How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy.

Decision making Decision support system MCDA Method recommendation Multiple criteria Taxonomy

Journal

Omega
ISSN: 0305-0483
Titre abrégé: Omega
Pays: England
ID NLM: 9876431

Informations de publication

Date de publication:
2020
Historique:
entrez: 22 3 2021
pubmed: 1 1 2020
medline: 1 1 2020
Statut: ppublish

Résumé

Decision making is a complex task that involves a multitude of perspectives, constraints, and variables. Multiple Criteria Decision Analysis (MCDA) is a process that has been used for several decades to support decision making. It includes a series of steps that systematically help Decision Maker(s) (DM(s)) and stakeholders in structuring a decision making problem, identifying their preferences, and building a decision recommendation consistent with those preferences. Over the last decades, many studies have demonstrated the conduct of the MCDA process and how to select an MCDA method. Until now, there has not been a review of these studies, nor a proposal of a unified and comprehensive high-level representation of the MCDA process characteristics (i.e., features), which is the goal of this paper. We introduce a review of the research that defines how to conduct the MCDA process, compares MCDA methods, and presents Decision Support Systems (DSSs) to recommend a relevant MCDA method or a subset of methods. We then synthesize this research into a taxonomy of characteristics of the MCDA process, grouped into three main phases, (i) problem formulation, (ii) construction of the decision recommendation, and (iii) qualitative features and technical support. Each of these phases includes a subset of the 10 characteristics that helps the analyst implementing the MCDA process, while also being aware of the implication of these choices at each step. By showing how decision making can be split into manageable and justifiable steps, we reduce the risk of overwhelming the analyst, as well as the DMs/stakeholders during the MCDA process. A questioning strategy is also proposed to demonstrate how to apply the taxonomy to map MCDA methods and select the most relevant one(s) using real case studies. Additionally, we show how the DSSs for MCDA method recommendation can be grouped into three main clusters. This proposal can enhance a traceable and categorizable development of such systems.

Identifiants

pubmed: 33746337
doi: 10.1016/j.omega.2020.102261
pmc: PMC7970504
mid: NIHMS1673395
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Intramural EPA
ID : EPA999999
Pays : United States

Déclaration de conflit d'intérêts

Declaration of Competing Interest None.

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Auteurs

Marco Cinelli (M)

Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland.

Miłosz Kadziński (M)

Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland.

Michael Gonzalez (M)

Environmental Decision Analytics Branch, Land Remediation and Technology Division, Center for Environmental Solutions and Emergency Response, Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Dr., Cincinnati, 45268, OH, United States.

Roman Słowiński (R)

Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland.
Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland.

Classifications MeSH