IDEAS home Printed from https://ideas.repec.org/a/taf/conmgt/v19y2001i2p175-188.html
   My bibliography  Save this article

A fuzzy neural network approach for contractor prequalification

Author

Listed:
  • K. C. Lam
  • Tiesong Hu
  • S. Thomas Ng
  • Martin Skitmore
  • S. O. Cheung

Abstract

Non-linearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which lead to the process being more of an art than a scientific evaluation. A fuzzy neural network (FNN) model, amalgamating both the fuzzy set and neural network theories, has been developed aiming to improve the objectiveness of contractor prequalification. Through FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eightyfive cases with detailed decision criteria and rules for prequalifying Hong Kong civil engineering contractors were collected. These cases were used for training (calibrating) and testing the FNN model. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feedforward neural network (GFNN, i.e. a crisp neural network) approach. Contractors' ranking orders, the model efficiency (R2) and the mean absolute percentage error (MAPE) were examined during the testing phase. These results indicate the applicability of the neural network approach for contractor prequalification and the benefits of the FNN model over the GFNN model. The fuzzy neural network is a practical approach for modelling contractor prequalification.

Suggested Citation

  • K. C. Lam & Tiesong Hu & S. Thomas Ng & Martin Skitmore & S. O. Cheung, 2001. "A fuzzy neural network approach for contractor prequalification," Construction Management and Economics, Taylor & Francis Journals, vol. 19(2), pages 175-188.
    • Handle: RePEc:taf:conmgt:v:19:y:2001:i:2:p:175-188
    DOI: 10.1080/01446190150505108
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/01446190150505108
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01446190150505108?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Song Xu & Reena Nupur & Devika Kannan & Rashi Sharma & Pallavi Sharma & Sushil Kumar & P. C. Jha & Chunguang Bai, 2023. "An integrated fuzzy MCDM approach for manufacturing process improvement in MSMEs," Annals of Operations Research, Springer, vol. 322(2), pages 1037-1073, March.
    2. Mohd. Ahmed & Saeed AlQadhi & Javed Mallick & Nabil Ben Kahla & Hoang Anh Le & Chander Kumar Singh & Hoang Thi Hang, 2022. "Artificial Neural Networks for Sustainable Development of the Construction Industry," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    3. Shrey Jain & Sunil Kumar Jauhar & Piyush, 2024. "A machine-learning-based framework for contractor selection and order allocation in public construction projects considering sustainability, risk, and safety," Annals of Operations Research, Springer, vol. 338(1), pages 225-267, July.
    4. Mimović Predrag & Krstić Ana, 2016. "Application of Multi-Criteria Analysis in the Public Procurement Process Optimization," Economic Themes, Sciendo, vol. 54(1), pages 103-128, March.
    5. Amirhosein Jafari, 2013. "A contractor pre-qualification model based on the quality function deployment method," Construction Management and Economics, Taylor & Francis Journals, vol. 31(7), pages 746-760, July.
    6. K. C. Lam & T. S. Hu & S. T. Ng, 2005. "Using the principal component analysis method as a tool in contractor pre-qualification," Construction Management and Economics, Taylor & Francis Journals, vol. 23(7), pages 673-684.
    7. Aziz Naghizadeh Vardin & Ramin Ansari & Mohammad Khalilzadeh & Jurgita Antucheviciene & Romualdas Bausys, 2021. "An Integrated Decision Support Model Based on BWM and Fuzzy-VIKOR Techniques for Contractor Selection in Construction Projects," Sustainability, MDPI, vol. 13(12), pages 1-28, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:conmgt:v:19:y:2001:i:2:p:175-188. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RCME20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.