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Maintenance optimization with duration-dependent costs

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  • Markus Bohlin
  • Mathias Wärja

Abstract

High levels of availability and reliability are essential in many industries where production is subject to high costs due to downtime. Examples include the mechanical drive in natural gas pipelines and power generation on oil platforms, where gas turbines are commonly used as a power source. To mitigate the effects of service outages and increase overall reliability, it is also possible to use one or more redundant units serving as cold standby backup units. In this paper, we consider preventive maintenance optimization for parallel k-out-of-n multi-unit systems, where production at a reduced level is possible when some of the units are still operational. In such systems, there are both positive and negative effects of grouping activities together. The positive effects come from parallel execution of maintenance activities and shared setup costs, while the negative effects come from the limited number of units which can be maintained at the same time. To show the possible economic effects, we evaluate the approach on models of two production environments under a no-fault assumption. We conclude that savings were substantial in our experiments on preventive maintenance, compared to a traditional preventive maintenance plan. For single-unit systems, costs were on average 39 % lower when using optimization. For multi-unit systems, average savings were 19 %. We also used the optimization models to evaluate the effects of re-planning at breakdown and effects due to modeling of inclusion relations. Breakdown re-planning saved between 0 and 11 % of the maintenance costs, depending on which component failed, while inclusion relation modeling resulted in an 7 % average cost reduction. Copyright Springer Science+Business Media, LLC 2015

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  • Markus Bohlin & Mathias Wärja, 2015. "Maintenance optimization with duration-dependent costs," Annals of Operations Research, Springer, vol. 224(1), pages 1-23, January.
  • Handle: RePEc:spr:annopr:v:224:y:2015:i:1:p:1-23:10.1007/s10479-012-1179-1
    DOI: 10.1007/s10479-012-1179-1
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    References listed on IDEAS

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    Cited by:

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    2. Fatemeh Moinian & Hamed Sabouhi & Jafar Hushmand & Ahmad Hallaj & Hiwa Khaledi & Mojtaba Mohammadpour, 2017. "Gas turbine preventive maintenance optimization using genetic algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(3), pages 594-601, September.
    3. M. S. Patel & A. A. Khalifa & M. S. Liew & Zahiraniza Mustaffa & Andrew Whyte, 2022. "Downtime cost analysis of offloading operations due to influence of partially standing waves in Malaysian waters and development of graphical user interface," Annals of Operations Research, Springer, vol. 315(2), pages 1263-1289, August.
    4. Nooshin Salari & Viliam Makis, 2020. "Joint maintenance and just-in-time spare parts provisioning policy for a multi-unit production system," Annals of Operations Research, Springer, vol. 287(1), pages 351-377, April.
    5. Yonit Barron, 2018. "Group maintenance policies for an R-out-of-N system with phase-type distribution," Annals of Operations Research, Springer, vol. 261(1), pages 79-105, February.

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