Robust goal programming for multi-objective optimization of data-driven problems: A use case for the United States transportation command's liner rate setting problem

•Demonstrates via a case study the utility of Robust Goal Programming (RGP) to address optimization problems having multiple objectives and parametric uncertainty.•Illustrates how to leverage historical data to inform the parameters of an RGP instance, considering a priori identification of decisionmaker risk attitude.•For the rate-setting problem in the US Transportation Command, improves the net operating cost and rate stability goals by up to 31.8% and 50%, respectively.