Maintenance optimization with duration-dependent costs

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 mit...

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Vydané v:	Annals of operations research Ročník 224; číslo 1; s. 1 - 23
Hlavní autori:	Bohlin, Markus, Wärja, Mathias
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Boston Springer US 01.01.2015 Springer Springer Nature B.V
Predmet:	Breakdown Breakdowns Business and Management Combinatorics Cost control Costs Economics Engineering research Gas pipelines Gas turbines Inclusions Integer programming Maintenance Maintenance costs Maintenance management Mathematical optimization Mathematical research Operations research Operations Research/Decision Theory Optimization Planning Preventive maintenance Stochastic analysis Studies Theory of Computation Turbines Water flooding Working hours Integer programming Multi-unit maintenance Maintenance optimization
ISSN:	0254-5330, 1572-9338, 1572-9338
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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.
AbstractList	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. 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. 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. Keywords Maintenance optimization * Integer programming * Multi-unit maintenance Issue Title: Optimization of Maintenance Activities - Models, Methods, and Applications 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.
Audience	Academic
Author	Bohlin, Markus Wärja, Mathias
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CitedBy_id	crossref_primary_10_1016_j_ress_2018_11_028 crossref_primary_10_1007_s10479_020_03845_9 crossref_primary_10_1016_j_psep_2017_02_015 crossref_primary_10_1007_s10479_019_03371_3 crossref_primary_10_1016_j_compchemeng_2023_108477 crossref_primary_10_1007_s10479_014_1543_4 crossref_primary_10_1007_s13198_017_0627_3 crossref_primary_10_1016_j_psep_2017_04_016 crossref_primary_10_1016_j_cie_2024_110460 crossref_primary_10_1007_s10479_017_2617_x crossref_primary_10_1016_j_jrtpm_2020_100177
Cites_doi	10.1016/S0098-1354(97)88493-1 10.1016/0951-8320(95)00076-3 10.1002/(SICI)1099-1638(199707)13:4<199::AID-QRE118>3.0.CO;2-A 10.1016/S0951-8320(03)00166-2 10.1057/jors.1985.202 10.1016/0377-2217(94)00260-J 10.1007/BF01194788 10.1016/0377-2217(91)90141-H 10.1016/j.ijpe.2008.09.012 10.1016/j.compind.2004.06.005 10.1016/j.ress.2007.03.036 10.1016/0377-2217(94)00363-7 10.1109/TPAS.1983.317771 10.1016/S0951-8320(02)00043-1 10.1016/S0377-2217(96)00320-7 10.1002/9780470451793 10.1016/S0377-2217(97)00319-6 10.1016/j.ress.2006.12.002 10.1111/j.1937-5956.1998.tb00128.x 10.1016/j.ress.2006.01.006 10.1016/S0951-8320(00)00047-8 10.1115/GT2009-59935 10.1016/S0377-2217(96)00316-5
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Snippet	High levels of availability and reliability are essential in many industries where production is subject to high costs due to downtime. Examples include the... Issue Title: Optimization of Maintenance Activities - Models, Methods, and Applications High levels of availability and reliability are essential in many...
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SubjectTerms	Breakdown Breakdowns Business and Management Combinatorics Cost control Costs Economics Engineering research Gas pipelines Gas turbines Inclusions Integer programming Maintenance Maintenance costs Maintenance management Mathematical optimization Mathematical research Operations research Operations Research/Decision Theory Optimization Planning Preventive maintenance Stochastic analysis Studies Theory of Computation Turbines Water flooding Working hours
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Title	Maintenance optimization with duration-dependent costs
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