Public Procurement Crisis of Iraq and its Impact on Construction Projects
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Abstract
The public procurement crisis in Iraq plays a fundamental role in the delay in the implementation of construction projects at different stages of project bidding (pre, during, and after). The procurement system of any country plays an important role in economic growth and revival. The paper aims to use the fuzzy logic inference model to predict the impact of the public procurement crisis (relative importance index and Likert scale) was carried out at the beginning to determine the most important parameters that affect construction projects, the fuzzy analytical hierarchy process (FAHP) to set up, and finally, the fuzzy decision maker's (FDM) verification of the parameter for comparison with reality. Sixty-five construction projects in Iraq have been selected, and the most crucial crisis variables were used for calculating the weights and their importance, using the fuzzy logic inference model to verify the crisis parameters and the extent of their impact in preparation for predicting the mathematical model of public procurement parameters. After the algorithm had been completed, it was noted that the fast, messy genetic algorithm produced a little difference between training and testing (0.012% and 0.0057%), which is more reliable for predicting mean results from models. The paper’s major conclusion is that 18 crisis factors in public procurement through different stages affect construction projects in Iraq.
Article received: 08/03/2023
Article accepted: 03/05/2023
Article published: 01/02/2024
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Abbas, N.N., and Burhan, A.M., 2023. Evaluation of the current status of the cost control processes in Iraqi construction projects. Journal of Engineering, 29(1), pp. 128-144. Doi:10.31026/j.eng.2023.01.08.
Abbasnia, R., Afshar, A., and Eshtehardian E., 2008. Time-cost trade off problem in construction project management, based on fuzzy logic. Journal of Applied Sciences, 822, pp. 4159-4165. Doi: 10.3923/jas.2008.4159.4165
Adeli, H., and Wu, M., 2018. Regularization neural network for construction cost estimation. Journal of Construction Engineering and Management, 124(1), pp. 18–24. Doi:10.1061/(ASCE)CO.1943-7862.0001570
Alroomi, A., Jeong, D.H.S., and Oberlender, G.D., 2016. Analysis of cost-estimating competencies using criticality matrix and factor analysis. Journal of Construction Engineering and Management .138(11), pp. 1270–1280. Doi:10.1061/(ASCE)CO.1943-7862.0000351
Altaie, M., and Borhan, A., 2019. Using Neural Network model to estimate the optimum time for repetitive construction projects in Iraq. Association of Arab Universities Journal of Engineering Sciences, 25(5), pp. 100-114. https://jaaru.org/index.php/auisseng/article/view/225
Araque, F., Carrasco, R., Salguero, A.G., Vila, A., and Martínez, L., 2008. Fuzzy extended dependencies to support decision-making in project management. Journal of Multiple-Valued Logic & Soft Computing, 14, pp. 435-4355
Attalla, M., and Hegazy, T., 2003. Predicting cost deviation in reconstruction projects: artificial neural networks versus regression. Journal of Construction Engineering and Management, 129(4), pp. 405–411.Doi:10.1061/(ASCE)0733-9364(2003)129:4(405)
Cheng, M.Y., Hoang, N.D., Roy, A.F., and Wu, Y.W. 2012. A novel time-depended evolutionary fuzzy SVM inference model for estimating construction project at completion. Engineering Applications of Artificial Intelligence, 25(4), pp. 744-752. Doi:10.1016/j.engappai.2011.09.022.
Ding, C., and He, X., 2004. K-means clustering via principal component analysis. Proceedings of the twenty- First international conference on Machine learning, (P. 29). Doi:10.1145/1015330.1015408.
Hosseini, S.M., Soltanpour, Y., and Paydar, M.M., 2022. Applying the Delphi and fuzzy DEMATEL methods for identification and prioritization of the variables affecting Iranian citrus exports to Russia. Soft Computing, pp. 1-14. Doi:10.1007/s00500-022-06738-0
Kamalabadi, I.N., Mirzaei, A.H., and Javadi, B., 2007. A possibility linear programming approach to solve a fuzzy single machinescheduling problem. Journal of Industrial and Systems Engineering, 1(2), pp. 116 -129.
Kuo, Y. C., and Lu, S. T., 2013. Using fuzzy multiple criteria decision-making approach to enhance risk assessment for metropolitan construction projects. International Journal of Project Management, 31(4), pp. 602-614. Doi:10.1016/j.ijproman.2012.10.003.
