maintenance policy selection considering resilience engineering by a new interval-valued fuzzy decision model under uncertain conditions

Dierent maintenance policies including preventive maintenance (pvm) and predictive maintenance (pdm) have been introduced to enhance the operation of systems. maintenance professional experts have faced numerous challenges in distinguishing between proper maintenance policies among which causes of failure, accessibility, and 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 to select a maintenance policy based on order inclination with comparability to ideal solutions through monte carlo simulation. this paper introduces novel separation measures and a new ivf-distinguished index based on possibilistic statistical concepts (pscs) so that maintenance decision-makers (dms) feel aided in ranking maintenance policy candidates. also, resilience engineering (re) factors are considered based on conventional evaluation criteria. finally, the steps of the proposed ivf model-based pscs are applied to survey a real case in the manufacturing industry. results of the presented model are compared with those existing in the recent literature and the outcome could help maintenance personnel in identifying the best policy systematically.