Application of neuro-fuzzy system for predicting the success of a company in public procurement

  • Dragan Pamučar Military academy, University of defence in Belgrade, Belgrade, Serbia
  • Darko Bozanic Military academy, University of defence in Belgrade, Belgrade, Serbia
  • Adis Puška Faculty of Agriculture, Bijeljena University, Bijeljina, Bosnia and Herzegovina
  • Dragan Marinković Department of Structural Analysis, Technical University of Berlin, Germany
Keywords: Fuzzy Sets, Neuro-fuzzy system, Artificial Bee Colony


The paper presents a neuro-fuzzy system for evaluating and predicting the success of a construction company in public tenders. This model enables companies to operate sustainably by assessing their own position in the market. The model was based on data from a seven-year study, where data from the first six years were used to adjust the model, while data from the last year of the study were used for testing and validation. The neuro-fuzzy model was tuned using the Artificial Bee Colony algorithm.


Download data is not yet available.


Adil, M., Nunes, M.B., & Peng, G.C. (2014). Identifying operational requirements to select suitable decision models for a public sector e-procurement decision support system. Journal of Information Systems and Technology Management, 11, 211-228. DOI:

Amid, A., Ghodsypour, S. H., & O’Brien, C. (2011). A weighted max-min model for fuzzy multi-objective supplier selection in a supply chain. International Journal of Production Economics, 131(1), 139–145. DOI:

Amin, S. H., & Zhang, G. (2012). An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert Systems and Applications, 39(8), 6782–6791. DOI:

Bana e Costa, C. A., Lourenço, J.C., Chagas, M. P., & Bana e Costa, J. C. (2007)., Development of reusable bid evaluation models for the Portuguese Electric Transmission Company. Operational Research working papers, LSEOR 07.98. Operational Research Group, Department of Management, London School of Economics and Political Science, London, UK.

Bhattacharya, A., Geraghty, J., & Young, P. (2010). Supplier selection paradigm: An integrated hierarchical QFD methodology under multiple-criteria environment. Applied Soft Computing Journal, 10(4), 1013–1027. DOI:

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. DOI:

Božanić, D., Milić, A., Tešić, D., Salabun, W., & Pamučar, D. (2021b). 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. DOI:

Božanic, D., Tešić, D., Marinković, D., & Milić, A. (2021a). Modeling of neuro-fuzzy system as a support in decision-making processes. Reports in Mechanical Engineering, 2(1), 222-234. DOI:

Buyukozkan, G., & Cifci, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), 3000–3011. DOI:

Chai J., Liu N.K.J., & Ngai E. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Systems with Applications, 40(10), 3872–3885. DOI:

Chan, F. T. S., & Chan, H. K. (2010). An AHP model for selection of suppliers in the fast changing fashion market. International Journal of Advanced Manufacturing Technology, 51(9–12), 1195–1207. DOI:

Chua, S. J. L., Ali, A. S., & Alias, A. B. (2015). Implementation of Analytic Hierarchy Process (AHP) decision making framework for building maintenance procurement selection: Case study of Malaysian public universities. Eksploatacja i Niezawodnosc –Maintenance and Reliability, 17 (5), 7–18. DOI:

Crispim, J. A., & De Sousa, J. P. (2010). Partner selection in virtual enterprises. International Journal of Production Research, 48(3), 683–707. DOI:

De Boer, L., Labro, E., & Morlacchi, P. (2001). A review of methods supporting supplier selection. European Journal of Purchasing and Supply Management, 7(2), 75–89. DOI:

Del Giudice, M., Garcia-Perez, A., Scuotto, V., & Orlando, B. (2019). Are social enterprises technological innovative? A quantitative analysis on social entrepreneurs in emerging countries. Technological Forecasting and Social Change, 148, 119704.

Dickson, G. W. (1966). An analysis of vendor selection system and decisions. Journal of Purchasing, 2(1), 28–41. DOI:

Dobi, K., Gugić, J., & Kancijan, J. (2010). AHP As a Decision Support Tool in the Multi criteria Evaluation of Bids in Public Procurement, Proceedings of the ITI 2010 Int. Conf. on Information Technology Interfaces, 32, 447- 452.

Dotoli, M., Epicoco, N. & Falagario, M. (2020). Multi-Criteria Decision Making techniques for the management of public procurement tenders: A case study. Applied Soft Computing Journal, 88, 106064.

