ABAC: Alternative by alternative comparison based multi-criteria decision making method

被引：5
作者：
Biswas A. [1 ]
Baranwal G. [2 ]
Kumar Tripathi A. [3 ]
机构：
[1] School of Computer Science Engineering and Technology, Bennett University, Greater Noida
[2] Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi
[3] Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi
来源：
Expert Systems with Applications | 2022年 / 208卷
关键词：
Decision analysis; Multi-attribute decision-making (MADM); Multi-criteria decision-making (MCDM); Rank reversal; Ranking;
D O I：
10.1016/j.eswa.2022.118174
中图分类号：
学科分类号：
摘要：
Decision-making appears as a complex and challenging task when it requires finding the most suitable alternative among the numerous alternatives in the presence of multiple, usually conflicting criteria. At the same time, stakeholders expect a simple, transparent, and traceable decision-making method. Multi-Criteria Decision-Making (MCDM) methods rank the alternatives considering multiple criteria. The rank reversal problem is an important issue in most existing conventional MCDM methods. This paper proposes a new alternative by alternative comparison-based MCDM Method (ABAC) that addresses the rank reversal problem. We prove that ABAC is free from the rank reversal problem. To illustrate and validate ABAC, we have taken the cloud service selection problem as an application. Further, to show the effectiveness of ABAC, we have provided several case studies covering various domains. We perform several experiments by simulating the ABAC method. We have compared ABAC and existing MCDM methods. The experimental results support that the ABAC method is a rank reversal free MCDM method. We also carry out sensitivity analysis for ABAC. Salient features of ABAC over existing MCDM methods are (i) it is simple; (ii) it is rank reversal free; (iii) it is more scalable. © 2022 Elsevier Ltd
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