Bidding Decision-Support Model for Construction Projects Based on Projection Pursuit Learning Method
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 7
Abstract
The decision of whether or not to bid is a crucial task for contractors in the construction industry because multiple attributes with the features of uncertainty and ambiguity can enhance the complexity and difficulty of decision-making. Meanwhile, traditional methods for bidding decisions primarily depend on experience and the subjective evaluation of decision-makers, and it is difficult to develop a more reliable model through data-driven techniques when the historical bidding data is limited. Therefore, this paper proposes a bidding decision-support model for construction projects using the projection pursuit learning method (PPLM), which can explain the characteristic rules in the bidding of contractors through the digging of information of previous bids and help one to make a reasonable and effective decision for bidding a new project. Finally, a case study is provided to demonstrate the applicability of the novel model. The results indicate that the method developed based on the training set can correctly classify 10 testing projects and has a better performance in fitting and predicting the accuracy of bidding decisions when compared with other approaches.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to sincerely thank the Science Foundation of Sichuan University (Grant No. 2018hhs-54) and the National Natural Science Foundation of China (Grant No. 72002152). We also appreciate the original data support from Sonmez and Sözgen’s (2017) study.
References
Al-Humaidi, H. M. 2016. “Construction projects bid or not bid approach using the fuzzy technique for order preference by similarity FTOPSIS method.” J. Constr. Eng. Manage. 142 (12): 4016068. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001180.
Aznar, B., E. Pellicer, S. Davis, and P. Ballesteros-Perez. 2017. “Factors affecting contractor’s bidding success for international infrastructure projects in Australia.” J. Civ. Eng. Manage. 23 (7): 880–889. https://doi.org/10.3846/13923730.2017.1341955.
Chisala, M. L. 2017. “Quantitative bid or no-bid decision-support model for contractors.” J. Constr. Eng. Manage. 143 (12): 4017088. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001407.
Chou, J., C. W. Lin, A. D. Phan, and J. Shao. 2015. “Optimized artificial intelligence models for predicting project award price.” Autom. Constr. 54 (Jun): 106–115. https://doi.org/10.1016/j.autcon.2015.02.006.
Chou, J. S., A. D. Phan, and H. Wang. 2013. “Bidding strategy to support decision-making by integrating fuzzy AHP and regression-based simulation.” Autom. Constr. 35 (Nov): 517–527. https://doi.org/10.1016/j.autcon.2013.06.007.
Chua, D. K. H., D. Z. Li, and W. T. Chan. 2001. “Case-based reasoning approach in bid decision making.” J. Constr. Eng. Manage. 127 (1): 35–45. https://doi.org/10.1061/(ASCE)0733-9364(2001)127:1(35).
Dias, W. P. S., and R. L. D. Weerasinghe. 1996. “Artificial neural networks for construction bid decisions.” Civ. Eng. Environ. Syst. 13 (3): 239–253. https://doi.org/10.1080/02630259608970200.
Ding, X., Z. Sheng, and H. Liu. 2017. “A two-stage method for mega projects bidding system based on fuzzy analytic hierarchy process and gale-shapely strategy.” Chin. J. Manage. Sci. 25 (2): 147–154. https://doi.org/10.16381/j.cnki.issn1003-207x.2017.02.016.
Egemen, M., and A. Mohamed. 2008. “SCBMD: A knowledge-based system software for strategically correct bid/no bid and mark-up size decisions.” Autom. Constr. 17 (7): 864–872. https://doi.org/10.1016/j.autcon.2008.02.013.
El-Mashaleh, M. S. 2010. “Decision to bid or not to bid: A data envelopment analysis approach.” Can. J. Civ. Eng. 37 (1): 37–44. https://doi.org/10.1139/L09-119.
El-Mashaleh, M. S. 2013. “Empirical framework for making the bid/no-bid decision.” J. Manage. Eng. 29 (3): 200–205. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000147.
