There are two main issues with a simulation-optimization framework that couples a high-fidelity numerical model with an optimization algorithm. First, the numerical models are often computationally expensive and second, the optimization algorithm requires the numerical model to run several thousands of times which makes the whole process overly time-consuming. In addition, the computational complexity can increase further if the considered optimal management problem has multiple criteria and/or a high number of decision variables (see Table 1). This computational burden can be reduced significantly by using different surrogate modeling techniques (Rao et al. 2004; Bhattacharjya & Datta 2009; Dhar & Datta 2009; Papadopoulou et al. 2010; Sreekanth & Datta 2011, 2015; Ketabchi & Ataie-Ashtiani 2015). A surrogate model is an empirical or lower-fidelity alternative to either (A) the response of the high-fidelity numerical simulation model itself or (B) the objective function of the considered optimization problem or (C) the operating policy model in case of optimal control problems (Ratto et al. 2012). A surrogate model is simpler, considerably faster to run, and is generally derived through a training framework from the original simulation model (Razavi et al. 2012b).