Modeling of neuro-fuzzy system as a support in decision-making processes

Authors

  • Darko Bozanic University of Defense in Belgrade, Military Academy, Serbia
  • Duško Tešić Military Academy, University of Defence in Belgrade, Belgrade, Serbia
  • Dragan Marinković Faculty of Mechanical Engineering and Transport Systems, TU Berlin, Berlin, Germany
  • Aleksandar Milić Military Academy, University of Defence in Belgrade, Belgrade, Serbia

DOI:

https://doi.org/10.31181/rme2001021222b

Keywords:

Neuro-Fuzzy System (ANFIS), MCDM, LMAW, AWRP

Abstract

In the paper is presented Neuro-Fuzzy System as a decision-making support in the selection of construction machines (the example of selecting a loader is provided). Construction characteristics of a loader make the basis for selection, but also other elements of importance. The data for Neuro-Fuzzy System modeling are prepared using the Multi-Criteria Decision Making (MCDM) methods: Logarithm Methodology of Additive Weights (LMAW), VIKOR, TOPSIS, MOORA and SAW. The paper also presents the method of aggregation of weights of rules premises (AWRP), which defines the key rules of Neuro-Fuzzy System. Finally, the training of the model is tested. The data for the selection of input variables and for model training are obtained by engaging experts

References

Ali, Z., Mahmood, T., Ullah, K., & Khan, Q. (2021). Einstein Geometric Aggregation Operators using a Novel Complex Interval-valued Pythagorean Fuzzy Setting with Application in Green Supplier Chain Management. Reports in Mechanical Engineering, 2(1), 105-134.

Bazzazi, A.A., Osanloo, M. & Karimi, B. (2011). Deriving preference order of open pit mines equipment through MADM methods: Application of modified VIKOR method. Expert Systems with Applications, 38(3), 2550-2556.

Božanić, D. (2017). Model of decision support in overcoming water obstacles in Army combat operations, (Only in Serbian: Model podrške odlučivanju pri savlađivanju vodenih prepreka u napadnoj operaciji Kopnene vojske). Doctoral dissertation, Belgrade: University of Defence in Belgrade, Military Academy.

Božanić, D., & Pamučar D. (2014). Making of Fuzzy Logic System rules base for decision making support by Aggregation of Weights of Rules Premises, Tehnika, 69 (1), 129-138.

Božanić, D., Karović, S., & Pamučar, D. (2014). Adaptive neural network for the selection of course of action as a prerequisite of the cost price estimate of an offensive army operation. Vojno delo, 66(4), 148-162.

Božanić, D., Milić, A., Tešić, D., Sałabun, W., & Pamučar, D. (2021). D numbers – FUCOM – fuzzy RAFSI model for selecting the Group of construction machines for enabling mobility. Facta Universitatis, Series: Mechanical Engineering, 19(3), 447-471

Božanić, D., Slavković, R., & Karović, S. (2015). Model of fuzzy logic application to the assessment of risk in overcoming the water obstacles during an army defensive operation. Vojno delo, 67(4), 240-260.

Brauers, W. K. M., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35(2), 443–468.

Byun, D.-H. (2001). The AHP approach for selecting an automobile purchase model. Information & Management. 38, 289-297.

Đenadić, S. P., Jovančić, P. D., Ignjatović, D. M., Miletić, F. M., & Janković, I. V. (2019). Analysis of the application of multi-criteria methods in optimizing the selection of hydraulic excavators on open-cast coal mining. Tehnika, 74(3), 369-377.

Deveci, M., Pamučar, D., Gokasar, I. (2021). Fuzzy Power Heronian function based CoCoSo method for the advantage prioritization of autonomous vehicles in real-time traffic management, Sustainable Cities and Society, 69, 102846.

Dombi, J.A. (1982). General class of fuzzy operators, the demorgan class of fuzzy operators and fuzziness measures induced by fuzzy operators. Fuzzy Sets and Systems, 8, 149–163.

Erdogan, N., Pamučar, D., Kucuksari, S., Deveci, M. (2021). An integrated multi-objective optimization and multi-criteria decision-making model for optimal planning of workplace charging stations, Applied Energy, 304, 117866.

Hara, T., Uchiyama, M., & Takahasi, S.E. (1998). A refinement of various mean inequalities. Journal of Inequalities and Applications, 2(4), 387-395.

Hristov, S. (1978). Organization of engineering works (Only in Serbian: Organizacija inžinjerijskih radova). Belgrade: VIZ.

Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: a state of the art survey: Lecture Notes in Economics and Mathematical Systems, vol. 186. Berlin: Springer-Verlag.

Ibrahimović, F. I., Kojić, S. L., Stević, Ž. R., & Erceg, Ž. J. (2019). Making an investment decision in a transportation company using an integrated FUCOM-MABAC model. Tehnika, 74(4), 577-584.

Jankovic, I., Djenadic, S., Ignjatovic, D., Jovancic, P., Subaranovic, T., & Ristovic, I. (2019). Multi-Criteria Approach for Selecting Optimal Dozer Type in Open-Cast Coal Mining. Energies, 12(12), 2245-2260.

Kizielewicz, B, Wątróbski, J, & Sałabun, W. (2020). Identification of Relevant Criteria Set in the MCDA Process—Wind Farm Location Case Study. Energies, 13(24), 6548.

Kun, M., Topaloglu, S., & Malli, T. (2013). Evaluation of Wheel Loaders in Open Pit Marble Quarrying by Using the AHP and Topsis Approaches. Archives of Mining Sciences, 58, 255-267.

Kuzmanović, B., Kovačević, M., & Terzić, M. (2017). Application of multi-criteria analysis methods (AHP and TOPSIS) in the procurement of the wideband radio receivers. Proceedings of the XLIV Symposium on Operational Research (SYM-OP-IS), Zlatibor, Serbia, 646-651.

Milićević, R. M., & Župac Ž. G. (2012a). An objective approach to determining criteria weights. Vojnotehnički glasnik/Military Technical Courier, 60(1), 39-56.

Milićević, R. M., & Župac Ž. G. (2012b). Subjective approach to the determination of criteria weights. Vojnotehnički glasnik/Military Technical Courier, 60(2), 48-70.

Opricović, S., & Gwo-Hshiung T. (2004). The Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445-455.

Pamučar, D. & Božanić, D. (2014). Overview of the new method for development of the base of the Fuzzy Logic Systems for decision making support rules - Premisa Rules Weight Aggregation method. Proceedings of the XLI Symposium on Operational Research (SYM-OP-IS), Divčibare, Serbia, 368-373.

Pamučar, D. (2020). Normalized weighted geometric Dombi Bonferroni mean operator with interval grey numbers: Application in multicriteria decision making. Reports in Mechanical Engineering, 1(1), 44-52.

Pamučar, D., & Savin, S. (2020). Multiple-criteria model for optimal off-road vehicle selection for passenger transportation: BWM-COPRAS model. Vojnotehnički glasnik/Military Technical Courier, 68(1), 28-64.

Pamučar, D., Božanić, D., & Komazec, N. (2016a). Risk Assessment of Natural Disasterts using Fuzzy Logic System Type-2. Management- Journal for Theory and Practice Management, 21(80), 23-32.

Pamučar, D., Božanić, D., & Milić, A. (2016b). Selection of a course of action by Obstacle Employment Group based on a fuzzy logic system. Yugoslav Journal of Operations Research, 26(1), 75-90.

Pamučar, D., Žižović, M., Biswas, S., & Božanić, D. (2021a). A new Logarithm Methodology of Additive Weights (LMAW) for multi-criteria decision-making: Application in logistics. Facta Universitatis, Series: Mechanical Engineering, 19(3), 361-380.

Pamučar, D., Deveci, M., Gokasar, I., Popović, M. (2021b). Fuzzy Hamacher WASPAS Decision-Making Model for Advantage Prioritization of Sustainable Supply Chain of Electric Ferry Implementation in Public Transportation. Environment, Development and Sustainability, 13, 1397.

Papić, Lj., & Milovanović, Z. (2007). System Maintainability and ReliabilityEdition. Prijevor: The research Center of Dependability and Quality Managament.

Pokorni, S. (2021). Current state of the application of artificial intelligence in reliability and maintenance. Vojnotehnički glasnik/Military Technical Courier, 69(3), 578-593.

Radoičić, G. (2009). Implementation of methods for multicriteria decision in the sample of public purchase from practice. Journal of Applied Engineering Science, 7(23-24), 15-26.

Rahimdel, M.J., & Bagherpour, R. (2018). Haulage system selection for open pit mines using fuzzy MCDM and the view on energy saving. Neural Computing and Applications, 29, 187–199.

