Condition-based maintenance policies under imperfect maintenance at scheduled and unscheduled opportunities

Research output: Contribution to journalArticleAcademicpeer-review

5 Downloads (Pure)

Abstract

Motivated by the cost savings that can be obtained by sharing resources in a network context, we consider a stylized, yet representative, model for the coordination of maintenance and service logistics for a geographic network of assets. Capital assets, such as wind turbines in a wind park, require maintenance throughout their long lifetimes. Two types of preventive maintenance are considered: planned maintenance at periodic, scheduled opportunities, and opportunistic maintenance at unscheduled opportunities. The latter type of maintenance arises due to the network context: When an asset in the network fails, this constitutes an opportunity for preventive maintenance for the other assets in the network. So as to increase the realism of the model at hand and its applicability to various sectors, we consider the option of not-deferring and of deferring planned maintenance after the occurrence of opportunistic maintenance. We also assume that preventive maintenance may not always restore the condition of the system to ‘as good as new.’ By formulating this problem as a semi-Markov decision process, we characterize the optimal policy as a control limit policy (depending on the remaining time until the next planned maintenance) that indicates on the one hand when it is optimal to perform preventive maintenance and on the other hand when maintenance resources should be shared if an opportunity in the network arises. In order to facilitate managerial insights on the effect of each parameter on the cost, we provide a closed-form expression for the long-run rate of cost for any given control limit policy (depending on the remaining time until the next planned maintenance) and compare the costs (under the optimal policy) to those of suboptimal policies that neglect the opportunity for resource sharing. We illustrate our findings using data from the wind energy industry.

Original languageEnglish
Pages (from-to)269-308
Number of pages40
JournalQueueing Systems
Volume93
Issue number3-4
Early online date24 Aug 2019
DOIs
Publication statusPublished - 1 Dec 2019

Fingerprint

Condition-based Maintenance
Maintenance Policy
Imperfect
Maintenance
Preventive Maintenance
Preventive maintenance
Resource Sharing
Costs
Optimal Policy
Condition-based maintenance
Semi-Markov Decision Process
Wind Energy
Wind Turbine
Long-run
Wind turbines
Wind power
Logistics
Lifetime
Sector
Closed-form

Keywords

  • Condition-based maintenance
  • Markov decision processes
  • Opportunistic maintenance

Cite this

@article{db58855ea84946dfb88991ff63ac5418,
title = "Condition-based maintenance policies under imperfect maintenance at scheduled and unscheduled opportunities",
abstract = "Motivated by the cost savings that can be obtained by sharing resources in a network context, we consider a stylized, yet representative, model for the coordination of maintenance and service logistics for a geographic network of assets. Capital assets, such as wind turbines in a wind park, require maintenance throughout their long lifetimes. Two types of preventive maintenance are considered: planned maintenance at periodic, scheduled opportunities, and opportunistic maintenance at unscheduled opportunities. The latter type of maintenance arises due to the network context: When an asset in the network fails, this constitutes an opportunity for preventive maintenance for the other assets in the network. So as to increase the realism of the model at hand and its applicability to various sectors, we consider the option of not-deferring and of deferring planned maintenance after the occurrence of opportunistic maintenance. We also assume that preventive maintenance may not always restore the condition of the system to ‘as good as new.’ By formulating this problem as a semi-Markov decision process, we characterize the optimal policy as a control limit policy (depending on the remaining time until the next planned maintenance) that indicates on the one hand when it is optimal to perform preventive maintenance and on the other hand when maintenance resources should be shared if an opportunity in the network arises. In order to facilitate managerial insights on the effect of each parameter on the cost, we provide a closed-form expression for the long-run rate of cost for any given control limit policy (depending on the remaining time until the next planned maintenance) and compare the costs (under the optimal policy) to those of suboptimal policies that neglect the opportunity for resource sharing. We illustrate our findings using data from the wind energy industry.",
keywords = "Condition-based maintenance, Markov decision processes, Opportunistic maintenance",
author = "Collin Drent and Stella Kapodistria and J.A.C. Resing",
year = "2019",
month = "12",
day = "1",
doi = "10.1007/s11134-019-09627-w",
language = "English",
volume = "93",
pages = "269--308",
journal = "Queueing Systems: Theory and Applications",
issn = "0257-0130",
publisher = "Springer",
number = "3-4",

}

Condition-based maintenance policies under imperfect maintenance at scheduled and unscheduled opportunities. / Drent, Collin; Kapodistria, Stella (Corresponding author); Resing, J.A.C.

