Risk-Oriented Assessment Model for Project Bidding Selection in Construction Industry of Pakistan Based on Fuzzy AHP and TOPSIS Methods

  • Muhammad NazamEmail author
  • Jamil Ahmad
  • Muhammad Kashif Javed
  • Muhammad Hashim
  • Liming Yao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 281)


Risk management for the selection of complex multiple projects during the bidding process is one of the most significant problem in construction industries all over the world. This article develops an evaluation model based on fuzzy set theory, analytical hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS) methods. The criteria weight is achieved by adopting Fuzzy set theory and Analytical Hierarchy Process (AHP). Then, with the minimized risk as the objective, the technique for order performance by similarity to ideal solution (TOPSIS) is applied to determine the final ranking level of the bidding projects according to their closeness coefficient. Finally, a real world application of National Construction Limited (NCL), a largest government oriented company of Pakistan, is conducted to demonstrate the utilization of the proposed model. The results indicate that the proposed model is feasible for risk assessment of project bidding selection in construction industry.


Risk management Multi-projects selection Criteria weights Fuzzy set Fuzzy TOPSIS 



The authors wish to thank the anonymous referees for their helpful and constructive comments and suggestions. The work is supported by the National Natural Science Foundation of China (Grant No. 71301109), the Western and Frontier Region Project of Humanity and Social Sciences Research, Ministry of Education of China (Grant No. 13XJC630018), the Philosophy and Social Sciences Planning Project of Sichuan province (Grant No. SC12BJ05), and the Initial Funding for Young Teachers of Sichuan University (Grant No. 2013SCU11014).


  1. 1.
    Chauhan A, Vanish R (2012) Magnetic material selection using multiple attribute decision making approach. Mater Des 36:1–5Google Scholar
  2. 2.
    Chen TY, Tsao CY (2008) The interval-valued fuzzy TOPSIS method and experimental analysis. Fuzzy Sets Syst 159:1410–1428CrossRefGoogle Scholar
  3. 3.
    Deng XD, Wang CY (2007) The Grey Fuzzy theory appraises the risk of the project. J China Three Gorges Univ (Natural Sciences) 29:12Google Scholar
  4. 4.
    Ebrahimnejad S, Mousavi SM, Moghaddam T (2012) A novel two phase gruoup decision making approach for construction project selection in a fuzzy environment. Appl Math Model 36:4197–4217CrossRefGoogle Scholar
  5. 5.
    El-Sayegh SM (2008) Risk assessment and allocation in the UAE construction industry. Int J Project Manage 26(4):31–38CrossRefGoogle Scholar
  6. 6.
    Hassan Y, Eiichiro T (2002) Decision making using hybrid rough sets and neural networks. Int J Neural Syst 12:35–46CrossRefGoogle Scholar
  7. 7.
    Ngai EWT, Wat FKT (2005) Fuzzy decision support system for risk analysis in e-commerce development. Decis Support Syst 40:235–255CrossRefGoogle Scholar
  8. 8.
    Reza KA, Mousavi N (2011) Risk assessment model selection in construction industry. Expert Syst Appl 38:105–111Google Scholar
  9. 9.
    Shi KB, Zhang LX, Shi GQ (2005) Study on risk index system and comprehensive evaluation in construction project. J Xinjiang Agriculture Univ 28:76–80Google Scholar
  10. 10.
    Tamosaitien J, Zavadskas EK (2013) Multi-criteria risk assessment of a construction project. Procedia Comput Sci 17:129–133CrossRefGoogle Scholar
  11. 11.
    Tah JHM, Carr V (2000) A proposal for construction project risk assessment using fuzzy logic. J Constr Manage Econ 18:491–500CrossRefGoogle Scholar
  12. 12.
    Wang KC (2007) Modelling risk allocation decision in construction contracts. Int J Project Manage 25(4):85–93Google Scholar
  13. 13.
    Xu TJ, Tiong LK (2001) Risk assessment on contractors pricing stratigies. J Constr Manage Econ 19:77–84CrossRefGoogle Scholar
  14. 14.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Muhammad Nazam
    • 1
    Email author
  • Jamil Ahmad
    • 1
  • Muhammad Kashif Javed
    • 1
  • Muhammad Hashim
    • 1
  • Liming Yao
    • 1
  1. 1.Uncertainty Decision-Making LaboratorySichuan UniversityChengduPeople’s Republic of China

Personalised recommendations