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Permanent link (DOI): https://doi.org/10.7939/R3JQ1F

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Spare parts provisioning decision support model for long lead time spares Open Access

Descriptions

Other title
Subject/Keyword
spares
montecarlo
genetic
optimization
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Aulakh, Amit
Supervisor and department
Zuo, Ming (Mechanical Engineering)
Examining committee member and department
AbouRizk, Simaan (Construction Engineering and Management )
Obaia, Khaled (Syncrude Canada Ltd.)
Moussa, Walied (Mechanical Engineering)
Department
Department of Mechanical Engineering
Specialization

Date accepted
2011-04-11T17:13:21Z
Graduation date
2011-06
Degree
Master of Science
Degree level
Master's
Abstract
Large corporations have a significant amount of working capital tied into the acquisition and storage of spare parts. In the industry, spare parts inventory policies and strategies are often developed in isolation from reliability centered maintenance practices – this results in significant wasteful direct and indirect cost attached to spare parts management for the equipment operator. This report will focus on developing a methodology for minimizing lifecycle indirect and direct cost that comes from storing long lead time spares. A combined Monte-Carlo and Genetic Algorithm based optimization approach to finding the optimal spare parts storage strategy is proposed. In this study, the indirect and direct cost of having a spare part in the storage facility will be balanced against the cost of lost opportunity that results from decreased availability - a consequence of not having the required spare part available when an equipment failure event occurs. The results of this study present the benefits of optimizing long lead time spares through a joint Monte-Carlo & Genetic Algorithm based approach.
Language
English
DOI
doi:10.7939/R3JQ1F
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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File title: Spare Parts Provisioning Decision Support Model for Long Lead Time Spares
File author: Amit S. Aulakh
File language: en-US
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