Toward robust decision-making under multiple evaluation scenarios with a novel fuzzy ranking approach: green supplier selection study case

计算机科学 排名(信息检索) 选择(遗传算法) 供应商评价 模糊逻辑 机器学习 人工智能 运筹学 数据挖掘 业务 供应链管理 供应链 数学 营销
作者
Jakub Więckowski,Jarosław Wątróbski,Wojciech Sałabun
出处
期刊:Artificial Intelligence Review [Springer Nature]
卷期号:58 (1) 被引量:9
标识
DOI:10.1007/s10462-024-11006-8
摘要

Abstract In the evolving field of decision-making, the continuous advancement of technologies and methodologies drives the pursuit of more reliable tools. Decision support systems (DSS) provide information to make informed choices and multi-criteria decision analysis (MCDA) methods are an important component of defining decision models. Despite their usefulness, there are still challenges in making robust decisions in dynamic environments due to the varying performance of different MCDA methods. It creates space for the development of techniques to aggregate conflicting results. This paper introduces a fuzzy ranking approach for aggregating results from multi-criteria assessments, specifically addressing the limitations of current result aggregation techniques. Unlike conventional methods, the proposed approach represents rankings as fuzzy sets, providing detailed insights into the robustness of decision problems. The study uses green supplier selection as a case study, examining the performance of the introduced approach and the robustness of its recommendations within the sustainability field. This study offers a new methodology for aggregating results from multiple evaluation scenarios, thereby enhancing decision-maker awareness and robustness. Through comparative analysis with traditional compromise solution methods, this paper highlights the limitations of current approaches and indicates the advantages of adopting fuzzy ranking aggregation. This study significantly advances the field of decision-making by enhancing the understanding of the stability of decision outcomes.

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