In: Queueing Systems, Vol. 93, No. 3-4, 01.12.2019, p. 269-308.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Condition-based maintenance policies under imperfect maintenance at scheduled and unscheduled opportunities

AU - Drent, Collin

AU - Kapodistria, Stella

AU - Resing, J.A.C.

PY - 2019/12/1

Y1 - 2019/12/1

N2 - Motivated by the cost savings that can be obtained by sharing resources in a network context, we consider a stylized, yet representative, model for the coordination of maintenance and service logistics for a geographic network of assets. Capital assets, such as wind turbines in a wind park, require maintenance throughout their long lifetimes. Two types of preventive maintenance are considered: planned maintenance at periodic, scheduled opportunities, and opportunistic maintenance at unscheduled opportunities. The latter type of maintenance arises due to the network context: When an asset in the network fails, this constitutes an opportunity for preventive maintenance for the other assets in the network. So as to increase the realism of the model at hand and its applicability to various sectors, we consider the option of not-deferring and of deferring planned maintenance after the occurrence of opportunistic maintenance. We also assume that preventive maintenance may not always restore the condition of the system to ‘as good as new.’ By formulating this problem as a semi-Markov decision process, we characterize the optimal policy as a control limit policy (depending on the remaining time until the next planned maintenance) that indicates on the one hand when it is optimal to perform preventive maintenance and on the other hand when maintenance resources should be shared if an opportunity in the network arises. In order to facilitate managerial insights on the effect of each parameter on the cost, we provide a closed-form expression for the long-run rate of cost for any given control limit policy (depending on the remaining time until the next planned maintenance) and compare the costs (under the optimal policy) to those of suboptimal policies that neglect the opportunity for resource sharing. We illustrate our findings using data from the wind energy industry.

AB - Motivated by the cost savings that can be obtained by sharing resources in a network context, we consider a stylized, yet representative, model for the coordination of maintenance and service logistics for a geographic network of assets. Capital assets, such as wind turbines in a wind park, require maintenance throughout their long lifetimes. Two types of preventive maintenance are considered: planned maintenance at periodic, scheduled opportunities, and opportunistic maintenance at unscheduled opportunities. The latter type of maintenance arises due to the network context: When an asset in the network fails, this constitutes an opportunity for preventive maintenance for the other assets in the network. So as to increase the realism of the model at hand and its applicability to various sectors, we consider the option of not-deferring and of deferring planned maintenance after the occurrence of opportunistic maintenance. We also assume that preventive maintenance may not always restore the condition of the system to ‘as good as new.’ By formulating this problem as a semi-Markov decision process, we characterize the optimal policy as a control limit policy (depending on the remaining time until the next planned maintenance) that indicates on the one hand when it is optimal to perform preventive maintenance and on the other hand when maintenance resources should be shared if an opportunity in the network arises. In order to facilitate managerial insights on the effect of each parameter on the cost, we provide a closed-form expression for the long-run rate of cost for any given control limit policy (depending on the remaining time until the next planned maintenance) and compare the costs (under the optimal policy) to those of suboptimal policies that neglect the opportunity for resource sharing. We illustrate our findings using data from the wind energy industry.

KW - Condition-based maintenance

KW - Markov decision processes

KW - Opportunistic maintenance

UR - http://www.scopus.com/inward/record.url?scp=85071485000&partnerID=8YFLogxK

U2 - 10.1007/s11134-019-09627-w

DO - 10.1007/s11134-019-09627-w

M3 - Article

AN - SCOPUS:85071485000

VL - 93

SP - 269

EP - 308

JO - Queueing Systems: Theory and Applications

JF - Queueing Systems: Theory and Applications

SN - 0257-0130

IS - 3-4

ER -