Falagario, M., Sciancalepore, F., Costantino, B., & Pietroforte, R. (2012). Using a DEA cross efficiency approach in public procurement tenders. European Journal of Operational Research, 218(2), 523–529. DOI:

Feng, B., Fan, Z., & Li, Y. (2011). A decision method for supplier selection in multiservice outsourcing. International Journal of Production Economics, 132(2), 240–250. DOI:

Ferreira, L., & Borenstein, D. (2012). A fuzzy-bayesian model for supplier selection.. Expert Systems with Applications, 39(9), 7834–7844. DOI:

Gharib, M. R. (2020). Comparison of robust optimal QFT controller with TFC and MFC controller in a multi-input multi-output system. Reports in Mechanical Engineering, 1(1), 151-161. DOI:

Ghazinoory, S., Esmail Zadeh, A., & Kheirkhah A. S. (2010). Application of fuzzy calculations for improving portfolio matrices. Inf Sci (Ny), 180, 1582–1590. DOI:

Guneri, A. F., Ertay, T., & Yucel, A. (2011). An approach based on ANFIS input selection and modeling for supplier selection problem. Expert Systems with Applications, 38(12), 14907–14917. DOI:

Hanák, T., Drozdová, A., Marović, I. (2021). Bidding Strategy in Construction Public Procurement: A Contractor’s Perspective. Buildings. 11(2), 47.

Ho, W., Dey, P. K., & Lockström, M. (2011). Strategic sourcing: A combined QFD and AHP approach in manufacturing. Supply Chain Management, 16(6), 446–461. DOI:

Hosseini, S., & Barker, K. (2016). A Bayesian network model for resilience-based supplier selection. International Journal of Production, Economics, 180, 86-87. DOI:

Ishizaka, A., Pearman, C., & Nemery, P. (2012). AHP Sort: An AHP-based method for sorting problems. International Journal of Production Research, 50(17), 4767–4784. DOI:

Jokić, Ž., Božanić, D., & Pamučar, D. (2021). Selection of fire position of mortar units using LBWA and Fuzzy MABAC model. Operational Research in Engineering Sciences: Theory and Applications, 4(1), 115-135. DOI:

Kahraman, C., Cebeci, U., & Ulukan, Z. (2003). Multi-criteria supplier selection using fuzzy AHP. Logistics Information Management, 16, 382 – 394. DOI:

Karamaşa, Ç., Ergün, M., Gülcan, B., Korucuk, S., Memiş, S., & Vojinović, D. (2021). Rankıng value-creatıng green approach practıces ın logıstıcs companıes operatıng ın the TR A1 regıon and choosıng ıdeal green marketıng strategy. Operational Research in Engineering Sciences: Theory and Applications, 4(3), 21-38. DOI:

Kuo, R.J., Hsu, C.W., &. Chen, Y.L. (2015). Integration of fuzzy ANP and fuzzy TOPSIS for evaluating carbon performance of suppliers. International Journal of Environmental Science and Technology, 12(5), 3863-3876. DOI:

Labib, A. W. (2011). A supplier selection model: A comparison of fuzzy logic and the analytic hierarchy process. International Journal of Production Research, 49(21), 6287–6299. DOI:

Levary, R. R. (2008). Using the analytic hierarchy process to rank foreign suppliers based on supply risks. Computers and Industrial Engineering, 55(2), 535–542. DOI:

Liu, P., & Zhang, X. (2011). Research on the supplier selection of a supply chain based on entropy weight and improved ELECTRE-III method. International Journal of Production Research, 49(3), 637–646. DOI:

Ltifi, H., Benmohamed, E., Kolski, C., & Ayed, M.B. (2016). Enhanced visual data mining process for dynamic decision-making. Knowledge-Based Systems, 112, 66–181. DOI:

Mafakheri, F., Breton, M., & Ghoniem, A. (2011). Supplier selection-order allocation: A two-stage multiple criteria dynamic programming approach. International Journal of Production Economics, 132(1), 52–57. DOI:

Maybeck, P. S. (1979). Stohastic models, estimation and control, Vol. 1, New York: Academic Press.

Mockler, R. J. (1972). The Management Control Process, New York: Appleton-Century-Crofts.

Moore, D. L., & Fearon, H. E. (1973). Computer-assisted decision-making in purchasing. Journal of Purchasing, 9(1), 5–25. DOI:

Pająk, M. (2020). Fuzzy model of the operational potential consumption process of a complex technical system. Facta Universitatis, Series: Mechanical Engineering, 18(3), 453-472

Pamucar, D., & Bozanic, D. (2018). Decision-support system for the prediction of performance of construction consulting companies in public tenders, 377-395, in: Stević, Ž., Vukić, M., & Lukovac, V. (ed.), Procedings of 2nd International Conference on Management, Engineering and Environment (ICMNEE), Obrenovac, Serbia.

