Journal
SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL
Volume 92, Issue 1, Pages 3-18Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0037549715616686
Keywords
maintenance policy; fuzzy analytic hierarchy process; fuzzy multiple-criteria decision-making approach; simulation
Funding
- University of Tehran [8106013/1/17]
- College of Engineering, University of Tehran, Tehran, Iran
- Iran National Science Foundation [94002128]
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Selecting a proper maintenance strategy in an attempt to preclude failures is of critical significance in system engineering due to its fallbacks in the safety and economics of plants operation. This process is a typical multiple-criteria decision-making (MCDM) problem that involves both tangible and intangible parameters that are often in conflict with each other. In this paper, an integrated analytic hierarchy process (AHP)-fuzzy MCDM approach is proposed to perform a comprehensive comparison between different maintenance policies. For this purpose, various criteria are taken into account that are different in nature, as some give a crisp value obtained from simulation, some are defined in linguistic terms based on experts' opinions and some are in the form of triangular fuzzy numbers. The AHP method is used to determine the importance weights of the criteria. Subsequently, a distance-based fuzzy MCDM approach is employed to rank different maintenance policies and select the most appropriate one. Moreover, the fuzzy technique for the order of prioritization by similarity to ideal solution is used for verification of the proposed integrated approach. Lastly, the impact of each criterion on the rankings is examined. Four commonly implemented maintenance policies, namely condition-based, time-based, failure-based and opportunistic, are considered in this study. Also, a real-world example is presented to demonstrate the applicability of the proposed approach. The most significant feature of this approach lies in its capability in incorporating data in the forms of linguistic variables, triangular fuzzy numbers and crisp numbers into the evaluation process.
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