D
Daniel Vanderpooten
Researcher at Paris Dauphine University
Publications - 115
Citations - 4603
Daniel Vanderpooten is an academic researcher from Paris Dauphine University. The author has contributed to research in topics: Knapsack problem & Regret. The author has an hindex of 32, co-authored 112 publications receiving 4178 citations. Previous affiliations of Daniel Vanderpooten include Kaiserslautern University of Technology & Centre national de la recherche scientifique.
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A generalized definition of rough approximations based on similarity
TL;DR: New definitions of lower and upper approximations are proposed, which are basic concepts of the rough set theory and are shown to be more general, in the sense that they are the only ones which can be used for any type of indiscernibility or similarity relation.
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Min–max and min–max regret versions of combinatorial optimization problems: A survey
TL;DR: This work surveys complexity results for the min-max and min- max regret versions of some combinatorial optimization problems: shortest path, spanning tree, assignment, min cut, min s-t cut, knapsack, and investigates the approximability of these problems.
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The European school of MCDA: Emergence, basic features and current works
Bernard Roy,Daniel Vanderpooten +1 more
TL;DR: In this article, the authors trace the emergence of the European School of Multi-criteria Decision Analysis (MSDA) and provide a general review of the current major research topics developed within this framework.
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Solving efficiently the 0-1 multi-objective knapsack problem
TL;DR: The main idea of the approach relies on the use of several complementary dominance relations to discard partial solutions that cannot lead to new non-dominated criterion vectors to obtain an efficient method that outperforms the existing methods both in terms of CPU time and size of solved instances.
Proceedings ArticleDOI
An outranking approach for rank aggregation in information retrieval
TL;DR: This paper proposes a rank aggregation method within a multiple criteria framework using aggregation mechanisms based on decision rules identifying positive and negative reasons for judging whether a document should get a better rank than another.