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Article

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Title

Recommending multiple criteria decision analysis methods with a new taxonomy-based decision support system

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee | [ SzD ] doctoral school student

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2022

Published in

European Journal of Operational Research

Journal year: 2022 | Journal volume: vol. 302 | Journal number: no. 2

Article type

scientific article

Publication language

english

Keywords
EN
  • Decision analysis
  • Multiple criteria
  • Taxonomy
  • Decision support system
  • Method recommendation
Abstract

EN We present a new methodology to lead the selection of Multiple Criteria Decision Analysis (MCDA) methods. It is implemented in the Multiple Criteria Decision Analysis Methods Selection Software (MCDA-MSS), a decision support system that helps analysts answer a recurring question in decision science: “Which is the most suitable Multiple Criteria Decision Analysis method (or a subset of MCDA methods) that should be used for a given Decision-Making Problem (DMP)?”. The MCDA-MSS provides guidance to lead decision-making processes and choose among an extensive collection (>200) of MCDA methods. These are assessed according to an original comprehensive set of problem characteristics. The accounted features concern problem formulation, preference elicitation and types of preference information, desired features of a preference model, and construction of the decision recommendation. The applicability of the MCDA-MSS has been tested on several case studies. The MCDA-MSS includes the capabilities of (i) covering from very simple to very complex DMPs, (ii) offering recommendations for DMPs that do not match any method from the collection, (iii) helping analysts prioritize efforts for reducing gaps in the description of the DMPs, and (iv) unveiling methodological mistakes that occur in the selection of the methods. A community-wide initiative involving experts in MCDA methodology, analysts using these methods, and decision-makers receiving decision recommendations will contribute to the expansion of the MCDA-MSS.

Date of online publication

21.01.2022

Pages (from - to)

633 - 651

DOI

10.1016/j.ejor.2022.01.011

URL

https://www.sciencedirect.com/science/article/pii/S0377221722000297

License type

CC BY (attribution alone)

Open Access Mode

czasopismo hybrydowe

Open Access Text Version

final published version

Date of Open Access to the publication

in press

Ministry points / journal

140

Impact Factor

6,4

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