Cost Minimization of Aircraft Critical Components for Planning and Maintenance Requirements

Spare parts management of the aircrafts is a part of maintenance planning that requires effective and efficient planning in order to reduce aircraft downtime in maintenance processes. Spare parts unavailability during maintenance is one of the factors that affects operational availability of the air...

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Published inInternational Journal of Integrated Engineering Vol. 12; no. 7
Main Authors Abdol Rahim, Fatin Hamizah, Mohd Noor, Norliza, Abdullah, Haslaile, Annanurov, Bayram
Format Journal Article
LanguageEnglish
Published 01.01.2020
Online AccessGet full text
ISSN2229-838X
2600-7916
2600-7916
DOI10.30880/ijie.2020.12.07.005

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Abstract Spare parts management of the aircrafts is a part of maintenance planning that requires effective and efficient planning in order to reduce aircraft downtime in maintenance processes. Spare parts unavailability during maintenance is one of the factors that affects operational availability of the aircrafts fleet. This study focused on identifying the current problem affecting operational availability of the aircrafts fleet, developing an optimization model for cost minimization of spare parts and assessing the impact on spare parts availability using the cost minimization optimization model. Data analysis using machine learning techniques in WEKA was used to identify the current problem that is affecting aircrafts fleet operational availability. Then, Linear Integer Programming (LIP) and Goal Programming (GP) methods were used to develop the cost minimization optimization model by considering the current stock level of the critical components and the budget constrain. From this study, we discovered that spare parts unavailability during maintenance was the biggest contributing factor to the operational availability of the aircrafts fleet. Besides that, the cost minimization optimization model developed in the research had produced optimum level of inventory for the critical components with minimum cost. Spare parts availability for the aircrafts fleet had been improved with the application of the model which directly maximizes the operational availability of the aircraft fleet.
AbstractList Spare parts management of the aircrafts is a part of maintenance planning that requires effective and efficient planning in order to reduce aircraft downtime in maintenance processes. Spare parts unavailability during maintenance is one of the factors that affects operational availability of the aircrafts fleet. This study focused on identifying the current problem affecting operational availability of the aircrafts fleet, developing an optimization model for cost minimization of spare parts and assessing the impact on spare parts availability using the cost minimization optimization model. Data analysis using machine learning techniques in WEKA was used to identify the current problem that is affecting aircrafts fleet operational availability. Then, Linear Integer Programming (LIP) and Goal Programming (GP) methods were used to develop the cost minimization optimization model by considering the current stock level of the critical components and the budget constrain. From this study, we discovered that spare parts unavailability during maintenance was the biggest contributing factor to the operational availability of the aircrafts fleet. Besides that, the cost minimization optimization model developed in the research had produced optimum level of inventory for the critical components with minimum cost. Spare parts availability for the aircrafts fleet had been improved with the application of the model which directly maximizes the operational availability of the aircraft fleet.
Author Mohd Noor, Norliza
Annanurov, Bayram
Abdol Rahim, Fatin Hamizah
Abdullah, Haslaile
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CorporateAuthor Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia (UTM), Jalan Sultan Yahaya Petra, Kuala Lumpur, 54100, MALAYSIA
Paragon International University, No. 8 St. 315, Boeng Kak 1, Tuol Kork, Phnom Penh, 12151, CAMBODIA
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