Stochastic Programming Model for Bidding Price Decision in Construction Projects
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 4
Abstract
In competitive bidding, the success and/or failure of the contractors strongly depends on their submitted bid price. Hence, the bidding price decision is a strategic subject for contractors of construction projects. This paper develops a stochastic programming model for determining the optimum bidding price in construction projects. Some model parameters, such as the number of competitors and the project’s cost, are estimated by analyzing historical data. Then, a mathematical model for the bidding price decision that maximizes the expected profit is proposed. To reduce the risk of suffering from a large loss, a maximum acceptable risk constraint is employed. To evaluate the model’s performance, some numerical problems are examined. Moreover, sensitivity analysis of the key parameters and a robustness evaluation of the model against uncertain parameters are conducted. To evaluate the model’s effectiveness in real-world situations, a case study is analyzed using the proposed approach. The numerical results indicate that the proposed approach reduces the cost estimation errors and increases the average expected profit, which validates the applicability of the model. This research contributes to the community of contractors of construction projects by providing a new approach for determining the optimum bidding price that is in greater accordance with real-world constraints.
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Data Availability Statement
The data, models, and code generated or used during the study are available from the corresponding author by request.
Acknowledgments
We express our sincere thanks for all of the assistance rendered to us by the National Iranian South Oil Company (NISOC) during this study.
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Published In
Journal of Construction Engineering and Management
Volume 147 • Issue 4 • April 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jun 3, 2020
Accepted: Oct 16, 2020
Published online: Feb 15, 2021
Published in print: Apr 1, 2021
Discussion open until: Jul 15, 2021
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