Group maintenance scheduling : a case study for a pipeline network
Li, Fengfeng, Ma, Lin, Sun, Yong, & Mathew, Joseph (2011) Group maintenance scheduling : a case study for a pipeline network. In Proceedings of the 6th World Congress on Engineering Asset Management, WCEAM, Duke Energy Center, Cincinnati, Ohio, pp. 1-9.
This paper presents a group maintenance scheduling case study for a water distributed 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 maintenance plan for the water utility. Current replacement planning is difficult as it needs to balance the 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. The 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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|Item Type:||Conference Paper|
|Keywords:||Group Maintenance Scheduling,, Genetic Algorithm, Maintenance Grouping Optimization (MGO), Judgment Matrix, Water Pipeline|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Infrastructure Engineering and Asset Management (090505)|
|Divisions:||Current > Research Centres > CRC Integrated Engineering Asset Management (CIEAM)|
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2011 [please consult the author]|
|Deposited On:||18 Aug 2011 10:23|
|Last Modified:||26 Sep 2012 19:19|
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