Evaluating two-range robust optimization for project selection

This paper investigates empirically two-range robust optimization (2R-RO) as an alternative to stochastic programming in terms of computational time and solution quality. We consider a number of possible projects with anticipated costs and cash flows, and an investment decision to be made under budget limitations. In 2R-RO, each uncertain parameter is allowed to take values from more than one uncertainty range and the number of parameters that fall within each range is bounded by a budget of uncertainty. The stochastic description of uncertainty involves three values (high, medium and low) for each ambiguous parameter. We set up the 2R-RO model so that the possible values taken by the uncertain parameters match the three scenarios in the stochastic programming approach and test both in simulations. While the stochastic programming (SP) approach takes about a day to solve, the robust optimization (RO) approach solves the same project selection problem in seconds.