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MOONA software for survey classification and evaluation of criteria to support decision-making for properties portfolio

    Ismael Cristofer Baierle Affiliation
    ; Jones Luis Schaefer   Affiliation
    ; Miguel Afonso Sellitto   Affiliation
    ; Leandro Pinto Fava   Affiliation
    ; João Carlos Furtado   Affiliation
    ; Elpidio Oscar Benitez Nara   Affiliation

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
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May 22, 2020
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References

Akkaya, G., Turanoğlu, B., & Öztaş, S. (2015). An integrated fuzzy AHP and fuzzy MOORA approach to the problem of industrial engineering sector choosing. Expert Systems with Applications, 42(24), 9565–9573.
https://doi.org/10.1016/j.eswa.2015.07.061

Alinezhad, A., & Khalili, J. (2019). MOORA method. In New Methods and Applications in Multiple Attribute Decision Making (MADM) (pp. 81–85). Springer.
https://doi.org/10.1007/978-3-030-15009-9_18

Apache. (2018). The Apache software foundation. https://www.apache.org/licenses/LICENSE-2.0

Arabsheybani, A., Paydar, M. M., & Safaei, A. S. (2018). An integrated fuzzy MOORA method and FMEA technique for sustainable supplier selection considering quantity discounts and supplier’s risk. Journal of Cleaner Production, 190, 577–591. https://doi.org/10.1016/j.jclepro.2018.04.167

Archana, M., & Sujatha, V. (2012). Application of fuzzy MOORA and GRA in multi-criterion decision making problems. International Journal of Computer Applications, 53(9), 46–50. https://doi.org/10.5120/8452-2249

Baležentis, T. (2011). A farming efficiency estimation model based on fuzzy MULTIMOORA. Management Theory and Studies for Rural Business and Infrastructure Development, 29(5), 43–52.

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

Brauers, W. K. M., & Zavadskas, E. K. (2010). Project management by MULTIMOORA as an instrument for transition economies. Technological and Economic Development of Economy, 16(1), 5–24. https://doi.org/10.3846/tede.2010.01

Brauers, W. K., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35, 445–469.

Brauers, W. K. M., Zavadskas, E. K., Peldschus, F., & Turskis, Z. (2008). Multi-objective decision making for road design.
Transport, 23(3), 183–193. https://doi.org/10.3846/1648-4142.2008.23.183-193

Brauers, W. K. M. (2002). The multiplicative representation for multiple objectives optimization with an application for arms procurement. Naval Research Logistics, 49, 327–340.
https://doi.org/10.1002/nav.10014

Chiang, T. Y., & Perng, Y. H. (2018). A new model to improve service quality in the property management industry. International Journal of Strategic Property Management, 22(5), 436–446. https://doi.org/10.3846/ijspm.2018.5226

Cochrane, J. (2011). Presidential address: discount rates. Journal of Finance, 66, 1047–1108.
https://doi.org/10.1111/j.1540-6261.2011.01671.x

Da Costa, M. B., Dos Santos, L. M. A. L., Schaefer, J. L., Baierle, I. C., & Nara, E. O. B. (2019). Industry 4.0 technologies basic network identification. Scientometrics, 121(2), 977–994. https://doi.org/10.1007/s11192-019-03216-7

Delbecq, A. L., & Van de Ven, A. H. (1971). A group process model for problem identification and program planning. The Journal of Applied Behavioral Science, 7(4), 466–492.
https://doi.org/10.1177/002188637100700404

Dimitrova, K. (2018). Modeling, measurement and management of business processes in organization. In 2nd International Scientific Conference Intelligent Information Technologies for Industry (pp. 410–419). Springer International Publishing. https://doi.org/10.1007/978-3-319-68324-9_45

Dutta, I., Dutta, S., & Raahemi, B. (2017). Detecting financial restatements using data mining techniques. Expert Systems with Applications, 90, 374–393. https://doi.org/10.1016/j.eswa.2017.08.030

Egmont-Petersen, M., Talmon, J. L., Hasman, A., & Amberge, A. W. (1998). Assessing the importance of features for multi-layer perceptrons. Neural Networks, 11(4), 623–635. https://doi.org/10.1016/S0893-6080(98)00031-8

ElMaraghy, H. A., & Wiendahl, H. P. (2009). Changeability–an introduction. In Changeable and reconfigurable manufacturing systems (pp. 3–24). Springer.
https://doi.org/10.1007/978-1-84882-067-8_1

Frydman, R., & Stillwagon, J. R. (2018). Fundamental factors and extrapolation in stock-market expectations: the central role of structural change. Journal of Economic Behavior & Organization, 148, 189–198. https://doi.org/10.1016/j.jebo.2018.02.017

GNUv3. (2018). GNU General Public License. https://www.gnu.org/licenses/gpl-3.0.en.html

Gonçalves, J. M., Ferreira, F. A., Ferreira, J. J., & Farinha, L. M. (2019). A multiple criteria group decision-making approach for the assessment of small and medium-sized enterprise competitiveness. Management Decision, 57(2), 480–500. https://doi.org/10.1108/MD-02-2018-0203

Greenwood, R., & Shleifer, A. (2014). Expectations of returns and expected returns. The Review of Financial Studies, 27(3), 714–746. https://doi.org/10.1093/rfs/hht082

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

Roiger, R. J. (2017). Data mining: a tutorial-based primer. CRC Press.

