Li et al 2013b - Revision history

Scipediacontent at 15:26, 21 January 2021

2021-01-21T15:26:47Z

Scipediacontent: Scipediacontent moved page Draft Content 936058738 to Li et al 2013b

2020-10-14T14:30:56Z

Scipediacontent moved page Draft Content 936058738 to Li et al 2013b

Scipediacontent: Created page with " == Abstract == This paper presents a group maintenance scheduling case study for a water distribution network. This water pipeline network presents the challenge of maintain..."

2020-10-14T14:30:53Z

Created page with " == Abstract == This paper presents a group maintenance scheduling case study for a water distribution network. This water pipeline network presents the challenge of maintain..."

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== Abstract ==

This paper presents a group maintenance scheduling case study for a water distribution network. This water pipeline network presents the challenge of maintaining aging pipelines with the associated increases in annual maintenance costs. The case study focuses on developing an effective pipeline replacement planning for the water utility. Replacement planning involves large capital commitment and can be difficult as it needs to balance various replacement needs under limited budgets. A Maintenance Grouping Optimization (MGO) model based on a modified genetic algorithm was utilized to develop an optimum group maintenance schedule over a 20 year cycle. An adjacent geographical distribution of pipelines was used as a grouping criterion to control the searching space of the MGO model through a Judgment Matrix. Based on the optimum group maintenance schedule, the total cost was effectively reduced compared with the schedules without grouping maintenance jobs. This optimum result can be used as a guidance to optimize the current maintenance plan for the water utility.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_936058738-beopen1029-5732-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://eprints.qut.edu.au/44018/2/44018.pdf http://eprints.qut.edu.au/44018/2/44018.pdf]

← Older revision		Revision as of 15:26, 21 January 2021
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	This paper presents a group maintenance scheduling case study for a water distribution network. This water pipeline network presents the challenge of maintaining aging pipelines with the associated increases in annual maintenance costs. The case study focuses on developing an effective pipeline replacement planning for the water utility. Replacement planning involves large capital commitment and can be difficult as it needs to balance various replacement needs under limited budgets. A Maintenance Grouping Optimization (MGO) model based on a modified genetic algorithm was utilized to develop an optimum group maintenance schedule over a 20 year cycle. An adjacent geographical distribution of pipelines was used as a grouping criterion to control the searching space of the MGO model through a Judgment Matrix. Based on the optimum group maintenance schedule, the total cost was effectively reduced compared with the schedules without grouping maintenance jobs. This optimum result can be used as a guidance to optimize the current maintenance plan for the water utility.		This paper presents a group maintenance scheduling case study for a water distribution network. This water pipeline network presents the challenge of maintaining aging pipelines with the associated increases in annual maintenance costs. The case study focuses on developing an effective pipeline replacement planning for the water utility. Replacement planning involves large capital commitment and can be difficult as it needs to balance various replacement needs under limited budgets. A Maintenance Grouping Optimization (MGO) model based on a modified genetic algorithm was utilized to develop an optimum group maintenance schedule over a 20 year cycle. An adjacent geographical distribution of pipelines was used as a grouping criterion to control the searching space of the MGO model through a Judgment Matrix. Based on the optimum group maintenance schedule, the total cost was effectively reduced compared with the schedules without grouping maintenance jobs. This optimum result can be used as a guidance to optimize the current maintenance plan for the water utility.
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−	~~Document type: Part of book or chapter of book~~
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−	~~== Full document ==~~
−	~~<pdf>Media:Draft_Content_936058738-beopen1029-5732-document.pdf</pdf>~~


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	* [http://eprints.qut.edu.au/44018/2/44018.pdf http://eprints.qut.edu.au/44018/2/44018.pdf]		* [http://eprints.qut.edu.au/44018/2/44018.pdf http://eprints.qut.edu.au/44018/2/44018.pdf]
		+
		+	* [http://link.springer.com/content/pdf/10.1007/978-1-4471-4993-4_15 http://link.springer.com/content/pdf/10.1007/978-1-4471-4993-4_15],
		+	: [http://dx.doi.org/10.1007/978-1-4471-4993-4_15 http://dx.doi.org/10.1007/978-1-4471-4993-4_15]
		+
		+	* [https://link.springer.com/chapter/10.1007%2F978-1-4471-4993-4_15 https://link.springer.com/chapter/10.1007%2F978-1-4471-4993-4_15],
		+	: [https://eprints.qut.edu.au/44018 https://eprints.qut.edu.au/44018],
		+	: [https://www.scipedia.com/public/Li_et_al_2013b https://www.scipedia.com/public/Li_et_al_2013b],
		+	: [https://rd.springer.com/chapter/10.1007%2F978-1-4471-4993-4_15 https://rd.springer.com/chapter/10.1007%2F978-1-4471-4993-4_15],
		+	: [https://core.ac.uk/display/146935652 https://core.ac.uk/display/146935652],
		+	: [https://academic.microsoft.com/#/detail/1484437626 https://academic.microsoft.com/#/detail/1484437626]

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