues relevant to the subject, including the techniques used for data collection, the quantitative and qualitative criteria taken into account in decision making, the maintenance strategies considered for evaluation, the methods applied to find the solution, and the type of industries being studied. In each category, the gaps are identified along with recommendations for the future research work.

Practical implications

Literature on classification of the MCDM models used to select the most appropriate maintenance strategy is very limited. The proposed classification scheme not only will be useful to researchers, but also assists maintenance professionals to find the models that fit their specific needs.

Originality/value

The paper provides many references in the field, including the articles published in academic journals, conference papers, master and doctoral dissertations, text books, and industrial reports, and suggests a classification scheme according to various attributes.

Keywords

Citation

Shafiee, M. (2015), "Maintenance strategy selection problem: an MCDM overview", Journal of Quality in Maintenance Engineering, Vol. 21 No. 4, pp. 378-402. https://doi.org/10.1108/JQME-09-2013-0063

Publisher

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Emerald Group Publishing Limited

Copyright © 2015, Emerald Group Publishing Limited

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