GAIA-SMAA-PROMETHEE for a hierarchy of interacting criteria

被引:37
作者
Arcidiacono, Sally Giuseppe [1 ]
Corrente, Salvatore [1 ]
Greco, Salvatore [1 ,2 ]
机构
[1] Univ Catania, Dept Econ & Business, Corso Italia 55, I-95129 Catania, Italy
[2] Univ Portsmouth, CORL, Portsmouth Business Sch, Richmond Bldg,Portland St, Portsmouth PO1 3DE, Hants, England
关键词
Decision support systems; PROMETHEE methods; Hierarchy of criteria; Robustness concerns; Bipolar Choquet integral; ROBUST ORDINAL REGRESSION; PRINCIPAL COMPONENT ANALYSIS; DECISION-MAKING; PREFERENCE RANKING; BI-CAPACITIES; UNCERTAINTY; SELECTION; ELECTRE; MCDA; SYSTEM;
D O I
10.1016/j.ejor.2018.03.038
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
In this paper, we propose an extension of the PROMETHEE methods. Despite PROMETHEE are among the most applied methods in Multiple Criteria Decision Aiding (MCDA), some drawbacks can be underlined with respect to their applicability since they do not take into account few characteristics of multicriteria decision problems that are very relevant in real world applications: (i) robustness regarding the plurality of compatible preference parameters; (ii) interaction between criteria; (iii) hierarchies of criteria. Even if different extensions of the PROMETHEE methods have taken into account all these aspects singularly, we propose to deal with all of them simultaneously presenting a new version which incorporates Robust Ordinal Regression (ROR), Stochastic Multicriteria Acceptability Analysis (SMAA), bipolar Choquet integral and Multiple Criteria Hierarchy Process (MCHP). ROR and SMAA permit to consider all the instances of the considered preference model compatible with the preference information provided by the DM; the bipolar Choquet integral is able to represent the possible positive and negative interactions between criteria as well as the antagonistic effect between some of them; finally, the MCHP permits to decompose the problem in small parts so that each of them can be analyzed more in detail with respect to the problem at hand. Moreover, we also introduce an extension of the GALA technique to handle visualization in MCDA problems presenting interactions and antagonistic effects between criteria organized in a hierarchy. Furthermore it gives the possibility to display the plurality of instances of the preference model considered by SMAA. A didactic example will illustrate the proposed methodology. (C) 2018 Elsevier B.V. All rights reserved.
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页码:606 / 624
页数:19
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