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| Titel: |
A neural network bid/no bid model: the case for contractors in Syria |
| Auteur: |
Wanous, Mohammed Boussabaine, Halim A. Lewis, John |
| Verschenen in: |
Construction management & economics |
| Paginering: | Jaargang 21 (2003) nr. 7 pagina's 737-744 |
| Jaar: |
2003-10 |
| Inhoud: |
Despite the crucial importance of the 'bid/no bid' decision in the construction industry, it has been given little attention by researchers. This paper describes the development and testing of a novel bid/no bid model using the artificial neural network (ANN) technique. A back-propagation network consisting of an input buffer with 18 input nodes, two hidden layers and one output node was developed. This model is based on the findings of a formal questionnaire through which key factors that affect the 'bid/no bid' decision were identified and ranked according to their importance to contractors operating in Syria. Data on 157 real-life bidding situations in Syria were used in training. The model was tested on another 20 new projects. The model wrongly predicted the actual bid/no bid decision only in two projects (10%) of the test sample. This demonstrates a high accuracy of the proposed model and the viability of neural network as a powerful tool for modelling the bid/no bid decision-making process. The model offers a simple and easy-to-use tool to help contractors consider the most influential bidding variables and to improve the consistency of the bid/no bid decision-making process. Although the model is based on data from the Syrian construction industry, the methodology would suggest a much broader geographical applicability of the ANN technique on bid/no bid decisions. |
| Uitgever: |
Routledge |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 3 van 16 gevonden artikelen
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