Safarzadeh, S., Khansefid, S., & Rasti-Barzoki, M. (2018). A group multi-criteria decision-making based on best-worst method. Computers & Industrial Engineering, 126, 111–121. https://doi.org/10.1016/j.cie.2018.09.011

Siahaan, A. P. U. (2018). Multi-objective optimization method by ratio analysis in determining results in decision support systems. International Journal for Innovative Research in Multidisciplinary Field, 4(10), 50–54. https://doi.org/10.31227/osf.io/yqjf3

Srinivasan, V., & Shocker, A. D. (1973). Linear programming techniques for multidimensional analysis of preferences. Psychometrika, 38(3), 337–369. https://doi.org/10.1007/BF02291658

Tamošaitienė, J., Šipalis, J., Banaitis, A., & Gaudutis, E. (2013). Complex model for the assessment of the location of highrise buildings in the city urban structure. International Journal of Strategic Property Management, 17(1), 93–109.
https://doi.org/10.3846/1648715X.2013.781968

Tang, J., Deng, C., & Huang, G. B. (2015). Extreme learning machine for multilayer perceptron. IEEE Transactions on Neural Networks and Learning Systems, 27(4), 809–821.
https://doi.org/10.1109/TNNLS.2015.2424995

Turskis, Z., & Juodagalvienė, B. (2016). A novel hybrid multicriteria decision-making model to assess a stairs shape for dwelling houses. Journal of Civil Engineering and Management, 22(8), 1078–1087. https://doi.org/10.3846/13923730.2016.1259179

Turskis, Z., Dzitac, S., Stankiuviene, A., & Šukys, R. (2019a). A fuzzy group decision-making model for determining the most influential persons in the sustainable prevention of accidents in the construction SMEs. International Journal of Computers, Communications & Control, 14(1), 90–106. https://doi.org/10.15837/ijccc.2019.1.3364

Turskis, Z., Urbonas, K., & Daniūnas, A. (2019b). A hybrid fuzzy group multi-criteria assessment of structural solutions of the symmetric frame alternatives. Symmetry, 11(2), 261.
https://doi.org/10.1142/S0219622016300019

Vatansever, K., & Kazançoğlu, Y. (2014). Integrated usage of fuzzy multi criteria decision making techniques for machine selection problems and an application. International Journal of Business and Social Science, 5(9), 12–24.

Wang, D., Wang, X., & Xia, N. (2018). How safety-related stress affects workers’ safety behavior: the moderating role of psychological capital. Safety Science, 103, 247–259.
https://doi.org/10.1016/j.ssci.2017.11.020

WEKA. (2018). Weka 3: data mining software in Java. Waikato University, New Zealand.
https://www.cs.waikato.ac.nz/ml/weka/

Wu, Y., Ke, Y., Zhang, T., Liu, F., & Wang, J. (2018). Performance efficiency assessment of photovoltaic poverty alleviation projects in China: a three-phase data envelopment analysis model. Energy, 159, 599–610. https://doi.org/10.1016/j.energy.2018.06.187

Xu, L., Fu, H. Y., Goodarzi, M., Cai, C. B., Yin, Q. B., Wu, Y., & She, Y. B. (2018). Stochastic cross validation. Chemometrics and Intelligent Laboratory Systems, 175, 74–81. https://doi.org/10.1016/j.chemolab.2018.02.008

Yazdani, M., Zarate, P., Zavadskas, E. K., & Turskis, Z. (2018). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501–2519. https://doi.org/10.1108/MD-05-2017-0458

Yazdani, M., Wen, Z., Liao, H., Banaitis, A., & Turskis, Z. (2019). A grey combined compromise solution (CoCoSo-G) method for supplier selection in construction management. Journal of Civil Engineering and Management, 25(8), 858–874. https://doi.org/10.3846/jcem.2019.11309

Zavadskas, E. K., Kaklauskas, A., Turskis, Z., & Kalibatas, D. (2009). An approach to multi-attribute assessment of indoor environment before and after refurbishment of dwellings. Journal of Environmental Engineering and Landscape Management, 17(1), 5–11. https://doi.org/10.3846/1648-6897.2009.17.5-11

Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision‐making. Technological and Economic Development of Economy, 16(2), 159–172. https://doi.org/10.3846/tede.2010.10

Zavadskas, E. K., Mardani, A., Turskis, Z., Jusoh, A., & Nor, K. M. (2016). Development of TOPSIS method to solve complicated decision-making problems – an overview on developments from 2000 to 2015. International Journal of Information Technology & Decision Making, 15(03), 645–682. https://doi.org/10.1142/S0219622016300019

Zavadskas, E. K., & Turskis, Z. (2011). Multiple criteria decision making (MCDM) methods in economics: an overview. Technological and Economic Development of Economy, 17(2), 397–427. https://doi.org/10.3846/20294913.2011.593291

Zavadskas, E. K., Turskis, Z., Dejus, T., & Viteikiene, M. (2007). Sensitivity analysis of a simple additive weight method. International Journal of Management and Decision Making, 8(5/6), 555–574. https://doi.org/10.1504/IJMDM.2007.013418