Macroeconomic conditions, such as commodity prices, labor wages, and inflation rates, affect the cost of construction projects. In a volatile market environment, highway agencies often pass such risks to contractors by using fixed-price contracts. In turn, contractors respond by adding premiums in bid prices. How much of this risk highway agencies should pass to contractors is the topic of this paper. More specifically, the objective of this paper is to develop a model that can help highway agencies manage cost risks associated with commodity prices. The weighted least squares regression model is used to estimate the risk premium; the solution to a multiobjective optimization formulation considers a genetic algorithm approach to nonconvex optimization. Crude oil prices are used as an example of volatile commodities. The results of this study suggest that the optimal risk mitigation actions are conditional on owners' risk preferences.
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