Multiple criteria decision aid; Linear Rankings; Big data
Abstract :
[en] We present in this paper a sparse HPC implementation for outranking digraphs of huge orders, up to several millions of decision alternatives. The proposed outranking digraph model is based on a quantiles equivalence class decomposition of the underlying multicriteria performance tableau. When locally ranking each of these ordered components, we may readily obtain an overall linear ranking of big sets of decision alternatives. For the local rankings, both, Copeland's as well as the Net-Flows ranking rules, appear to give the best compromise between, on the one side, the fitness of the overall ranking with respect to the given global outranking relation and, on the other side, computational tractability for very big outranking digraphs modelling up to several trillions of pairwise outranking situations.
Research center :
ULHPC - University of Luxembourg: High Performance Computing
Disciplines :
Computer science
Author, co-author :
BISDORFF, Raymond ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
External co-authors :
no
Language :
English
Title :
Computing linear rankings from trillions of pairwise outranking situations
Publication date :
November 2016
Number of pages :
6
Event name :
DA2PL'2016 From Multiple Criteria Decision Aid to Preference Learning
Event organizer :
University of Paderborn
Event place :
Paderborn, Germany
Event date :
from 07-11-2016 to 08-11-2016
Audience :
International
Focus Area :
Computational Sciences
FnR Project :
FNR10367986 - Algorithmic Decision Theory, 2015 (01/01/2015-31/12/2018) - Christoph Dr Schommer
