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On ordered weighted averaging aggregation operators in multicriteria decision-making
Ronald R. Yager
- Vol. 18, pp 183-190
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The article was published on 1988-01-01 and is currently open access. It has received 3030 citations till now. The article focuses on the topics: Ordered weighted averaging aggregation operator.read more
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Location selection of city logistics centers under sustainability
TL;DR: A new evaluation system for the location selection of a CLC from a sustainability perspective using a fuzzy multi-attribute group decision making (FMAGDM) technique based on a linguistic 2-tuple to evaluate potential alternative CLC locations.
Book ChapterDOI
Learning from ambiguously labeled examples
Eyke Hüllermeier,Jürgen Beringer +1 more
TL;DR: This paper is concerned with ambiguous label classification (ALC), an extension of this setting in which several candidate labels may be assigned to a single example, and shows that appropriately designed learning algorithms can successfully exploit the information contained in ambiguously labeled examples.
Journal ArticleDOI
Analytic properties of maximum entropy OWA operators
TL;DR: The maximum entropy approach to obtaining the weights associated with the ordered weighted averaging (OWA) aggregation operator is discussed and an analytic form for these weights are obtained and some of their properties are described.
Journal ArticleDOI
A heuristic for the multi-satellite, multi-orbit and multi-user management of Earth observation satellites
TL;DR: A tabu search heuristic for the problem of selecting and scheduling the requests to be satisfied, under operational constraints is described, and an upper bounding procedure based on column generation is used to evaluate the quality of the solutions.
Journal ArticleDOI
Minimum-Cost Consensus Models Under Aggregation Operators
TL;DR: Analytical results indicate that the proposed framework reduces to the consensus model of Ben-Arieh when the selected aggregation operator is the ordered weighted averaging (OWA) operator with weight vector.