Fuzzy Logic Model for Determining Minimum Overheads-Cum-Markup Rate
Publication: Journal of Construction Engineering and Management
Volume 145, Issue 4
Abstract
Contractors in bidding for a construction project often focus on the direct cost and determine a bid price by applying an overheads-cum-markup rate on top of the estimated direct cost. Such a simple method is prone to incorrectness if a rate is charged without a sound basis. As an improvement, a fuzzy logic model is proposed for determining the minimum rate for a project based on the bid position of a contractor. Using the estimates of chance of winning and loss risk as inputs, the model incorporates the bid position in a set of fuzzy rules that reflect the degree of need for work and risk attitude. A regression equation built from bid data for public sector projects connects the ratio of winning bid to owner’s budget with project attributes and is used in estimating the chances of winning for various rates. An illustrative example is provided, in which the rules representing the bid positions in nine scenarios were formulated and recent bids in Taiwan were collected for building the regression equation. The results of simulation of bidding for two bidding cases show that the model can differentiate the bid positions and suggest minimum overheads-cum-markup rates consistently. The model may prevent inadequate rates in bidding under intense price competition, thus advancing the field of bidding.
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Data Availability Statement
Data generated or analyzed in the study are available from the corresponding author by request. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
This work was supported by the Ministry of Science and Technology, ROC (Grant No. 105-2221-E-327-008).
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Information & Authors
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Published In
Journal of Construction Engineering and Management
Volume 145 • Issue 4 • April 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: May 20, 2018
Accepted: Sep 18, 2018
Published online: Jan 26, 2019
Published in print: Apr 1, 2019
Discussion open until: Jun 26, 2019
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