3 research outputs found
A novel sorting method topsis-sort: an applicaiton for tehran environmental quality evaluation
Many real-life problems are multi-objective by nature that requires evaluation of more than one
criterion, therefore MCDM has become an important issue. In recent years, many MCDM methods
have been developed; the existing approaches have been improved and extended. Multi criteria
decision analysis has been regarded as a suitable set of methods to perform sustainability
evaluations. Among numerous MCDM methods developed to solve real-life decision problems,
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work
satisfactorily in diverse application areas. In this paper, a novel sorting method (TOPSIS-Sort) based
on the classic TOPSIS method is presented. In the TOPSIS-Sort approach an outranking relation
is used for sorting purposes. The proposed approach uses characteristic profiles for defining the
classes and outranking relation as the preference model. Application of the proposed approach is
demonstrated by classifying 22 districts of Tehran into
five classes (but none of the districts
fits into
Classes 4 and 5), representing areas with different levels of environmental quality. An analysis and
assessment of the environmental conditions in Tehran helps to identify the districts with the poor
environmental quality. Priority should be given to these areas to maintain and improve the quality
of environment. The results obtained by the TOPSIS-Sort give credence to its success, because
the results of sorting con
firm our and specialists’ evaluation of the districts. This research provides
appropriate results with respect to the development of sorting models in the form of outranking
relations. The model, proposed by this study, is applicable to the other outranking methods such as
ELECTRE, PROMETHEE, etc
Solving unstructured classification problems with multicriteria decision aiding
Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Automação). Universidade do Porto. Faculdade de Engenharia. 201
Handling multicriteria preferences in cluster analysis
In the framework of multicriteria decision aid, a lot of interest has been devoted to sorting problems, in which the set of categories is pre-defined. Besides, preference oriented multicriteria clustering has received little attention. Usual geometric and related metrics are not well suited for this problem. Here, we propose a clustering method based on a valued indifference relation inspired by outranking methods. We suggest a method (based on comparing cluster centers and an average net flow score of clusters) to build a complete ranking of the set of clusters, that is, a way of defining a set of ordered categories for sorting purposes. The new approach performs very well in some examples.Data mining Clustering Multicriteria analysis Outranking methods