Abstract
In a competitive construction environment, contractors are often faced with a large number of tenders that compel them to make the best decision in a limited time. In this paper, a decision support system (DSS) based on group method of data handling model (GMDH) was developed for the scoring of tenders (STs). Based on a comprehensive study of the existing literature and experts’ opinions, seven criteria were extracted, namely client, contract, company, consulting engineers, project status/situation, risk, and economic criteria as the inputs of the system. To develop the DSS, the data from 135 tenders were collected from the previous tenders of a private construction contractor. The results illustrate that the proposed model, with a negligible error, is reliable in ST. Moreover, the proposed model specifies the score of new tenders, and decision-makers (DMs) can easily make their decisions in prioritizing and evaluating new tenders. Furthermore, a sensitivity analysis was performed to address the importance of the criteria, and it was concluded that the contract related criterion is the most important principle to the contractor. Eventually, a graphical user interface was designed in a user-friendly environment which allows the decision-makers to visualize the ST.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ahmad I, Minkarah I (1988) Questionnaire survey on bidding in construction. Journal of Management in Engineering 4(3):229–243, DOI: https://doi.org/10.1061/(ASCE)9742-597X(1988)4:3(229)
Ahmadi MH, Ahmadi MA, Mehrpooya M, Rosen MA (2015) Using GMDH neural networks to model the power and torque of a stirling engine. Sustainability (Switzerland) 7(2):2243–2255, DOI: 10.3390/su7022243
Al-Humaidi HM (2016) Construction projects bid or not bid approach using the fuzzy technique for order preference by similarity FTOPSIS method. Journal of Construction Engineering and Management 142(12):04016068, DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001180
Alsaedi M, Assaf S, Hassanain MA, Abdallah A (2019) Factors affecting contractors’ bidding decisions for construction projects in Saudi Arabia. Buildings 9(2), DOI: 10.3390/buildings9020033
Bageis AS, Fortune C (2009a) Factors affecting the bid/no bid decision in the Saudi Arabian construction contractors. Construction Management and Economics 27(1):53–71, DOI: https://doi.org/10.1080/01446190802596220
Bageis AS, Fortune C (2009b) Factors affecting the bid/no bid decision in the Saudi Arabian construction contractors. Construction Management and Economics 27(1):53–71, DOI: https://doi.org/10.1080/01446190802596220
Ballesteros-Pérez P, González-Cruz MC, Pastor-Ferrando JP, Fernández-Diego M (2012) The iso-Score Curve Graph. A new tool for competitive bidding. Automation in Construction 22:4810490, DOI: https://doi.org/10.1016/j.autcon.2011.11.007
Carbonneau R, Vahidov R (2016) A multi-attribute bidding strategy for a single-attribute auction marketplace. Expert Systems with Applications 43:42050, DOI: https://doi.org/10.1016/j.eswa.2015.08.039
Chang J, Hung C (2007) Development of pavement maintenance and rehabilitation decision model by group method of data handling (GMDH). Computing in Civil Engineering 2007:51058, DOI: 10.1061/40937(261)7
Chua DKH, Li D (2000) Key factors in bid reasoning model. Journal of Construction Engineering and Managemen 126(October):46–57, DOI: https://doi.org/10.1061/(ASCE)0733-9364(2000)126:5(349)
Costantino F, Di Gravio G, Nonino F (2015) Project selection in project portfolio management: An artificial neural network model based on critical success factors. International Journal of Project Management 33(8):1744–1754, DOI: https://doi.org/10.1016/j.ijproman.2015.07.003
Dias WPS, Weerasinghe RLD (1996) Artificial neural networks for construction bid decisions. Civil Engineering Systems 13(3):239–253, DOI: https://doi.org/10.1080/02630259608970200
Dikmen I, Birgonul MT, Gur AK (2007) A case-based decision support tool for bid mark-up estimation of international construction projects. Automation in Construction 17(1):30–44, DOI: https://doi.org/10.1016/j.autcon.2007.02.009
