Indeterminacy Fuzzy TOPSIS Framework for Unmanned Stealth Aircraft Selection

Abstract

Decision-making problems in defense procurement are inherently complex due to multiple conflicting criteria, subjective expert judgments, and pervasive uncertainty. To address these challenges, this study proposes a comprehensive indeterminacy fuzzy TOPSIS (IFS–TOPSIS) framework for the selection of unmanned stealth aircraft in strategic national defense missions. Linguistic evaluations provided by multiple decision makers are modeled using indeterminacy fuzzy sets, allowing the simultaneous representation of truth, indeterminacy, and falsity degrees. Decision-maker importance and criterion weights are determined through indeterminacy fuzzy aggregation, while alternative performances are evaluated via a distance-based ideal solution approach.
The proposed framework is applied to a realistic case study involving three unmanned stealth aircraft alternatives evaluated against five key criteria: stealth capability, payload capacity, communication effectiveness, survivability, and affordability. The results identify the second alternative as the most suitable option, exhibiting the closest proximity to the positive ideal solution. Sensitivity analysis confirms the robustness of the ranking under varying criterion weight scenarios, and a comparative analysis demonstrates the superior discrimination capability of the proposed method over classical TOPSIS. The findings indicate that IFS–TOPSIS provides a robust, transparent, and doctrine-aligned decision-support tool for defense system selection under uncertainty.