ProBCA Taxonomy of Discrete MCDA Methods for Ranking

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dc.contributor.authorKirensky, Roman
dc.contributor.authorLawson, Craig
dc.contributor.authorSalonitis, Konstantinos
dc.date.accessioned2024-06-12T12:54:35Z
dc.date.available2024-06-12T12:54:35Z
dc.date.issued2023-11-01 09:46
dc.description.abstractThis book is accompanied by the publication titled 'A SYNOPTIC TAXONOMY OF DISCRETE MULTI-CRITERIA DECISION ANALYSIS METHODS FOR RANKING', which introduces the presented taxonomy for MADM (Multi-Attribute Decision-Making) methods for ranking tasks. The taxonomy consists of: - the collection of 300 MADM methods covering the various parts of a typical MCDA (Multi-Criteria Decision Analysis) process; - the characterisation system for the recorded methods called ProBCA (Problem-Based Characterisation Approach). The title (ProBCA) reflects an application-oriented mindset that the presented taxonomy is based on. It focuses on the DP (Decision Problem) parameters and how the DM (Decision Maker) deals with it to describe the presented methods, rather than the intrinsic characteristics of the methods itself. The taxonomy is operated by picking from the list of available values for each of the 17 descriptor parameters characterising the possible DP context specifics and DM constraints. If a method (or several) matching the provided DP characterisation is available in the presented collection, it will remain visible after filtering for appropriate values while the remaining methods will become hidden. It is possible to use partial DP characterisation to identify a range of potentially suitable methods if the DM is flexible about theof defining define the DP and how to approach its solution. The number of methods matching each of the available characterising values is always shown next to these values in the top section of the taonomy, and is progressively updated as the DM proceeds with value selection. The taxonomy is dedicated to allow a broad spectrum of DMs to efficiently select the most appropriate MADM method for their ranking DP at hand.
dc.description.sponsorship'Future Cabin for the Asian Market'
dc.identifier.citationKirensky, Roman; Lawson, Craig; Salonitis, Konstantinos (2021). ProBCA Taxonomy of Discrete MCDA Methods for Ranking. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.13420022
dc.identifier.doi10.17862/cranfield.rd.13420022
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22457
dc.publisherCranfield University
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dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject'MCDA'
dc.subject'decision-making models'
dc.subject'decision-analysis'
dc.subject'choice framework'
dc.subject'selection criteria'
dc.subject'Artificial Intelligence'
dc.subject'Taxonomic analysis'
dc.subject'Operations Research'
dc.subject'Mathematical Logic
dc.subjectSet Theory
dc.subjectLattices and Universal Algebra'
dc.subject'Decision Theory'
dc.subject'Decision Making'
dc.subject'Decision Support and Group Support Systems'
dc.titleProBCA Taxonomy of Discrete MCDA Methods for Ranking
dc.typeDataset

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