Preference Disaggregation for Multicriteria Decision Aiding: An Overview and Perspectives

2019 ◽  
pp. 115-130 ◽  
Author(s):  
Michalis Doumpos ◽  
Constantin Zopounidis
Keyword(s):  
Multicriteria Decision ◽  
Decision Aiding ◽  
Preference Disaggregation

scholarly journals Rough set and rule-based multicriteria decision aiding

2012 ◽  
Vol 32 (2) ◽  
pp. 213-270 ◽  
Author(s):  
Roman Slowinski ◽  
Salvatore Greco ◽  
Benedetto Matarazzo
Keyword(s):  
Rough Set ◽  
Rule Based ◽  
Multicriteria Decision ◽  
Decision Aiding

scholarly journals Development of a scenario prospecting model with the use of multicriteria decision aiding: Importance of environmental variables

2017 ◽  
Vol 14 (2) ◽  
pp. 210 ◽  
Author(s):  
José Artur Moraes Vieira ◽  
Carlos Francisco Simões Gomes ◽  
Igor Engel Braga
Keyword(s):  
Strategic Planning ◽  
Planning Process ◽  
Financial Resources ◽  
Actual Case ◽  
Multicriteria Decision ◽  
Future Possibility ◽  
Decision Aiding ◽  
The Subject ◽  
Legal Difficulties ◽  
Corporate Social

The significance of using resources optimally comes from its increasingly present scarcity, whether they are related to the environment, term, financial resources, and political or legal difficulties. This study proposes the use of prospective scenarios, considering multiple and uncertain alternatives. It can be an essential tool for the strategic planning process of organizations. The motivation of the subject studied is the possibility to contribute to the expansion of the corporate strategic planning vision and towards social welfare, related to the commitment of companies to society, since it proposes a model for prospecting scenarios supported by multicriteria decision aiding (MDA) approach, necessarily considering variables related to Corporate Social Responsibility and its nuances. As a result, it is expected to fill the identified gap, which places prospecting scenarios as an empirical tool that deals only with economics and a single future possibility. For further research the application of the model in an actual case is suggested, still raising important questions such as: is there a real contribution with the application of prospective scenarios? Is this tool applicable to any type of company? Who are the stakeholders and how do you measure the effectiveness of this tool? 

scholarly journals Questions guiding the choice of a multicriteria decision aiding method

2013 ◽  
Vol 1 (1-2) ◽  
pp. 69-97 ◽  
Author(s):  
Bernard Roy ◽  
Roman Słowiński
Keyword(s):  
Multicriteria Decision ◽  
Decision Aiding

A comparative approach of economic sectors in Sinaloa, Mexico, based on multicriteria decision aiding

EconoQuantum ◽  
2015 ◽  
Vol 13 (1) ◽  
pp. 97-124
Author(s):  
Juan Carlos Leyva López ◽  
◽  
Diego Alonso Gastélum Chavira ◽  
Margarita Urías Ruiz ◽  
◽  
...  
Keyword(s):  
Comparative Approach ◽  
Economic Sectors ◽  
Multicriteria Decision ◽  
Decision Aiding

scholarly journals SOFTWARE PROCESS ASSESSMENT AND IMPROVEMENT USING MULTICRITERIA DECISION AIDING - CONSTRUCTIVIST

2012 ◽  
Vol 9 (3) ◽  
Author(s):  
Leonardo Ensslin ◽  
Luiz Carlos Mesquita Scheid ◽  
Sandra Rolim Ensslin ◽  
Rogerio Tadeu de Oliveira Lacerda
Keyword(s):  
Software Process ◽  
Process Assessment ◽  
Multicriteria Decision ◽  
Decision Aiding ◽  
Software Process Assessment

Verbal ordinal classification with multicriteria decision aiding

2008 ◽  
Vol 185 (3) ◽  
pp. 964-983 ◽  
Author(s):  
Iryna Yevseyeva ◽  
Kaisa Miettinen ◽  
Pekka Räsänen
Keyword(s):  
Ordinal Classification ◽  
Multicriteria Decision ◽  
Decision Aiding

scholarly journals A new approach for criteria weight elicitation of the ARAS-H method

2021 ◽  
Vol 16 ◽  
pp. 89-109
Author(s):  
Maroua Ghram ◽  
◽  
Hela Moalla Frikha ◽  
Keyword(s):  
Mathematical Programming ◽  
Decision Making Process ◽  
Linear Programs ◽  
Programming Technique ◽  
New Approach ◽  
Preference Information ◽  
Decision Aiding ◽  
Criteria Weights ◽  
Preference Disaggregation ◽  
Elicitation Procedure

Criteria weight inference is a crucial step for most of multi-criteria methods. However, criteria weights are often determined directly by the decision-maker (DM) which makes the results unreliable. Therefore, to overcome the imprecise weighting, we suggest the use of the preference programming technique. Instead of obtaining criteria weights directly from the DM, we infer them in a more objective manner to avoid the subjectivity and the unreliability of the results. Our aim is to elicit the ARAS-H criteria weights at each level of the hierarchy tree via mathematical programming, taking into account the DM’s preferences. To put it differently, starting from preference information provided by the DM, we proceed to model our constraints. The ARAS-H method is an extension of the classical ARAS method for the case of hierarchically structured criteria. We adopt a bottom-up approach in order to elicit ARAS-H criteria weights, that is, we start by determining the elementary criteria weights (i.e. the criteria at the lowest level of the hierarchy tree). The solution of the linear programs is obtained using LINGO software. The main contribution of our criteria weight elicitation procedure is in overcoming imprecise weighting without excluding the DM from the decision making process. Keywords: Multiple Criteria Decision Aiding, preference disaggregation, ARAS-H, criteria weights, mathematical programming.

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