Picture fuzzy complex proportional assessment approach with step-wise weight assessment ratio analysis and criteria importance through intercriteria correlation

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Engineering Applications of Artificial Intelligence Pub Date : 2024-11-04 DOI:10.1016/j.engappai.2024.109554
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Abstract

The concept of the picture fuzzy set (PiFS) significantly enhances the multi-criteria decision-making (MCDM) process by incorporating membership value (MV), non-membership value (NMV), and a neutral component. PiFS extends the capabilities of traditional fuzzy sets (FSs), intuitionistic fuzzy sets (IFSs), and other fuzzy models. This paper introduces a novel MCDM approach, the picture fuzzy SWARA-CRITIC-COPRAS (PiF-SCC) method, specifically designed to assist decision-makers (DMs) in evaluating and selecting dynamic digital marketing (DDM) technologies within PiFS settings. The proposed method integrates the strengths of PiFS with step-wise weight assessment ratio analysis (SWARA), criteria importance through intercriteria correlation (CRITIC), and complex proportional assessment (COPRAS), aiming to improve the precision and effectiveness of technology evaluations. To validate the approach, a case study is conducted on DDM technology assessment within a specific business context. The PiF-SCC technique is applied to rank technological options using linguistic terms (LTs), PiFS numbers, an accuracy function (AF), and a score function (SF). Additionally, a comprehensive sensitivity analysis is performed to evaluate the robustness of the proposed method under different input scenarios and uncertainties. A thorough comparison with existing techniques is also provided, demonstrating the superior decision-making capability of the new approach, which leads to more accurate and dependable technology selection results. The manuscript also discusses marginal implications and limitations, along with potential future research directions to further enhance the applicability and effectiveness of the proposed approach.
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图片模糊复合比例评估法与分步权重评估比率分析以及通过标准间相关性确定标准重要性
图片模糊集(PiFS)的概念通过整合成员值(MV)、非成员值(NMV)和中性成分,大大增强了多标准决策(MCDM)过程。PiFS 扩展了传统模糊集(FS)、直觉模糊集(IFS)和其他模糊模型的功能。本文介绍了一种新颖的 MCDM 方法,即图片模糊 SWARA-CRITIC-COPRAS (PiF-SCC) 方法,专门用于帮助决策者(DMs)在 PiFS 环境中评估和选择动态数字营销(DDM)技术。所提出的方法将 PiFS 的优势与逐步权重评估比率分析 (SWARA)、通过标准间相关性确定标准重要性 (CRITIC) 和复杂比例评估 (COPRAS) 相结合,旨在提高技术评估的精确性和有效性。为了验证该方法,我们在一个特定的业务环境中对 DDM 技术评估进行了案例研究。采用 PiF-SCC 技术,使用语言术语 (LT)、PiFS 数字、准确度函数 (AF) 和评分函数 (SF) 对技术方案进行排序。此外,还进行了全面的敏感性分析,以评估拟议方法在不同输入情景和不确定性下的稳健性。还提供了与现有技术的全面比较,证明新方法具有卓越的决策能力,能带来更准确、更可靠的技术选择结果。手稿还讨论了边际影响和局限性,以及未来可能的研究方向,以进一步提高拟议方法的适用性和有效性。
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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