Document Type : Article
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
Faculty of Built Environment, University of New South Wales, Sydney, Australia
Different maintenance policies, including preventive maintenance and predictive maintenance, are introduced to enhance the execution of systems. Maintenance professional experts have faced numerous challenges with distinguishing the proper maintenance policy, among which causes of failure, accessibility, and the capability of maintenance should be regarded seriously. Moreover, most organizations do not have a deliberate and compelling model for evaluating maintenance policies under uncertainty to deal with real-world conditions. The aim of this paper is to introduce a new interval-valued fuzzy (IVF) decision model for the selection of maintenance policy based on order inclination with comparability to ideal solutions by Monte Carlo simulation. This paper introduces novel separation measures and a new IVF-distinguish index via possibilistic statistical concepts (PSCs) which can assist maintenance decision makers to rank maintenance policy candidates. Also, resilience engineering (RE) factors are considered along with conventional evaluation criteria. Finally, the steps of the proposed IVF model-based PSCs are applied to survey a real case in manufacturing industry. Results of the presented model are compared with the recent literature and could help maintenance personnel in identifying the best policy systematically.