Best Paper Award 2010

The use of design/build (DB) contracting by transportation agencies has been steadily increasing as a project delivery system for large complex highway projects. However, moving to DB from traditional design-bid-build procurement can be a challenge. One significant barrier is gaining acceptance of a best-value selection process in which technical aspects of a proposal are considered separately and then combined with price to determine the winning proposal. These technical aspects mostly consist of qualitative criteria, thus making room for human errors or biases. Any perceived presence of bias or influence in the selection process can lead to public mistrust and protests by bidders. It is important that a rigorous quantitative mathematical analysis of the evaluation process be conducted to determine whether bias exists and to eliminate it. The paper discusses two potential sources of bias—evaluators and weighting model—in the DB selection process and presents mathematical models to detect and remove biases should they exist. A score normalization model deals with biases from the evaluators; then a graphical weight-space volume model and a Monte Carlo statistical sampling model are developed to remove biases from the weighting model. The models are then tested and demonstrated using results from the DB bridge replacement project for the collapsed Mississippi River bridge of Interstate 35W in Minneapolis.

This paper introduces a methodology for eliminating biases in the best-value selection process used in design-build procurement. The authors identified two sources of biases: evaluators and weighting. A score normalization model deals with biases from the evaluators, while a graphical weight-space volume model and a Monte Carlo statistical sampling model are used to remove biases from the weighting used in the selection model. The models are tested using results from the design-build bridge replacement project for the collapsed Mississippi River bridge of Interstate 35W in Minneapolis.

The award merit of this paper lies in the anticipated impact of the proposed bias-elimination models. Existing selection methodologies for design-build teams are commonly based upon straightforward weighted averages. These methods make room for potential biases where one criterion weight or one evaluator can substantially influence the final selection decision. Any perceived presence of bias or influence in the selection process of design-build projects can lead to public mistrust and protests by bidders. This paper enhances the existing selection approaches by applying sound mathematical methods to remove biases.

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