Authors:
Pasi Luukka
1
;
Mario Fedrizzi
2
;
Leoncie Niyigena
1
and
Mikael Collan
1
Affiliations:
1
Lappeenranta University of Technology, Finland
;
2
University of Trento, Italy
Keyword(s):
Fuzzy Similarity, Fuzzy TOPSIS, Multi-distances, OWA, O’Hagan’s Method.
Related
Ontology
Subjects/Areas/Topics:
Approximate Reasoning and Fuzzy Inference
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Fuzzy Information Processing, Fusion, Text Mining
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
This article introduces a new extension to fuzzy similarity based fuzzy TOPSIS that uses multi-distance in aggregation. OWA-based multi-distances are used in the aggregation process. For the weight generation in OWA the O'Hagan's method is used to find optimal weights. Several different, predefined orness values were tested. The presented method is applied to a project selection problem.