Premium
Maintenance strategy selection with risky evaluations using RAHP
Author(s) -
Seiti Hamidreza,
Tagipour Razieh,
Hafezalkotob Ashkan,
Asgari Farhad
Publication year - 2017
Publication title -
journal of multi‐criteria decision analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.462
H-Index - 47
eISSN - 1099-1360
pISSN - 1057-9214
DOI - 10.1002/mcda.1618
Subject(s) - preventive maintenance , quality (philosophy) , reliability (semiconductor) , risk analysis (engineering) , process (computing) , task (project management) , selection (genetic algorithm) , computer science , reliability engineering , maintenance actions , operations research , engineering , business , systems engineering , artificial intelligence , philosophy , power (physics) , physics , epistemology , quantum mechanics , operating system
In recent years, maintenance, especially its preventive type, has been seen as an effective and considerable factor in improving the functions of machines. Maintenance plays a vital role in organizations by keeping and increasing the reliability, accessibility, the quality of products, risk mitigation, return improvement, and safety. An effective maintenance programme can be realized by implementing a proper maintenance strategy. Therefore, maintenance and its strategies have a special status in industries. However, selecting a proper maintenance strategy has always been a complex process because maintenance is a nonrepetitive task. Likewise, the lack of failure records and constant changes in the conditions of machines makes it further complicated. Hence, the decision‐making also depends on experts' opinions and because some kind of risk and uncertainty are always there in experts evaluations, the reliability of evaluations is questionable. The present study was aimed to develop a risk‐based model grounded on the analytical hierarchical process, namely, RAHP, to meet this need of maintenance. A case study from a steel rolling company was considered to evaluate the effectiveness of this model.