Riaz, M., Farid, H.M.A., Aslam, M., Pamučar, D., & Božanić, D. (2021). Novel Approach for Third-Party Reverse Logistic Provider Selection Process under Linear Diophantine Fuzzy Prioritized Aggregation Operators. Symmetry, 13(7), 1152.

Sałabun, W. (2015). The Characteristic Objects Method: A New Distance-based Approach to Multicriteria Decision-making Problems. Journal of Multi-Criteria Decision Analysis, 22, 37-50.

Sałabun, W., Shekhovtsov, A., Pamučar, D., Wątróbski, J., Kizielewicz, B., Więckowski, J., Bozanić, D., Urbaniak, K., & Nyczaj, B. (2020). A Fuzzy Inference System for Players Evaluation in Multi-Player Sports: The Football Study Case. Symmetry, 12(12), 2029, 1-49.

Sathyapriya, S., Bavya Seeli, P., & Swathi, R. (2018). Selection of cars using combined multi criteria decision making techniques. International Journal of Creative Research Thoughts (IJCRT), 6(1), 82-841.

Satoglu, S.I., & Türkekul, İ. (2021). Selection of Material Handling Equipment using the AHP and MOORA. Jurnal Teknik Industri, 22(1), 113-124.

Srikrishna, S., Reddy, S., & Vani, S.G. (2014). A New Car Selection in the Market using TOPSIS Technique. International Journal of Engineering Research and General Science, 2(4), 177-181.

Subotić, M., Radičević, V., Pavlović, Z., & Ćirović, G. (2021). Development of a New Risk Assessment Methodology for Light Goods Vehicles on Two-Lane Road Sections. Symmetry, 13(7), 1271.

Ulkhaq, M.M., Wismar, R., Wijayanti, M., Zain, S., Baskara, E., & Leonita, W. (2018). Combining the AHP and TOPSIS to evaluate car selection. Proceedings of the 2nd International Conference on High Performance Compilation, Computing and Communications (HP3C). Association for Computing Machinery, New York, NY, USA, 112–117.

Waris, M., Panigrahi, S., Mengal, A., Soomro, M., Mirjat, N., Ullah, M., Azlan, Z., & Khan, A. (2019). An Application of Analytic Hierarchy Process (AHP) for Sustainable Procurement of Construction Equipment: Multicriteria-Based Decision Framework for Malaysia. Mathematical Problems in Engineering, 2019, 1-20.

Yavuz, M. (2015). Equipment selection based on the AHP and Yager's method. Journal of the Southern African Institute of Mining and Metallurgy, 115(5), 425-433.

Yayla, A.Y, & Yildiz, A. (2013). Fuzzy Analitic Network Process based Multi Criteria Decision Making methodology for a family automobile purchasing decision. South African Journal of Industrial Engineering, 24, 167-180.

Yuan, Y., & Suarga, S. (1995). On the Integration of Neural Networks and Fuzzy Logic Systems, International Conference on Systems, Man and Cybernetics, Vancouver, Canada, 452-456.

Zapata C.J.A., Arango, S.M.D., & Moreno, M.S.P. (2012). Expertchoice© decision support system implementation for vehicle selection in a colombian shipyard. Dyna, 79(173), 53-60.

Zavadskas, E. K., & Kaklauskas, A. (1996). Determination of an efficient contractor by using the new method of multicriteria assessment. International Symposium for “The Organisation and Management of Construction”. Shaping Theory and Practice Vol. 2 (pp. 94-104). Bury St. Edmunds, Su

olk: St. Edmudsbury Press.

Zhang, F., Ju, Y., Santibanez-Gonzalez, E.D., & Wang, A. (2020). SNA-based multi-criteria evaluation of multiple construction equipment: A case study of loaders selection. Advanced Engineering Informatics, 44, 101056.

Zionts, S., & Wallenius, J. (1983). An interactive multiple objective linear programing method fora a class of underlying nonlinear utility fuctions. Management Science, 29(5), 519-529.

Zulqarnain, R. M., Siddique, I., Ali, R., Pamučar, D., Marinković, D., & Božanić, D. (2021). Robust Aggregation Operators for Intuitionistic Fuzzy Hypersoft Set With Their Application to Solve MCDM Problem. Entropy, 23(6), 688.

Published

2021-11-10

How to Cite

Bozanic, D., Tešić, D., Marinković, D., & Milić, A. (2021). Modeling of neuro-fuzzy system as a support in decision-making processes. Reports in Mechanical Engineering, 2(1), 222–234. https://doi.org/10.31181/rme2001021222b