Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/78031
Title: A fuzzy-AHP-based decision support system for maintenance strategy selection in facility management
Authors: Pun, KP 
Tsang, YP 
Choy, KL 
Tang, V 
Lam, HY 
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: PICMET 2017 - Portland International Conference on Management of Engineering and Technology : Technology Management for the Interconnected World, Proceedings, 9-13 July 2017, p. 1-7 How to cite?
Abstract: In facility management, maintenance plays an important role in the sustainable development of buildings, involving safety, technical, economic and environmental aspects. An effective building maintenance strategy is critical in improving equipment reliability and availability, maintaining a comfortable environment, enhancing energy efficiency and minimizing the life-cycle cost of the building. Hence, a company's competitiveness and profitability can be improved significantly if unintended failure can be avoided. However, in the current maintenance process, maintenance knowledge and expertise are usually very subjective while the conventional approach is not sufficiently systematical to explain the judgement and assessment criteria. In addition, even though applications of optimization approaches in the maintenance process have been considered, exploring acceptable reasoning for vague information, such as costs, risk assessment and expert feedback is lacking, in the consideration of different maintenance strategies. Therefore, in this paper, a fuzzy-AHP-based decision support system (FADSS) is proposed for assisting in the multi-criteria decision-making process so as to determine the most cost-effective and efficient maintenance strategy. To evaluate the proposed system, a case study is conducted regarding building facilities maintenance. It is found that, through formulating the most suitable strategy, the work efficiency can be improved, and the maintenance costs can be minimized.
URI: http://hdl.handle.net/10397/78031
ISBN: 9.78189E+12
DOI: 10.23919/PICMET.2017.8125300
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