Friedman, J. H., and J. W. Tukey. 1974. “A projection pursuit algorithm for exploratory data analysis.” IEEE Trans. Comput. 23 (9): 881–890. https://doi.org/10.1109/T-C.1974.224051.
Hatush, Z., and M. Skitmore. 1997. “Evaluating contractor prequalification data: Selection criteria and project success factors.” Constr. Manage. Econ. 15 (2): 129–147. https://doi.org/10.1080/01446199700000002.
Huang, D., and L. Ke. 2012. “Application of ELECTRE-II on bid evaluation in construction engineering.” In Proc., 2012 IEEE Fifth Int. Conf. on Advanced Computational Intelligence, 816–819. Berlin: Springer.
Hwang, C. L., and K. P. Yoon. 1981. Multiple attribute decision making—Methods and application: A state of the art survey. Berlin: Springer.
Hwang, J. N., S. R. Lay, M. Maechler, R. D. Martin, and J. Schimert. 1994. “Regression modeling in back-propagation and projection pursuit learning.” IEEE Trans. Neural Networks 5 (3): 342–353. https://doi.org/10.1109/72.286906.
Hwang, J. S., and Y. S. Kim. 2016. “A bid decision-making model in the initial bidding phase for overseas construction projects.” KSCE J. Civ. Eng. 20 (4): 1189–1200. https://doi.org/10.1007/s12205-015-0760-y.
Kennedy, L. M., and M. Basu. 2000. “Application of projection pursuit learning to boundary detection and deblurring in images.” Pattern Recognit. 33 (12): 2019–2031. https://doi.org/10.1016/S0031-3203(99)00188-0.
Kumar, J. K., and S. V. Raj. 2020. “Bid decision model for construction projects—A review.” Mater. Today Proc. 22 (3): 688–690. https://doi.org/10.1016/j.matpr.2019.09.144.
Lesniak, A., and A. Radziejowska. 2017. “Supporting bidding decision using multi-criteria analysis methods.” Procedia Eng. 208 (Jan): 76–81. https://doi.org/10.1016/j.proeng.2017.11.023.
Leśniak, A. 2016. “Supporting contractors’ bidding decision: RBF neural networks application.” In Proc, 2015 Int. Conf. of Numerical Analysis and Applied Mathematics, 1–5. College Park, MD: American Institute of Physics.
Li, G., G. Zhang, C. Chen, and I. Martek. 2020. “Empirical bid or no bid decision process in international construction projects: Structural equation modeling framework.” J. Constr. Eng. Manage. 146 (6): 4020050. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001830.
Li, H., Y. Zou, X. Huang, R. Jiang, X. Li, X. Du, and Y. Liu. 2017. “Projection pursuit learning network algorithm for plant classification.” In Proc., 2017 IEEE Int. Symp. on Geoscience and Remote Sensing, 838–841. New York: IEEE.
Lowe, D. J., and J. Parvar. 2004. “A logistic regression approach to modelling the contractor’s decision to bid.” Constr. Manage. Econ. 22 (6): 643–653. https://doi.org/10.1080/01446190310001649056.
Marzouk, M., and E. Mohamed. 2018. “Modeling bid/no bid decisions using fuzzy fault tree.” Constr. Innovation 18 (1): 90–108. https://doi.org/10.1108/CI-11-2016-0060.
Oke, A., A. Omoraka, and A. Olatunbode. 2020. “Appraisal of factors affecting bidding decisions in Nigeria.” Int. J. Constr. Manage. 20 (2): 169–175. https://doi.org/10.1080/15623599.2018.1484846.
Plebankiewicz, E., K. Zima, and D. Wieczorek. 2016. “Life cycle cost modelling of buildings with consideration of the risk.” Arch. Civ. Eng. 62 (2): 149–166. https://doi.org/10.1515/ace-2015-0071.
