The University of Southampton
University of Southampton Institutional Repository

Multi-objective optimization for periodic preventive maintenance

Multi-objective optimization for periodic preventive maintenance
Multi-objective optimization for periodic preventive maintenance
This article investigates a JIT single machine scheduling problem with a periodic preventive maintenance. Also to maintain the quality of the products, there is a limitation on the maximum number of allowable jobs in each period. The proposed bi-objective mixed integer model minimizes total earliness-tardiness and makespan simultaneously. Due to the computational complexity of the problem, multi-objective particle swarm optimization (MOPSO) algorithm is implemented. Also, as well as MOPSO, two other optimization algorithms are used for comparing the results. Eventually, Taguchi method with metrics analysis is presented to tune the algorithms' parameters and a multiple criterion decision making (MCDM) technique based on the technique for order of preference by similarity to ideal solution (TOPSIS) is applied to choose the best algorithm. Comparison results confirmed supremacy of MOPSO to the other algorithms.
MCDM, multi-objective optimization, periodic maintenance, scheduling, single machine, total earliness-tardiness
173-182
IEEE
Zade, Amir Ebrahimi
21fb69bf-388b-440b-85a1-6399b0b70f0b
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Maghsoudlou, Hamidreza
31029a70-7ce3-45c8-8db1-8218d39345e0
Toloo, Mehdi
917a363a-99e6-4ce9-9864-4b230621d09d
Zade, Amir Ebrahimi
21fb69bf-388b-440b-85a1-6399b0b70f0b
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Maghsoudlou, Hamidreza
31029a70-7ce3-45c8-8db1-8218d39345e0
Toloo, Mehdi
917a363a-99e6-4ce9-9864-4b230621d09d

Zade, Amir Ebrahimi, Barak, Sasan, Maghsoudlou, Hamidreza and Toloo, Mehdi (2016) Multi-objective optimization for periodic preventive maintenance. In, 2015 International Conference on Industrial Engineering and Systems Management (IESM). IEEE, pp. 173-182. (doi:10.1109/IESM.2015.7380154).

Record type: Book Section

Abstract

This article investigates a JIT single machine scheduling problem with a periodic preventive maintenance. Also to maintain the quality of the products, there is a limitation on the maximum number of allowable jobs in each period. The proposed bi-objective mixed integer model minimizes total earliness-tardiness and makespan simultaneously. Due to the computational complexity of the problem, multi-objective particle swarm optimization (MOPSO) algorithm is implemented. Also, as well as MOPSO, two other optimization algorithms are used for comparing the results. Eventually, Taguchi method with metrics analysis is presented to tune the algorithms' parameters and a multiple criterion decision making (MCDM) technique based on the technique for order of preference by similarity to ideal solution (TOPSIS) is applied to choose the best algorithm. Comparison results confirmed supremacy of MOPSO to the other algorithms.

Full text not available from this repository.

More information

Published date: 12 January 2016
Keywords: MCDM, multi-objective optimization, periodic maintenance, scheduling, single machine, total earliness-tardiness

Identifiers

Local EPrints ID: 434848
URI: https://eprints.soton.ac.uk/id/eprint/434848
PURE UUID: 168eeb26-3dad-4779-9ea5-ca3f2be8ed44
ORCID for Sasan Barak: ORCID iD orcid.org/0000-0001-7715-9958

Catalogue record

Date deposited: 11 Oct 2019 16:30
Last modified: 12 Oct 2019 00:20

Export record

Altmetrics

Contributors

Author: Amir Ebrahimi Zade
Author: Sasan Barak ORCID iD
Author: Hamidreza Maghsoudlou
Author: Mehdi Toloo

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×