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New MCDM methods under uncertainty applied to integrated natural risks management

Abstract : No single, stand-alone decision-aid method, expert assessment methodology or physical, deterministic approaches can be used to solve complex real life problems which often imply several actors and base on heterogeneous imperfect information provided by several more or less reliable sources. This paper presents adapted and extended Multi-Criteria Decision-Making Methods (MCDMs) to help expertise process and adapt to information availability and quality. New MCDMs including fuzzy sets, possibility, belief function theories and information fusion have been designed to cope with imperfect information and uncertainty. The paper recalls and describes their main features and interest before describing a comparison between Cost-Benefit Analysis (CBA, Analytical Hierarchy Process (AHP) and the new Belief Function based Technique for Order Preference by Similarity to Ideal Solution (BF-TOPSIS) methods recently developed in the context of natural risks in mountains.
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Submitted on : Wednesday, September 13, 2017 - 10:46:52 AM
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Jean-Marc Tacnet, Simon Carladous, Jean Dezert, Deqiang Han, Mireille Batton-Hubert. New MCDM methods under uncertainty applied to integrated natural risks management. IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2017, Jun 2017, Annecy, France. ⟨10.1109/CIVEMSA.2017.7995325⟩. ⟨emse-01586720⟩



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