A Hybrid IRN-Based BWM–COPRAS Framework for Electro-Optic System Selection in UAVs with Heterogeneous Evaluations

The rapid proliferation of unmanned aerial vehicles (UAVs) across diverse civil and military domains has heightened the need for careful payload selection due to inherent weight and capacity constraints. Among these payloads, electro-optic (EO) systems are of paramount importance, offering capabilities such as real-time imaging, surveillance, reconnaissance, and precision targeting. Given the increasing diversity and complexity of EO systems, selecting the most appropriate system for Medium-Altitude Long-Endurance (MALE) UAVs has emerged as a multi-dimensional decision-making challenge that requires the integration of both technical specifications and expert evaluations. This study proposes a novel decision-making framework that integrates the Fuzzy Best-Worst Method (BWM) and the Complex Proportional Assessment (COPRAS) method under the Interval Rough Number (IRN) theory. The approach addresses the uncertainty and heterogeneity in expert judgments and objective data, thereby providing a more nuanced and robust evaluation mechanism. The proposed hybrid framework offers methodological contributions by extending traditional MCDM techniques to heterogeneous decision environments through the use of IRNs. Empirical results demonstrate the model’s reliability and consistency, with sensitivity and comparative analyses validating its robustness across multiple scenarios. The findings provide valuable insights for decision-makers and system developers in the aerospace and defense industries, offering a structured and adaptable tool for selecting EO systems in MALE-class UAV applications.

کلیدواژه ها:

Fuzzy BWM ، Rough COPRAS ، Electro-Optic System ، Unmanned aerial vehicle ، Heterogeneous Information