A New Decision Making Model based on Factor Analysis (FA), F-ANP, and F-ARAS for Selecting and Ranking Maintenance Strategies

Habib Farajpoor Khanaposhtani, Mohsen Shafiei Nikabadi, Hossein Eftekhari and Alireza Aslani
Additional contact information
Habib Farajpoor Khanaposhtani: Department of Industrial Management, University of Semnan, Semnan, Iran
Mohsen Shafiei Nikabadi: Department of Industrial Management, University of Semnan, Semnan, Iran
Hossein Eftekhari: Department of Technology and Innovation Management, University of Tehran, Tehran, Iran
Alireza Aslani: Department of Technology and Innovation Management, University of Tehran, Tehran, Iran

International Journal of Business Analytics (IJBAN), 2016, vol. 3, issue 4, 41-63

Abstract: Today, companies have admired that maintenance is a profitable commercial element. Therefore, its role in modern manufacturing systems has become more important. Maintenance plays a vital role in achieving organizational goals and improving indicators such as reliability, accessibility, decreasing equipment downtime, products quality, risk mitigation, productivity increase, equipment safety, etc. In this regard, maintenance and its strategies have found special importance in industry. As a result, the main aim of this research is to select the best maintenance strategy by using Fuzzy ARAS and Fuzzy ANP techniques in oil industry (Tehran Oil Refinery – Shahr-e-Ray). Since many variables (i.e. security, cost, added – value, etc.) are effective in selecting a maintenance strategy, these variables are initially identified by reviewing the relevant literature and maintenance experts' opinions and then the best maintenance strategy is selected by using Fuzzy ARAS.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJBAN.2016100103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jban00:v:3:y:2016:i:4:p:41-63

Access Statistics for this article

International Journal of Business Analytics (IJBAN) is currently edited by John Wang

More articles in International Journal of Business Analytics (IJBAN) from IGI Global
Bibliographic data for series maintained by Journal Editor ().