Egemen M, Mohamed AN (2007) A framework for contractors to reach strategically correct bid/no bid and mark-up size decisions. Building and Environment 42(3):1373–1385, DOI: https://doi.org/10.1016/j.buildenv.2005.11.016
Egemen M, Mohamed A (2008) SCBMD: A knowledge-based system software for strategically correct bid/no bid and mark-up size decisions. Automation in Construction 17(7):864–872, DOI: https://doi.org/10.1016/j.autcon.2008.02.013
Friedman L (1956) A competitive-bidding strategy. Operations Research 4(1):104–112, DOI: 10.1287/opre.4.1.104
Gabriel SA, Kumar S, Ordóñez J, Nasserian A (2006) A multiobjective optimization model for project selection with probabilistic considerations. Socio-Economic Planning Sciences 40(4):297–313, DOI: https://doi.org/10.1016/j.seps.2005.02.002
Ghazanfari N, Gholami S, Emad A, Shekarchi M (2017) Evaluation of GMDH and MLP networks for prediction of compressive strength and workability of concrete. Bulletin de la Société Royale des Sciences de Liège 86:8550868
Golafshani EM (2015) Introduction of biogeography-based programming as a new algorithm for solving problems. Applied Mathematics and Computation 270:1012, DOI: https://doi.org/10.1016/j.amc.2015.08.026
Golafshani EM, Behnood A (2018) Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete. Applied Soft Computing Journal 64:3770400, DOI: https://doi.org/10.1016/j.asoc.2017.12.030
Golafshani EM, Behnood A (2019) Estimating the optimal mix design of silica fume concrete using biogeography-based programming. Cement and Concrete Composites 96:950105
Golafshani EM, Talatahari S (2018) Predicting the climbing rate of slip formwork systems using linear biogeography-based programming. Applied Soft Computing Journal 70:2630278, DOI: https://doi.org/10.1016/j.asoc.2018.05.036
Guo JX, Hu CM, Bao R (2019) Predicting the duration of a general contracting industrial project based on the residual modified model. KSCE Journal of Civil Engineering 23(8):3275–3284, DOI: https://doi.org/10.1007/s12205-019-1543-7
Hwang JS, Kim YS (2016) A bid decision-making model in the initial bidding phase for overseas construction projects. KSCE Journal of Civil Engineering 20(5):1189–1200, DOI: https://doi.org/10.1007/s12205-015-0760-y
Ivakhnenko AG (1971) Polynomial theory of complex systems. IEEE Transactions on Systems, Man and Cybernetics SMC-1(4):364–378 DOI: https://doi.org/10.1109/TSMC.1971.4308320
Ivakhnenko A G, Ivakhnenko GA (1995) The review of problems solvable by algorithms of the group method of data handling (GMDH). Pattern Recognition and Image Analysis C/C of Raspoznavaniye Obrazov I Analiz Izobrazhenii 5:5270535
Jarkas AM, Mubarak SA, Kadri CY (2014) Critical factors determining bid/no bid decisions of contractors in qatar. Journal of Management in Engineering 30(4):05014007, DOI: https://doi.org/10.1061/(ASCE)ME.1943-5479.0000223
Jiang Y, Liu S, Peng L, Zhao N (2019) A novel wind speed prediction method based on robust local mean decomposition, group method of data handling and conditional kernel density estimation. Energy Conversion and Management 200(15), DOI: https://doi.org/10.1016/j.enconman.2019.112099
Khanzadi M (2008) Applying delphi method and decision support system for bidding. First international conference on construction in developing countries (ICCIDC–I), August 4–5, Karachi, Pakistan, 64–73
Lee CC, Ou-Yang C (2009) A neural networks approach for forecasting the supplier’s bid prices in supplier selection negotiation process. Expert Systems with Applications 36(2):2961–2970, DOI: https://doi.org/10.1016/j.eswa.2008.01.063
Lin L, Li S, Sun S, Yuan Y, Yang M (2020) A novel efficient model for gas compressibility factor based on GMDH network. Flow Measurement and Instrumentation 71(3), DOI: https://doi.org/10.1016/j.flowmeasinst.2019.101677
Maghsoodi AI, Khalilzadeh M (2018) Identification and evaluation of construction projects’ critical success factors employing fuzzy-TOPSIS approach. KSCE Journal of Civil Engineering 22(5):1593–1605, DOI: https://doi.org/10.1007/s12205-017-1970-2
Minli Z, Shanshan Q (2012) Research on the application of artificial neural networks in tender offer for construction projects. Physics Procedia 24:178101788, DOI: https://doi.org/10.1016/j.phpro.2012.02.262
Moselhi O, Hegazy T, Fazio P (1991) Neural network as tools in construction. Journal for Construction Engineering and Management 117(4):606–625