Liang, T. F., 2006. Project management decisions using fuzzy linear programming. International Journal of Systems Science, 37(15), pp. 1141–1152. Doi:10.1080/00207720601014396
Lin, F., 2008. Time-cost trade-off problem based on confidence-interval estimates and level (1 − α) fuzzy numbers. IEEE International Conference on Systems, Man and Cybernetics, 12-15 October 2008, Singapore. Doi:10.1109/ICSMC.2008.4811343
Mahjoob, A.M.R., Wali, M.R., and Atyah, H.A., 2016. Exploring the factors affecting the elemental cost estimation with relationship analysis using AHP. Journal of Engineering, 22(8), pp. 1–13. Doi:10.31026/j.eng.2016.08
Marsh, K., and Fayek, A.R., 2010. Surety Assist: Fuzzy expert system to assist surety underwriters in evaluating construction contractors for bonding. Journal of Construction Engineering and Management, 136(11), ASCE. Doi:10.1061/(ASCE)CO.1943-7862.0000224
Nassar, Y.S., Erzaij, K.R., 2022. Building and analyzing a crisis management model using Fuzzy DEMATEL technique. Journal Of Algebraic Statistics, 13(2), pp. 2664-2681.
Noori, H., and Rasheed, S., 2023. Procurement Management of Power Plants Construction Projects in Iraq. Journal of Engineering, 29(2), pp. 37-58. Doi:10.31026/j.eng.2023.02.03
Plebankiewicz, E., 2009. Contractor prequalification model using fuzzy sets. Journal of Civil Engineering and Management, 15(4), pp. 377–385. Doi:10.3846/1392-3730.2009.15.377-385
Rajan, A., 2006. Automated requirements-based test case generation. ACM SIGSOFT Software Engineering Notes, 31(6), pp. 1-2. Doi:10.1145/1218776.1218799.
Rashidi A., Jazebi, F., and Brilakis, L., 2011. Neuro-fuzzy Genetic System for Selection of Construction Project Managers. Journal of Construction. Doi:10.1061/(ASCE)CO.1943-7862.0000200
Saifan, A. A., Alsukhni, E., Alawneh, H., and Sbaih, A. A., 2016. Test case reduction using data mining technique. International Journal of Software Innovation (IJSI), 4(4), pp. 56-70. Doi:10.4018/IJSI.2016100104.
Serbane, L., 2013. Small business research – Policy parameters. International Small Business Journal: Researching Entrepreneurship, 28(1), pp. 43–64. Doi:10.1177/0266242609350804
Shankar N.R., Sireesha V., and Rao P.P.B., 2010. Critical path analysis in the fuzzy project network Advances in Fuzzy Mathematics, 5(3), pp. 285–294. https://www.researchgate.net/publication/215527904
Shaw, J.L., Dockery, C.R., Lewis, S.E., Harris, L., and Bettis, R., 2009. Definition and formulation of scientific prediction and its role in inquiry-based laboratories. Journal of Chemical Education, 86, pp. 1416– 1418. Doi:10.1021/ed1006508
Smith, N.J., Merna, T., and Jobling, P., 2014. Managing risk in construction projects. John Wiley and Sons. Doi:10.5772/51460
Soltani, A., and Haji, R., 2007. A project scheduling method based on fuzzy theory. Journal of Industrial and Systems Engineering, 1(1), pp. 70-80.
Van, S.S., 2022. Stochastic model For Simulating construction projects. Ph.D. thesis in construction management, University of Ginim. Ghana. pp. 214 – 244. Doi:10.1111/j.1467-8667.1993.tb00220.x
Yang, X., Song, Q., and Wang, Y., 2007. A weighted support vector machine for data classification. International Journal of Pattern Recognition and Artificial Intelligence, 21(05), pp.961-976. Doi:10.1142/S0218001407005703.
Yin, Z., and Hou, J., 2016. Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes. Neurocomputing, 174, pp. 643-650. Doi:10.1016/j.neucom.2015.09.081.
Zahraie B., and Tavakolan M., 2009. Stochastic time-cost-resourceutilization optimization using non-dominated sorting genetic algorithm and discrete fuzzy sets. Journal of Construction Engineering and Management, 135(11), ASCE. Doi:10.1061/(ASCE)CO.1943-7862.00000
Zhang, A., Lipton, Z.C., Li, M., and Smola, A.J., 2021. Dive into deep learning. arXiv preprint arXiv:2106.11342. Doi:10.48550/arXiv.2106.11342.
Zhang, H., Tam, C.M., and Li H., 2005. Modeling uncertain activity duration by fuzzy number and discrete-event simulation. European Journal of Operational Research, 164 , pp. 715–729. Doi:10.1016/j.ejor.2004.01.035