owa operator
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Author(s):  
István Á. Harmati ◽  
Robert Fullér ◽  
Imre Felde
Keyword(s):  

2021 ◽  
Vol 21 (2) ◽  
pp. 15-34
Author(s):  
Martín Huesca-Gastélum ◽  
Martín León-Santiesteban

The objective of this paper is to rank the competitiveness of tourist destinations based on different aggregation operators, specifically, the ordered weighted average (OWA) operator and the simple additive weighting (SAW) method. The use of these methods allows tourist destinations to be sorted according to their competitiveness. In addition, it enables the generation of different scenarios that highlight the relative importance of each criterion. This information is useful for the government and recreation sites when generating different evaluations based on the specific characteristics of each municipality. An application of these methods to determine the competitiveness of the tourism destinations of Sinaloa, Mexico has been performed.  JEL Codes: D49, L83, C44 Received: 10/07/2020.  Accepted: 03/11/2020.  Published: 01/12/2021. 


Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3045
Author(s):  
Emili Vizuete-Luciano ◽  
Sefa Boria-Reverter ◽  
José M. Merigó-Lindahl ◽  
Anna Maria Gil-Lafuente ◽  
Maria Luisa Solé-Moro

The ordered weighted averaging (OWA) operator is one of the most used techniques in the operator’s aggregation procedure. This paper proposes a new assignment algorithm by using the OWA operator and different extensions of it in the Branch-and-bound algorithm. The process is based on the use of the ordered weighted average distance operator (OWAD) and the induced OWAD operator (IOWAD). We present it as the Branch-and-bound algorithm with the OWAD operator (BBAOWAD) and the Branch-and-bound algorithm with the IOWAD operator (BBAIOWAD). The main advantage of this approach is that we can obtain more detailed information by obtaining a parameterized family of aggregation operators. The application of the new algorithm is developed in a consumer decision-making model in the city of Barcelona regarding the selection of groceries by districts that best suit their needs. We rely on the opinion of local commerce experts in the city. The key advantage of this approach is that we can consider different sources of information independent of each other.


Author(s):  
Diego García-Zamora ◽  
Álvaro Labella ◽  
Rosa M. Rodríguez ◽  
Luis Martínez
Keyword(s):  

Author(s):  
Muhammad Touqeer ◽  
Rimsha Umer ◽  
Ali Ahmadian ◽  
Soheil salahshour

Multi-criteria decision-making (MCDM) is concerned with structuring and solving decision problems involving multiple criteria for decision-makers in vague and inadequate environment. The “Technique for Order Preference by Similarity to Ideal Solution” (TOPSIS) is one of the mainly used tactic to deal with MCDM setbacks. In this article, we put forward an extension of TOPSIS with interval type-2 trapezoidal neutrosophic numbers (IT2TrNNs) using the concept of (α, β, γ)-cut. First, we present a novel approach to compute the distance between two IT2TrNNs using ordered weighted averaging (OWA) operator and (α, β, γ)-cut. Subsequently, we broaden the TOPSIS method in the context of IT2TrNNs and implemented it on a MCDM problem. Lastly, a constructive demonstration and several contrasts with the other prevailing techniques are employed to articulate the practicability of the proposed technique. The presented strategy yields a flexible solution for MCDM problems by considering the attitudes and perspectives of the decision-makers.


2021 ◽  
Vol 11 (16) ◽  
pp. 7195
Author(s):  
Iris Dominguez-Catena ◽  
Daniel Paternain ◽  
Mikel Galar

Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the exponential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations.


Author(s):  
Wen He ◽  
Rosa M. Rodríguez ◽  
Bapi Dutta ◽  
Luis Martínez
Keyword(s):  

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