Polat, G. 2016. “Subcontractor selection using the integration of the AHP and PROMETHEE methods.” J. Civ. Eng. Manage. 22 (8): 1042–1054. https://doi.org/10.3846/13923730.2014.948910.
Polat, G., B. N. Bingol, and E. Uysalol. 2014. “Modeling bid/no bid decision using adaptive neuro fuzzy inference system (ANFIS): A case study.” In Proc., 2014 Construction Research Congress, 1083–1092. Reston, VA: ASCE.
Quintian, H., and E. Corchado. 2017. “Beta Hebbian learning as a new method for exploratory projection pursuit.” Int. J. Neural Syst. 27 (6): 17500246. https://doi.org/10.1142/S0129065717500241.
Seydel, J., and D. L. Olson. 1990. “Bids considering multiple criteria.” J. Constr. Eng. Manage. 116 (4): 609–623. https://doi.org/10.1061/(ASCE)0733-9364(1990)116:4(609).
Shafahi, A., and A. Haghani. 2014. “Modeling contractors’ project selection and markup decisions influenced by eminence.” Int. J. Project Manage. 32 (8): 1481–1493. https://doi.org/10.1016/j.ijproman.2014.01.013.
Shash, A. A. 1993. “Factors considered in tendering decisions by top UK contractors.” Constr. Manage. Econ. 11 (2): 111–118. https://doi.org/10.1080/01446199300000004.
Shi, H. 2012. “ACO trained ANN-based bid/no-bid decision-making.” Int. J. Model. Identif. Control 15 (4): 290–296. https://doi.org/10.1504/IJMIC.2012.046408.
Shi, H., H. Yin, and L. Wei. 2016. “A dynamic novel approach for bid/no-bid decision-making.” Springerplus 5 (1589): 1–10. https://doi.org/10.1186/s40064-016-3230-1.
Sonmez, R., and B. Sözgen. 2017. “A support vector machine method for bid/no bid decision making.” J. Civ. Eng. Manage. 23 (5): 641–649. https://doi.org/10.3846/13923730.2017.1281836.
Tan, Y. T., L. Y. Shen, C. Langston, and Y. Liu. 2010. “Construction project selection using fuzzy TOPSIS approach.” J. Model. Manage. 5 (3): 302–315. https://doi.org/10.1108/17465661011092669.
Wang, W. C., K. W. Chau, D. M. Xu, L. Qiu, and C. C. Liu. 2017. “The annual maximum flood peak discharge forecasting using Hermite projection pursuit regression with SSO and LS method.” Water. Res. Manage. 31 (1): 461–477. https://doi.org/10.1007/s11269-016-1538-9.
Wanous, M., H. Boussabaine, and J. Lewis. 2003. “A neural network bid/no bid model: The case for contractors in Syria.” Constr. Manage. Econ. 21 (7): 737–744. https://doi.org/10.1080/0144619032000093323.
Wu, Y., and Q. Bian. 2013. “The research on bidding decision-making for electric power construction enterprises—Based on the improved grey Minkowski-TOPSIS model.” Technol. Econ. Manage. Res. 4 (1): 12–16. https://doi.org/10.3969/j.issn.1004-292X.2013.04.005.
Xie, M. P., J. G. Zhang, and B. J. Qiao. 2000. “Application of projection pursuit learning in ship motion extreme short time prediction.” In Proc., 3rd World Congress on Intelligent Control and Automation, 1090–1091. New York: IEEE.
Zhao, Y., and C. G. Atkeson. 1996. “Implementing projection pursuit learning.” IEEE Trans. Neural Networks 7 (2): 362–373. https://doi.org/10.1109/72.485672.
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Published In
Journal of Construction Engineering and Management
Volume 147 • Issue 7 • July 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jul 21, 2020
Accepted: Dec 23, 2020
Published online: Apr 19, 2021
Published in print: Jul 1, 2021
Discussion open until: Sep 19, 2021
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