Naaranoja M, Haapalainen P, Lonka H (2007) Strategic management tools in projects case construction project. International Journal of Project Management 25(7):659–665, DOI: https://doi.org/10.1016/j.ijproman.2007.04.002
Olatunji OA, Aje OI, Makanjuola S (2017) Bid or no-bid decision factors of indigenous contractors in Nigeria. Engineering, Construction and Architectural Management 24(3):378–392, DOI: https://doi.org/10.1108/ECAM-01-2016-0029
Rahman SM, Khondaker AN, Abdel-Aal R (2012) Self organizing ozone model for empty quarter of saudi arabia: Group method data handling based modeling approach. Atmospheric Environment 59:3980407, DOI: https://doi.org/10.1016/j.atmosenv.2012.05.008
Ravanshadnia M, Rajaie H, Abbasian HR (2010) Hybrid fuzzy MADM project-selection model for diversified construction companies. Canadian Journal of Civil Engineering 37(8):1082–1093, DOI: 10.1139/L10-;048
Ravanshadnia M, Rajaie H, Abbasian HR (2011) A comprehensive bid/ no-bid decision making framework for construction companies. Iranian Journal of Science and Technology, Transaction B: Engineering 35(1):95–103
Sackey S, Kim BS (2018) Development of an expert system tool for the selection of procurement system in large-scale construction projects (ESCONPROCS). KSCE Journal of Civil Engineering 22(11): 4205–4214, DOI: https://doi.org/10.1007/s12205-018-0439-2
Shafahi A, Haghani A (2014) Modeling contractors’ project selection and markup decisions influenced by eminence. International Journal of Project Management 32(8):1481–1493, DOI: https://doi.org/10.1016/j.ijproman.2014.01.013
Shash AA (1993) Factors considered in tendering decisions by top UK contractors. Construction Management and Economics 11(2):111–118, DOI: 10.1080/01446199300000004
Shash AA, Abdul-Hadi NH (1992) Factors affecting a contractor’s mark-up size decision in Saudi Arabia. Construction Management and Economics 10(5):415–429, DOI: 10.1080/01446199200000039
Sonmez R, Sözgen B (2017) A support vector machine method for bid/no bid decision making. Journal of Civil Engineering and Management 23(5), DOI: https://doi.org/10.3846/13923730.2017.1281836
Takano Y, Ishii N, Muraki M (2018) Determining bid markup and resources allocated to cost estimation in competitive bidding. Automation in Construction 85:3580368, DOI: https://doi.org/10.1016/j.autcon.2017.06.007
Utama WP, Chan APC, Zulherman Zahoor H, Gao R, Jumas DY (2018) ANFIS multi criteria decision making for overseas construction projects: A methodology. 3rd international conference on research methodology for built environment and engineering, November 8–9, Shah Alam, Malaysia
Walton R, Binns A, Bonakdari H, Ebtehaj I, Gharabaghi B (2019) Estimating 2-year flood flows using the generalized structure of the Group Method of Data Handling. Journal of Hydrology 575:6710689, DOI: https://doi.org/10.1016/j.jhydrol.2019.05.068
Wang X, Li L, Lockington D, Pullar D, Jeng D-S (2005) Self-organizing polynomial neural network for modelling complex hydrological processes. Research Report, University of Sydney, Camperdown, Australia, 861
Wang WC, Wang SH, Tsui YK, Hsu CH (2012) A factor-based probabilistic cost model to support bid-price estimation. Expert Systems with Applications 39(5):5358–5366, DOI: https://doi.org/10.1016/j.eswa.2011.11.049
Wanous M, Boussabaine AH, Lewis J (2000) To bid or not to bid: A parametric solution. Construction Management and Economics 18(4):457–466, DOI: 10.1080/01446190050024879
Wanous M, Boussabaine HA, Lewis J (2003) A neural network bid/no bid model: The case for contractors in Syria. Construction Management and Economics 21(7):737–744, DOI: 10.1080/0144619032000093323
Yan P, Liu J, Skitmore M (2018) Individual, group, and organizational factors affecting group bidding decisions for construction projects. Advances in Civil Engineering 2018:1010, DOI: 10.1155/2018/3690302
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mehrabani, M.N., Golafshani, E.M. & Ravanshadnia, M. Scoring of Tenders in Construction Projects Using Group Method of Data Handling. KSCE J Civ Eng 24, 1996–2008 (2020). https://doi.org/10.1007/s12205-020-1537-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-020-1537-5