A new extension of the EDAS method in a fuzzy environment for group decision-making

IF 1.5 Q3 MANAGEMENT Decision Pub Date : 2024-08-27 DOI:10.1007/s40622-024-00396-x
Dariusz Kacprzak
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Abstract

The complexity of the decision-making problems being analysed has led to the development of multiple multi-criteria decision-making (MCDM) methods. One of the more recent methods belonging to this group is the evaluation based on distance from average solution (EDAS) method. To date, it has found extensive use in solving real-world decision-making problems and has seen many extensions to input data types other than real numbers. One of these is the EDAS method for group decision-making in a fuzzy environment. This method aggregates individual evaluations of decision-makers into a group evaluation using the arithmetic mean. This may result in equal group ratings despite the variety of individual ratings, making it difficult or even impossible to rank alternatives because the EDAS algorithm will be blocked. The paper proposes a new fuzzy extension of EDAS called the PFEDAS method for group decision-making. The main difference between the proposed method and the original one is that at the initial stage the individual decision matrices are not aggregated into a group matrix but are transformed into matrices of alternatives. As a result, the new PFEDAS method is based on the initial data instead of their averaged values which allows a more accurate comparison of alternatives. Using a numerical example, the PFEDAS method is compared with other similar methods known from the literature.

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模糊环境下 EDAS 方法的新扩展,用于群体决策
决策问题分析的复杂性导致了多重标准决策(MCDM)方法的发展。基于平均解距离的评估(EDAS)方法是最近出现的一种方法。迄今为止,该方法已广泛应用于解决现实世界中的决策问题,并在实数以外的输入数据类型中得到了许多扩展。其中之一就是用于模糊环境下群体决策的 EDAS 方法。这种方法使用算术平均值将决策者的个人评价汇总为小组评价。尽管个体评价各不相同,但这可能会导致群体评价相等,从而使 EDAS 算法受阻,难以甚至无法对备选方案进行排序。本文提出了一种新的 EDAS 模糊扩展方法,即用于群体决策的 PFEDAS 方法。所提出的方法与原始方法的主要区别在于,在初始阶段,个体决策矩阵不会被汇总为群体矩阵,而是被转换为备选方案矩阵。因此,新的 PFEDAS 方法是基于初始数据,而不是它们的平均值,这样可以更准确地比较备选方案。通过一个数值示例,我们将 PFEDAS 方法与文献中已知的其他类似方法进行了比较。
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来源期刊
Decision
Decision MANAGEMENT-
CiteScore
2.30
自引率
0.00%
发文量
27
期刊介绍: The aim of the Journal, Decision, is to publish qualitative, quantitative, survey-based, simulation-based research articles at the national and sub-national levels. While there is no stated regional focus of the journal, we are more interested in examining if and how individuals, firms and governments in emerging economies may make decisions differently. Published for the management scholars, business executives and managers, the Journal aims to advance the management research by publishing empirically and theoretically grounded articles in management decision making process. The Editors aim to provide an efficient and high-quality review process to the authors. The Journal accepts submissions in several formats such as original research papers, case studies, review articles and book reviews (book reviews are only by invitation). The Journal welcomes research-based, original and insightful articles on organizational, individual, socio-economic-political, environmental decision making with relevance to theory and practice of business. It also focusses on the managerial decision-making challenges in private, public, private-public partnership and non-profit organizations. The Journal also encourages case studies that provide a rich description of the business or societal contexts in managerial decision-making process including areas – but not limited to – conflict over natural resources, product innovation and copyright laws, legislative or policy change, socio-technical embedding of financial markets, particularly in developing economy, an ethnographic understanding of relations at a workplace, or social network in marketing management, etc. Research topics covered in the Journal include (but not limited to): Finance and Accounting Organizational Theory and Behavior Decision Science Public Policy-Economic Insights Operation Management Innovation and Entrepreneurship Information Technology and Systems Management Optimization and Modelling Supply Chain Management Data Analytics Marketing Management Human Resource Management
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