Applying ER-MCDA and BF-TOPSIS to Decide on Effectiveness of Torrent Protection

Abstract : Experts take into account several criteria to assess the effectiveness of torrential flood protection systems. In practice, scoring each criterion is imperfect. Each system is assessed choosing a qualitative class of effectiveness among several such classes (high, medium, low, no). Evidential Reasoning for Multi-Criteria Decision-Analysis (ER-MCDA) approach can help formalize this Multi-Criteria Decision-Making (MCDM) problem but only provides a coarse ranking of all systems. The recent Belief Function-based Technique for Order Preference by Similarity to Ideal Solution (BF-TOPSIS) methods give a finer ranking but are limited to perfect scoring of criteria. Our objective is to provide a coarse and a finer ranking of systems according to their effectiveness given the imperfect scoring of criteria. Therefore we propose to couple the two methods using an intermediary decision and a quantification transformation step. Given an actual MCDM problem, we apply the ER-MCDA and its coupling with BF-TOPSIS, showing that the final fine ranking is consistent with a previous coarse ranking in this case.
Type de document :
Communication dans un congrès
Jiřina Vejnarová, Václav Kratochvíl. 4th International Conference, BELIEF 2016, Sep 2016, Prague, Czech Republic. Springer International Publishing, Belief Functions: Theory and Applications, Volume 9861, pp 56-65, 2016, Lecture Notes in Computer Science. 〈10.1007/978-3-319-45559-4_6〉
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https://hal-emse.ccsd.cnrs.fr/emse-01364864
Contributeur : Florent Breuil <>
Soumis le : mardi 13 septembre 2016 - 09:15:02
Dernière modification le : jeudi 28 septembre 2017 - 01:12:01

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Simon Carladous, Jean-Marc Tacnet, Jean Dezert, Deqiang Han, Mireille Batton-Hubert. Applying ER-MCDA and BF-TOPSIS to Decide on Effectiveness of Torrent Protection. Jiřina Vejnarová, Václav Kratochvíl. 4th International Conference, BELIEF 2016, Sep 2016, Prague, Czech Republic. Springer International Publishing, Belief Functions: Theory and Applications, Volume 9861, pp 56-65, 2016, Lecture Notes in Computer Science. 〈10.1007/978-3-319-45559-4_6〉. 〈emse-01364864〉

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