An enhance vehicle selection problem for an effective transportation system: A logarithmic-based distance measure approaches via q-Rung Orthopair Fuzzy Information

Abstract

An effective transportation system is fundamental for socioeconomic development, public safety, andenvironmental sustainability. A critical component of such a system is the Vehicle Selection Problem (VSP), which isinherently a Complex Decision-Making (CDM) problem due to conflicting and uncertain criteria such as fuel efficiency,purchase cost, maintenance cost, and warranty. To address this complexity, this study develops two novel logarithmic-based distance measures within the q-Rung Orthopair Fuzzy (q-ROF) framework. The proposed distance metricsincorporate membership, non-membership, and hesitation degrees, along with the cardinality of the universe of discuss,ensuring a more comprehensive representation of uncertainty. Their metric properties are rigorously proven, and theyare integrated with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve a VSP. Thecase study involving seven vehicle brands and seven evaluation criteria, assessed by domain experts, demonstrates theeffectiveness of the proposed approach. Comparative analysis with existing logarithmic-based distance measures showsthat the new methods provide superior accuracy, stability, and discrimination ability in ranking alternatives. The findingshighlight the practical significance of the proposed q-ROF distance measures, offering a robust decision-support tool forvehicle selection and other CDM scenarios under uncertainty.