Intelligent system for successful selection of construction bidders

Practice long proved the traditional bid evaluation approach that depends solely on the bid price to have lots of pitfalls. This led to a rise in using multi-parametric bid evaluation systems, e.g., the A+B bidding system common to highway projects. However, a challenging issue remains in that several evaluation parameters are quite subjective and difficult to assess, particularly those associated with the expected future performance of a given bidder. To improve the quality of the bidder selection process, this paper introduces a newly developed system that benefits from the intelligent inference capabilities of Bayesian Belief Networks (BBN). The main concept of this system is based on the calculation of equivalent monetary values of various bidder parameters, e.g., time, quality, safety, work attitude and practices. These monetary values are then added to the bid price to calculate the total combined bid value (TCBV), which can be used to identify the most appropriate bidder for the project. The automated system is primarily composed of four integrated modules that handle adjustment of bid price, prediction of expected future performance for a given bidder, prioritization of the evaluation parameters, and calculation of the TCBV. All four modules are encompassed in a user-friendly interface to facilitate the utilization of the developed system. Finally, the paper demonstrates the use of this intelligent system through an illustrative example of a residential building project.