利用新型模糊排序法实现多重评估情景下的稳健决策:绿色供应商选择研究案例

IF 10.7 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Review Pub Date : 2024-11-04 DOI:10.1007/s10462-024-11006-8
Jakub Więckowski, Jarosław Wątróbski, Wojciech Sałabun
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引用次数: 0

摘要

在不断发展的决策领域,技术和方法的不断进步推动着人们对更可靠工具的追求。决策支持系统(DSS)提供了做出明智选择的信息,而多标准决策分析(MCDA)方法则是定义决策模型的重要组成部分。尽管这些方法非常有用,但由于不同 MCDA 方法的性能各不相同,在动态环境中做出稳健决策仍面临挑战。这为开发汇总相互冲突结果的技术创造了空间。本文介绍了一种用于汇总多标准评估结果的模糊排序方法,特别解决了当前结果汇总技术的局限性。与传统方法不同的是,所提出的方法将排序表示为模糊集,为决策问题的稳健性提供了详细的见解。本研究以绿色供应商选择为案例,考察了所引入方法的性能及其在可持续发展领域所提建议的稳健性。本研究提供了一种新方法,用于汇总多个评估方案的结果,从而提高决策者的认识和稳健性。通过与传统折中方案方法的对比分析,本文强调了当前方法的局限性,并指出了采用模糊排序聚合法的优势。这项研究通过加强对决策结果稳定性的理解,极大地推动了决策领域的发展。
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Toward robust decision-making under multiple evaluation scenarios with a novel fuzzy ranking approach: green supplier selection study case

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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来源期刊
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
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