ABAC: Alternative by alternative comparison based multi-criteria decision making method. (1st December 2022)
- Record Type:
- Journal Article
- Title:
- ABAC: Alternative by alternative comparison based multi-criteria decision making method. (1st December 2022)
- Main Title:
- ABAC: Alternative by alternative comparison based multi-criteria decision making method
- Authors:
- Biswas, Amit
Baranwal, Gaurav
Kumar Tripathi, Anil - Abstract:
- Highlights: A new Multi-Criteria Decision-Making Method, ABAC, is proposed. ABAC is simple, more scalable and free from rank reversal problem. ABAC is illustrated and validated using cloud service selection. Various case studies using ABAC in different domains are provided. Abstract: 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 isHighlights: A new Multi-Criteria Decision-Making Method, ABAC, is proposed. ABAC is simple, more scalable and free from rank reversal problem. ABAC is illustrated and validated using cloud service selection. Various case studies using ABAC in different domains are provided. Abstract: 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. … (more)
- Is Part Of:
- Expert systems with applications. Volume 208(2022)
- Journal:
- Expert systems with applications
- Issue:
- Volume 208(2022)
- Issue Display:
- Volume 208, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 208
- Issue:
- 2022
- Issue Sort Value:
- 2022-0208-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-12-01
- Subjects:
- Multi-criteria decision-making (MCDM) -- Multi-attribute decision-making (MADM) -- Ranking -- Rank reversal -- Decision analysis
Expert systems (Computer science) -- Periodicals
Systèmes experts (Informatique) -- Périodiques
Electronic journals
006.33 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09574174 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.eswa.2022.118174 ↗
- Languages:
- English
- ISSNs:
- 0957-4174
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3842.004220
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 23331.xml