Enhancing decision-making with q-complex Diophantine neutrosophic normal interval-valued sets for industrial robot selection

Abstract

Robots will increasingly be able to perform everyday tasks with ease, provided they are equipped with decision-making and planning capabilities that enable them to understand and execute these tasks effectively. This research applies a multiple-attribute decision-making (MADM) approach to solve complex decision-making problems, utilizing degrees of truth functions to manage data uncertainty. The proposed q-complex Diophantine neutrosophic normal interval-valued set (q-CDNNIVS) presents a comprehensive framework for managing uncertainty, especially in cases involving periodicity. The concept of q-CDNNIVS is introduced, which uses three independent truth degrees to evaluate information. We propose several averaging and geometric aggregation operators (AOs) and discuss their algebraic properties, such as distributivity, idempotency, and associativity. A MADM algorithm is designed to address decision-making challenges, and its applicability is demonstrated through a case study on industrial robot selection that considers factors such as reach, payload, flexibility, speed, and standardization. A comparative study with existing methods highlights the conservative nature of the proposed algorithm, where sensitivity analysis and validation further support its effectiveness. The results offer decision-makers a reliable tool for managing complex, inconsistent data in industrial applications. Additionally, the importance of the parameter q is highlighted, with a comparison analysis confirming the method's feasibility and practical application.