Mathematical modeling for business decision making - MODELEM

Reference of the Group:

GIUV2023-594

 
Description of research activity:
The Mathematical Modelling for Business Decision Making (MODELEM) group is attached to the Department of Mathematics for Economics and Business (DMEE) of the University of Valencia (UV). Specifically, it is made up of four associate university professors and one contract associate professor. MODELEM is made up of a multidisciplinary group of experts in mathematical techniques applied to real data. Our main objective is to respond and guide decision-making in problems and concerns raised by companies, higher education institutions, university services and any other public of interest. Although the team's research experience is very broad, as shown by their participation in many European projects, the national research plan and different regional plans, the aim of consolidating this research group is to start a new research career in the Department of Mathematics for Economics and Business in which younger or newly incorporated researchers can be integrated. The members of the group have research experience directly related to the lines of research proposed in the group: indicator systems, data envelopment analysis, multi-criteria analysis, time series and machine learning. The main...The Mathematical Modelling for Business Decision Making (MODELEM) group is attached to the Department of Mathematics for Economics and Business (DMEE) of the University of Valencia (UV). Specifically, it is made up of four associate university professors and one contract associate professor. MODELEM is made up of a multidisciplinary group of experts in mathematical techniques applied to real data. Our main objective is to respond and guide decision-making in problems and concerns raised by companies, higher education institutions, university services and any other public of interest. Although the team's research experience is very broad, as shown by their participation in many European projects, the national research plan and different regional plans, the aim of consolidating this research group is to start a new research career in the Department of Mathematics for Economics and Business in which younger or newly incorporated researchers can be integrated. The members of the group have research experience directly related to the lines of research proposed in the group: indicator systems, data envelopment analysis, multi-criteria analysis, time series and machine learning. The main line of the group's leading researcher, María del Carmen Bas, is dedicated to the construction of simple and composite indicators defined in different fields of application (economics and business, social sciences, education and environmental sciences). Team members Vicente José Bolós Lacave and Rafael Benítez Suárez have a long history of joint collaboration in different lines of research. These include the "Integral Equations and Non-Linear Analysis" line and the "Mathematical Models and Data Analysis in Biophysics" line. They also have applied experience in the development of software libraries (packages) in R programming language and in the development of web applications with Shiny interface. The research areas in which the other two members of the group, María José Marín and Pedro Ruiz, have worked correspond to the use of quantitative techniques and mathematical modelling to address problems involving many variables, as well as in the statistical data processing. It should be noted that the lines of work proposed by the MODELEM group have a very practical aspect, which makes their research clearly directed towards solving real problems. Therefore, many of the activities have been developed within the framework of projects or transfer agreements with companies. The MODELEM group is currently involved in several projects. On the one hand, it is working on an open line of research together with the University of Extremadura, focused on the application of multi-criteria techniques for analysing academic-work careers. Furthermore, it is working on the development of a platform of indicators for business decision-making evaluating the impact of COVID-19, a project within the call for emerging projects of the Regional Government of Valencia.
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Web:
 
Scientific-technical goals:
  • Implementacion de tecnicas matematicas aplicadas a los datos reales
  • Apoyo en la toma de decisiones a problemas e inquietudes planteadas por empresas, instituciones de educacion superior, servicios de la universidad etc
  • Desarrollo de software implementado tecnicas matematicas orientadas a la toma de decisiones
 
Research lines:
  • Data Envelopment Analysis.Data envelopment analysis (DEA) is a non-parametric technique for measuring the relative inefficiency of organisational units (Decision Making Units [DMUs]) where there are multiple input and/or output variables.
  • Indicator System.Data set through the analysis of which summarised or synthetic forms are obtained to understand the phenomenon studied.
  • Machine Learning.Machine learning is a branch of artificial intelligence, inferential statistics and computing in which algorithms for pattern detection are developed.
  • Multicriteria Decision Analysis.Multi-criteria analysis is a method that assists decision-making when there are different alternatives that must be ordered according to a series of criteria that, in many cases, are contradictory or come into conflict.
  • Wavelets.Wavelet analysis of time series makes it possible to analyse and decompose signals at different frequencies or scales over time, overcoming the limitations of Fourier analysis.
 
Group members:
Name Nature of participation Entity Description
MARIA DEL CARMEN BAS CERDADirectorUniversitat de València
Research team
PEDRO DAVID RUIZ FEMENIAMemberUniversitat de València
VICENTE BOLOS LACAVECollaboratorUniversitat de València
MARIA JOSE MARIN FERNANDEZCollaboratorUniversitat de València
RAFAEL BENITEZ SUAREZCollaboratorUniversitat de València
 
CNAE:
  • -
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Keywords:
  • eficiencia; productividad
  • indicador; indicador compuesto
  • algoritmo; machine learning
  • toma de decisiones; multicriterio
  • predicción; multirresolución