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Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems

Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems
Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems

The key to achieving optimum ship system reliability and safety is to have a sound maintenance management system in place for mitigating or eliminating equipment/component failures. Maintenance has three key elements; risk assessment, maintenance strategy selection and the process of determining the optimal interval for the maintenance task. The optimisation of these three main elements of maintenance is what constitute a sound maintenance management system. One of the challenges that marine maintenance practitioners are faced with is the problem of maintenance selection for each equipment item of the ship machinery system. The decision making process involves utilising different conflicting decision criteria in selecting the optimum maintenance strategy from among multiple maintenance alternatives. In tackling such decision making problems the application of a multi-criteria decision making (MCDM) method is appropriate. Hence in this paper two hybrid MCDM methods; Delphi-AHP and Delphi-AHP-PROMETHEE, are presented for the selection of appropriate maintenance strategies for ship machinery systems and other related ship systems. A case study of a ship machinery system maintenance strategy selection problem is used to demonstrate the suitability of the proposed methods.

Analytical hierarchy process, Delphi method, Machinery system, Maintenance strategy alternative, PROMETHEE
0956-5515
519-531
Emovon, Ikuobase
e0a51526-6e06-4c29-ba98-6ea531cee694
Norman, Rosemary A.
6d2518aa-ece8-498f-82dc-dee5ec7b1b37
Murphy, Alan J.
8e021dad-0c60-446b-a14e-cddd09d44626
Emovon, Ikuobase
e0a51526-6e06-4c29-ba98-6ea531cee694
Norman, Rosemary A.
6d2518aa-ece8-498f-82dc-dee5ec7b1b37
Murphy, Alan J.
8e021dad-0c60-446b-a14e-cddd09d44626

Emovon, Ikuobase, Norman, Rosemary A. and Murphy, Alan J. (2018) Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems. Journal of Intelligent Manufacturing, 29, 519-531. (doi:10.1007/s10845-015-1133-6).

Record type: Article

Abstract

The key to achieving optimum ship system reliability and safety is to have a sound maintenance management system in place for mitigating or eliminating equipment/component failures. Maintenance has three key elements; risk assessment, maintenance strategy selection and the process of determining the optimal interval for the maintenance task. The optimisation of these three main elements of maintenance is what constitute a sound maintenance management system. One of the challenges that marine maintenance practitioners are faced with is the problem of maintenance selection for each equipment item of the ship machinery system. The decision making process involves utilising different conflicting decision criteria in selecting the optimum maintenance strategy from among multiple maintenance alternatives. In tackling such decision making problems the application of a multi-criteria decision making (MCDM) method is appropriate. Hence in this paper two hybrid MCDM methods; Delphi-AHP and Delphi-AHP-PROMETHEE, are presented for the selection of appropriate maintenance strategies for ship machinery systems and other related ship systems. A case study of a ship machinery system maintenance strategy selection problem is used to demonstrate the suitability of the proposed methods.

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More information

Accepted/In Press date: 21 July 2015
e-pub ahead of print date: 30 July 2018
Keywords: Analytical hierarchy process, Delphi method, Machinery system, Maintenance strategy alternative, PROMETHEE

Identifiers

Local EPrints ID: 483836
URI: http://eprints.soton.ac.uk/id/eprint/483836
ISSN: 0956-5515
PURE UUID: b3a0fd1c-95ae-43c3-b958-cf08a635ba9d

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Date deposited: 06 Nov 2023 18:21
Last modified: 17 Mar 2024 05:37

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Contributors

Author: Ikuobase Emovon
Author: Rosemary A. Norman
Author: Alan J. Murphy

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