scispace - formally typeset
M

María Teresa Lamata

Researcher at University of Granada

Publications -  65
Citations -  1650

María Teresa Lamata is an academic researcher from University of Granada. The author has contributed to research in topics: Fuzzy logic & Fuzzy number. The author has an hindex of 20, co-authored 64 publications receiving 1381 citations.

Papers
More filters
Journal ArticleDOI

GIS-based onshore wind farm site selection using Fuzzy Multi-Criteria Decision Making methods. Evaluating the case of Southeastern Spain

TL;DR: Some fuzzy approaches of different Multi-Criteria Decision Making (MCDM) methods are combined in order to deal with a trending decision problem such as onshore wind farm site selection.
Journal ArticleDOI

A new measure of consistency for positive reciprocal matrices

TL;DR: The analytic hierarchy process (AHP) provides a decision maker with a way of examining the consistency of entries in a pairwise comparison matrix and the hierarchy as a whole through the consistency ratio measure.
Journal ArticleDOI

Comparative TOPSIS-ELECTRE TRI methods for optimal sites for photovoltaic solar farms. Case study in Spain

TL;DR: In this article, the best locations to build solar photovoltaic farms (large grid-connected PV-cells which have more than 100kW p of installed capacity) with the coast of Murcia in the southeast of Spain being used as an example are selected.
Journal ArticleDOI

The shortest path problem on networks with fuzzy parameters

TL;DR: An iterative algorithm that assumes a generic ranking index for comparing the fuzzy numbers involved in the problem, in such a way that each time in which the decision-maker wants to solve a concrete problem, he can choose (or propose) the ranking index that best suits that problem.
Journal ArticleDOI

RIM-reference ideal method in multicriteria decision making

TL;DR: A new method based on the concept of “ideal solution” is presented, which can vary between the maximum value and the minimum value; this does not occur in the VIKOR and TOPSIS methods in which these values are the extreme values.