Pamučar, D., & Božanić, D. (2018). Decision-support system for the prediction of performance of construction consulting companies in public tenders, 377-395, in: Stević, Ž., Vukić, M., & Lukovac, V. (ed.), Procedings of 2nd International Conference on Management, Engineering and Environment (ICMNEE), Obrenovac, Serbia.

Pamučar, D., Božanić, D., & Komazec, N. (2016a). Risk assessment of natural disasters using fuzzy logic system of type 2. Management- Journal for Theory and Practice Management, 21(80), 23-34. DOI:

Pamučar, D., Božanić, D., & Kurtov, D. (2016b). Fuzzification of the Saaty's scale and a presentation of the hybrid fuzzy AHP-TOPSIS model: An example of the selection of a brigade artillery group firing position in a defensive operation. Military technical courier, 64(4), 966-986. DOI:

Pamučar, D., Božanić, D., & Milić, A. (2016c). 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. DOI:

Pamučar, D., Ljubojević, S., Kostadinović, D., & Đorović, B. (2016d). Cost and Risk aggregation in multi-objective route planning for hazardous materials transportation - A neuro-fuzzy and artificial bee colony approach. Expert Systems with Applications, 65, 1-15. DOI:

Puška, A., Beganović, A., & Šadić, S. (2018). Model for investment decision making by applying the multi-criteria analysis method. Serbian Journal of Management, 13(1), 7-28. DOI:

Razmi, J., Rafiei, H., & Hashemi, M. (2009). Designing a decision support system to evaluate and select suppliers using fuzzy analytic network process. Computers and Industrial Engineering, 57(4), 1282–1290. DOI:

Rezaei, J., & Davoodi, M. (2012). A joint pricing, lot-sizing, and supplier selection model. International Journal of Production Research, 50(16), 4524–4542. DOI:

Rezaei, J., Nispeling, T., Sarkis, J., & Tavasszy, L. (2016). A supplier selection life cycle approach integrating traditional and environmental criteria using the Best Worst Method. Journal of Cleaner Production, 135, 577–588. DOI:

Rietveld, J., & Schilling, M. A. (2020). Platform Competition: A Systematic and Interdisciplinary Review of the Literature. Journal of Management, 47(6), 1528–1563.

Rodríguez, M. J. G., Montequín, V. R., Fernández, F. O., & Balsera, J. M. V. (2020). Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain. Complexity, 2020, 8858258.

Sameh, K., Noufal, M., & Abdalah, S. (2016). A fuzzy-AHP multi-criteria decision making model for procurement process. International Journal of Logistics Systems and Management, 23 (1), 1-24. DOI:

Sen, C. G., Sen, S., & Baslgil, H. (2010) Pre-selection of suppliers through an integrated fuzzy analytic hierarchy process and max-min methodology. International Journal of Production Research, 48(6), 1603–1625. DOI:

Singh, M.G. (1980). Dynamical Hierarchical Control. Amsterdam: North-Holland.

Son, H., & Kim, C. (2015). Early prediction of the performance of green building projects using pre-project planning variables: data mining approaches. Journal of Cleaner Production, 109, 144-151. DOI:

Tešić, D., Božanić, D., Pamučar, D., & Din, J. (2022). DIBR - Fuzzy MARCOS model for selecting a location for a heavy mechanized bridge. Military technical courier, 70(2), 314-339.

Vahdani, B., & Zandieh, M. (2010). Selecting suppliers using a new fuzzy multiple criteria decision model: The fuzzy balancing and ranking method. International Journal of Production Research, 48(18), 5307–5326. DOI:

Yu, M., Goh, M., & Lin, H. (2012). Fuzzy multi-objective vendor selection under lean procurement. European Journal of Operational Research, 219(2), 305–311. DOI:

Zak, J. (2015). Comparative Analysis of Multiple Criteria Evaluations of Suppliers in Different Industries. Transportation Research Procedia, 10, 809-819. DOI:

Zhang, Z., & Liao, H. (2022). A stochastic cross-efficiency DEA approach based on the prospect theory and its application in winner determination in public procurement tenders. Annals of Operations Research,

Zhao, H., & Guo, S. (2014). Selecting Green Supplier of Thermal Power Equipment by Using a Hybrid MCDM Method for Sustainability. Sustainability, 6(1), 217-235. DOI:

Zhun, J., Yu, J. Z., Haghighat, F., Benjamin C. M., & Fung, C. M. B (2016). Advances and challenges in building engineering and data mining applications for energy-efficient communities. Sustainable Cities and Society, 25, 33–38. DOI:

How to Cite
Pamučar, D., Bozanic, D., Puška, A., & Marinković, D. (2022). Application of neuro-fuzzy system for predicting the success of a company in public procurement . Decision Making: Applications in Management and Engineering, 5(1), 135-153.