A fuzzy DEMATEL-ANP-VIKOR analytical model for maintenance strategy selection of safety critical assets

Abstract

In most industries, such as aerospace, manufacturing, transport and energy sectors, maintenance plays a vital role in improving the performance of safety critical equipment and facilities. It also helps industries achieve the largest possible efficiency, ensure workplace and environmental safety, and reduce unnecessary breakdowns and costs. Therefore, it is crucial for industries to adopt an optimal maintenance strategy for their critical systems and infrastructure. In this study, we aim to propose a novel analytical multi-criteria decision-making (MCDM) methodology for selecting the most suitable maintenance strategy in distillation units of oil refinery plants. The alternative maintenance strategies include run-to-failure (RTF), preventive maintenance (PM), condition-based maintenance (CBM), and reliability centered maintenance (RCM), which are evaluated with respect to 12 sub-criteria in three categories of economical, safety, and sustainability issues. The MCDM methodology consists of a DEMATEL-based analytic network process (ANP) method to determine the importance weights of decision criteria and a VIKOR method to rank the maintenance strategies. Also, interval type-2 fuzzy sets are used to capture uncertainty in experts’ individual judgments. Finally, a real case study is provided to show the applicability of the proposed methodology to an oil refinery plant. The results show that, thanks to advances in degradation modeling, sensor technology, and data analytics platforms, the RCM and CBM are the superior maintenance strategy for crude oil distillation systems.