Incentive management techniques for pavement rehabilitation construction

The purpose of this study was to investigate incentive management techniques that will assist in the timely completion of pavement rehabilitation contracts. The modeling approach was used to investigate incentive management techniques. A questionnaire was sent out to 51 state transportation agencies in the United States, and interviews were conducted with California Department of Transportation Agency personnel and contractors. Forty-three of the states responded to the questionnaire. Using the results from the questinnaire, and information from published sources, a state-of-the-art study was performed on current incentive management techniques. Four models were then developed to explain the information obtained from the questionnaire and interviews. First, a cash flow model was developed to analyze what effect agency-controlled incentives have on markup, cash flow, and the profitability of a contractor on a rehabilitation contract. Incentives analyzed include: liquidated damages and bonus rates, retention and mobilization schedules, percentage of work subcontracted, and payment lag times. Second, a decision-tree model was developed to describe the decisions, made under conditions of uncertainty, which lead a contractor to complete a project early, on time, or late. Third, an adaptation model was developed which described a contractor's process of learning in response to an agency's policies about enforcing incentives and disincentives. This learning process is directly related to the contractor's decision whether or not to complete a project on time. Fourth, a traffic handling decision model was developed, based on multiattribute utility theory, which enables a design engineer to choose the best alternatives in a given pavement rehabilitation project. Finally, an illustrative case was developed to demonstrate the requirements, mechanics, and solution format of the multiattribute utility theory model. To illustrate the sensitivity of the model's scaling constants, and the certainty equivalents of the attributes of each alternative in the model, a sensitivity analysis was performed.