A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Neural-Network-Centered Approach to Determining Lower Limit of Combined Rate of Overheads and Markup
In bidding for construction projects, a contractor often uses the simple method of adding a combined rate of overheads and markup on top of the estimated direct cost for arriving at a bid. If the rate is subjectively charged, a greater loss risk is involved. An improved approach to determining the lower limit of the rate for a project is proposed. A neural network model built from recent winning bids and project attributes maps the rate in the winning bid for a project and is used to estimate the probabilities of winning for various rate levels. Then, the minimum rate to be charged is determined based on minimization of the overall loss risk defined by a probabilistic model with the estimated probabilities of winning and project cost variability. The approach is illustrated by an example using a firm’s bidding cases in Taiwan; the results are consistent with the features of the approach. In setting the lower limit of the overheads-cum-markup rate, the approach can be used to prevent arbitrary overcuts in bids under intense competition, thereby filling a gap among existing works and advancing the field of bidding.
Neural-Network-Centered Approach to Determining Lower Limit of Combined Rate of Overheads and Markup
In bidding for construction projects, a contractor often uses the simple method of adding a combined rate of overheads and markup on top of the estimated direct cost for arriving at a bid. If the rate is subjectively charged, a greater loss risk is involved. An improved approach to determining the lower limit of the rate for a project is proposed. A neural network model built from recent winning bids and project attributes maps the rate in the winning bid for a project and is used to estimate the probabilities of winning for various rate levels. Then, the minimum rate to be charged is determined based on minimization of the overall loss risk defined by a probabilistic model with the estimated probabilities of winning and project cost variability. The approach is illustrated by an example using a firm’s bidding cases in Taiwan; the results are consistent with the features of the approach. In setting the lower limit of the overheads-cum-markup rate, the approach can be used to prevent arbitrary overcuts in bids under intense competition, thereby filling a gap among existing works and advancing the field of bidding.
Neural-Network-Centered Approach to Determining Lower Limit of Combined Rate of Overheads and Markup
Chao, Li-Chung (author) / Kuo, Chiang-Pin (author)
2017-12-15
Article (Journal)
Electronic Resource
Unknown
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