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Risk-based life cycle cost analysis using a two-level multi-objective genetic algorithm
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-1967-6604
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0001-5620-5265
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0001-7744-2155
2020 (English)In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 33, no 10-11, p. 1076-1088Article in journal (Refereed) Published
Abstract [en]

The aim of this study has been to develop a two-level multi-objective genetic algorithm (MOGA) to optimize risk-based LCC analysis to find the optimal maintenance replacement time for road tunnel ventilation fans. Level 1 uses a MOGA based on a financial risk model to provide different risk percentages, while level 2 uses a MOGA based on an LCC model to estimate the optimal fan replacement time. Our method is compared with the approach of using a risk-based LCC model. The results are promising, showing that the risk-based LCC offers the possibility of significantly reducing the maintenance costs of the ventilation system by optimising the replacement schedule by considering the risk costs. The risk-based LCC can be used with repairable components, making it applicable, useful and implementable within Swedish Transport Administration (Trafikverket). In this study, MOGA operators have selected the cost of maintenance and risk data through the previous levels using different ways to provide different possible solutions. A drawback of the MOGA based on a risk-based LCC model with regard to its estimation is that a late replacement period over 20-year period might increase the maintenance cost. Therefore, the MOGA does not provide a good solution for a risk-based LCC.

Place, publisher, year, edition, pages
Taylor & Francis, 2020. Vol. 33, no 10-11, p. 1076-1088
Keywords [en]
Life cycle cost (LCC), multi-objective genetic algorithm (MOGA), risk-based life cycle cost, optimal maintenance replacement time, optimization
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-78947DOI: 10.1080/0951192X.2020.1757157ISI: 000534126700001Scopus ID: 2-s2.0-85084842349OAI: oai:DiVA.org:ltu-78947DiVA, id: diva2:1431226
Conference
1st International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM2019), 17-19 October, 2019, Dublin, Ireland
Note

Godkänd;2020;Nivå 0;2020-12-03 (alebob);Konferensartikel i tidskrift

Available from: 2020-05-19 Created: 2020-05-19 Last updated: 2020-12-03Bibliographically approved

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Al-Douri, Yamur K.Al-Chalabi, HussanLundberg, Jan

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