Open AccessMOONA SOFTWARE FOR SURVEY CLASSIFICATION AND EVALUATION OF CRITERIA TO SUPPORT DECISION-MAKING FOR PROPERTIES PORTFOLIO
Author(s) -
Ismael Cristofer Baierle,
Jones Luís Schaefer,
Miguel Afonso Sellitto,
Leandro Pinto Fava,
João Carlos Furtado,
Elpídio Oscar Benitez Nara
Publication year - 2020
Publication title -
international journal of strategic property management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.43
H-Index - 30
eISSN - 1648-9179
pISSN - 1648-715X
DOI - 10.3846/ijspm.2020.12338
Subject(s) - computer science , artificial neural network , software , portfolio , project portfolio management , machine learning , degree (music) , artificial intelligence , data mining , operations research , mathematics , project management , engineering , systems engineering , financial economics , economics , programming language , physics , acoustics
The MOORA for Neural Networks Analysis (MONNA) software was created to classify variables and evaluate the degree of correlation between them, helping to choose a property portfolio and facilitating decision making involving multiple criteria. The MONNA software presents the classification of the alternatives calculated automatically by the MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) and provides a Global Average Rate (GAR). Artificial Neural Networks (ANNs) analysis provides the degree of correlation between variables and uses GAR as the output parameter. The degree of correlation between the variables allows us to assess whether these variables are dependent on each other and can capture customer preferences. For the application we used a survey that sought to know the preferences of customers, which will serve to make the decision of which properties should be part of the company’s portfolio. The contribution and originality of the MONNA software is that through the integration of the MOORA and ANN methods, the classification and criterion evaluation calculations are faster and standardized. The use of software by decision makers helps to have more accurately find and classify available options, preventing simulations from being done by iterative processes and providing validated numerical data for management evaluation.