A novel SCDM algorithm with offset centroid-driven weight adaptation and its application to appearance design of automotive steering wheels

Abstract

This paper introduces a novel group single-criteria decision-making algorithm that addresses the challenges from personal biases. The algorithm utilizes offset centroid-driven weight adaptation to enhance the fairness and reliability of decisions, providing robust decision-making. The core steps of the new algorithm include splitting the evaluation dataset, calculating the offset centroid, introducing dynamic adjustment coefficients, constructing a comparison matrix, and calculating the weighted mean. To empirically validate the effectiveness and applicability of the proposed algorithm, we conducted a case study involving five appearance design alternatives for automotive steering wheels, and the numerical results demonstrate the algorithm’s substantial improvements on group decision-making outcomes. Remarkably, the algorithm not only facilitates the flourishing of a fair decision-making environment but also can effectively handles biased decision-making scenarios and mitigate the impact of unfairness. By utilizing this algorithm, decision-makers can alleviate individual biases and enable fairer and more reliable decision-making processes. Consequently, this algorithm introduces a novel approach for tackling complex decision problems and exhibits promising prospects for practical applications. Its versatility renders it highly valuable in diverse decision-making processes, empowering decision-makers to achieve fairness and precision in their choices.