MOONA software for survey classification and evaluation of criteria to support decision-making for properties portfolio
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.
Keyword : MOORA, MONNA, artificial neural network, decision-making, criteria evaluation, properties portfolio
How to Cite
Baierle, I. C. ., Schaefer, J. L. ., Sellitto, M. A. ., Fava, L. P. ., Furtado, J. C. ., & Nara, E. O. B. (2020). MOONA software for survey classification and evaluation of criteria to support decision-making for properties portfolio. International Journal of Strategic Property Management, 24(4), 226-236. https://doi.org/10.3846/ijspm.2020.12338
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Brauers, W. K. M., Ginevicius, R., & Podvezko, V. (2010). Regional development in Lithuania considering multiple objectives by the MOORA method. Technological and Economic Development of Economy, 16, 613–640.
https://doi.org/10.3846/tede.2010.38
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https://doi.org/10.1111/j.1540-6261.2011.01671.x
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https://doi.org/10.1177/002188637100700404
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https://doi.org/10.1007/978-1-84882-067-8_1
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Hashemkhani Zolfani, S., Zavadskas, E. K., & Turskis, Z. (2013). Design of products with both international and local perspectives based on Yin-Yang balance theory and SWARA method. Economic Research-Ekonomska Istraživanja, 26(2), 153–166. https://doi.org/10.1080/1331677X.2013.11517613
Hazarika, B., & Goswami, K. (2018). Micro-entrepreneurship development in the handloom industry: an empirical analysis among the tribal women in Assam. International Journal of Rural Management, 14(1), 22–38.
https://doi.org/10.1177/0973005218754437
Kalibatas, D., & Turskis, Z. (2008). Multicriteria evaluation of inner climate by using MOORA method. Information Technology and Control, 37(1), 79–83.
Kazancoglu, Y., & Ozturkoglu, Y. (2018). Integrated framework of disassembly line balancing with Green and business objectives using a mixed MCDM. Journal of Cleaner Production, 191, 179–191. https://doi.org/10.1016/j.jclepro.2018.04.189
Keršulienė, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new stepwise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243–258. https://doi.org/10.3846/jbem.2010.12
Kracka, M., Brauers, W. K. M., & Zavadskas, E. K. (2010). Ranking heating losses in a building by applying the MULTIMOORA. Inzinerine Ekonomika-Engineering Economics, 21(4), 352–359.
Li, X., Ang, C. L., & Gay, R. (1997). An intelligent scenario generator for strategic business planning. Computers in Industry, 34(3), 261–269. https://doi.org/10.1016/S0166-3615(97)00062-6
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology. http://psycnet.apa.org/record/1933-01885-001
Lindblom, A., & Tikkanen, H. (2010). Knowledge creation and business format franchising. Management Decision, 48(2), 179–188. https://doi.org/10.1108/00251741011022563
Lo, H. W., & Liou, J. J. (2018). A novel multiple-criteria decisionmaking-based FMEA model for risk assessment. Applied Soft Computing, 73, 684-696. https://doi.org/10.1016/j.asoc.2018.09.020
Ly, A., Marsman, M., & Wagenmakers, E. J. (2018). Analytic posteriors for Pearson’s correlation coefficient. Statistica Neerlandica, 72(1), 4–13. https://doi.org/10.1111/stan.12111
Mandal, U. K., & Sarkar, B. (2012). Selection of best intelligent manufacturing system (IMS) under fuzzy MOORA conflicting MCDM environment. International Journal of Emerging Technology and Advanced Engineering, 2(9), 301–310.
Mehdy Hashemy Shahdany, S., & Roozbahani, A. (2015). Selecting an appropriate operational method for main irrigation canals within multicriteria decision-making methods. Journal of Irrigation and Drainage Engineering, 142(4), 04015064. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000996
Mokarram, V., & Banan, M. R. (2018). An improved multi-objective optimization approach for performance-based design of structures using nonlinear time-history analyses. Applied Soft Computing, 73, 647–665. https://doi.org/10.1016/j.asoc.2018.08.048
Nara, E. O. B., Sordi, D. C., Schaefer, J. L., Schreiber, J. N. C., Baierle, I. C., Sellitto, M. A., & Furtado, J. C. (2019). Prioritization of OHS key performance indicators that affecting business competitiveness – a demonstration based on MAUT and Neural Networks. Safety Science, 118, 826–834.
https://doi.org/10.1016/j.ssci.2019.06.01
NetBeans 8.2. (2018). https://netbeans.org/
OPENCSV. (2018). http://opencsv.sourceforge.net/
Osmanbegović, E., & Suljić, M. (2012). Data mining approach for predicting student performance. Economic Review: Journal of Economics and Business, 10(1), 3–12. http://hdl.handle.net/10419/193806
Pagell, M., & Shevchenko, A. (2014). Why research in sustainable supply chain management should have no future. Journal of Supply Chain Management, 50(1), 44–55. https://doi.org/10.1111/jscm.12037
Pamučar, D., Stević, Ž., & Zavadskas, E. K. (2018). Integration of interval rough AHP and interval rough MABAC methods for evaluating university web pages. Applied Soft Computing, 67, 141–163. https://doi.org/10.1016/j.asoc.2018.02.057
Park, J. G., & Jo, S. (2016). Approximate Bayesian MLP regularization for regression in the presence of noise. Neural Networks, 83, 75–85. https://doi.org/10.1016/j.neunet.2016.07.010
Peral, J., Maté, A., & Marco, M. (2017). Application of data mining techniques to identify relevant key performance indicators. Computer Standards & Interfaces, 54, 76–85.
https://doi.org/10.1016/j.csi.2016.11.006
Qian, W., & Shu, W. (2018). Attribute reduction in incomplete ordered information systems with fuzzy decision. Applied Soft Computing, 73, 242–253.
https://doi.org/10.1016/j.asoc.2018.08.032
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