A distance-based decision model in interval-valued hesitant fuzzy setting for industrial selection problems

In a classical Group Decision-Making (GDM) analysis, the ratings of potential alternatives and the weights of criteria or Decision Makers (DMs) are known precisely. However, for dealing with uncertain situations, the DMs can de ne their opinions in linguistic variables based on fuzzy sets in industrial selection problems. In this respect, an Interval-Valued Hesitant Fuzzy Set (IVHFS) is the suitable and capable theory that could help the DMs with assigning some interval-valued membership degrees to a candidate or option under a set. This paper introduces a novel Interval-Valued Hesitant Fuzzy Distance-Based Group Decision (IVHF-DBGD) model by a group of DMs, in which the best potential alternative can be appraised and selected among the con icting criteria. In the proposed IVHF-DBGD model, the weight of each criterion is determined by extended IVHF-entropy method along with the DMs' opinions about the criteria's weights. Also, the weight of each DM is computed by a new IVHF-order preference method with the relative closeness. Moreover, this paper introduces a new IVHF-collective index to discriminate among potential alternatives in the selection process. Finally, the computational results with a robot selection from the literature indicate that the proposed IVHF-DBGD model is the suitable group decision-making tool for the industrial selection problems

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

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