Abstract

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.

Highlights

  • Most day-to-day problems include alternatives and various criteria and it is up to the manager to decide on the most appropriate alternative (Safarzadeh et al, 2018)

  • We are looking for some examples such as the integration of AHP (Analytic Hierarchy Process) with MABAC (Multi-Attributive Border Approximation Area Comparison) method for evaluation of university web sites (Pamučar et al, 2018), a new model of FMEA (Failure Mode and Effect Analysis) based on multi-criteria decision-making for risk assessment (Lo & Liou, 2018), extension of the FC-MOPSO (Fuzzy Clustering Multi-Objective Particle Swarm Optimizer) algorithm employed to evaluate optimization problems (Mokarram & Banan, 2018) and a model for reducing attributes in incomplete information systems (Qian & Shu, 2018)

  • This paper aims to present the MOORA for Neural Networks Analysis (MONNA) software, that was created to assist 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) that will be used as a output parameter by Artificial Neural Network (ANN) analysis to provide the degree of correlation between the variables

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Summary

Introduction

Most day-to-day problems include alternatives and various criteria and it is up to the manager to decide on the most appropriate alternative (Safarzadeh et al, 2018). We are looking for some examples such as the integration of AHP (Analytic Hierarchy Process) with MABAC (Multi-Attributive Border Approximation Area Comparison) method for evaluation of university web sites (Pamučar et al, 2018), a new model of FMEA (Failure Mode and Effect Analysis) based on multi-criteria decision-making for risk assessment (Lo & Liou, 2018), extension of the FC-MOPSO (Fuzzy Clustering Multi-Objective Particle Swarm Optimizer) algorithm employed to evaluate optimization problems (Mokarram & Banan, 2018) and a model for reducing attributes in incomplete information systems (Qian & Shu, 2018) What these models have in common is that they need quality input data, which is usually collected by a survey. We discuss the results and propose some future work

Theoretical board
Artificial neural network
Pearson’s correlation
About MONNA development
Stages of obtaining information
Initial analysis
Final analysis
General conclusions
Full Text
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