In water resources optimization problems, the objective function usually presumes to first run a simulation model and then evaluate its outputs. However, long simulation times may pose significant barriers to the procedure. Often, to obtain a solution within a reasonable time, the user has to substantially restrict the allowable number of function evaluations, thus terminating the search much e...

A special class of the knapsack problem is called the separable nonlinear knapsack problem. This problem has received considerable attention recently because of its numerous applications. Dynamic programming is one of the basic approaches for solving this problem. Unfortunately, the size of state-pace will dramatically increase and cause the dimensionality problem. In this paper, an efficient a...

Many industrial processes are in cascade configuration in which the material being processed goes through a sequence of processing units. In many cases, direct in-process measurements of the relevant variable are not available at all but at the last processing unit. Surrogate or model based soft measurements in which the relevant inprocess variables are inferred from other measurements would be...

The use of surrogate models is a standard method to deal with complex, realworld optimization problems. The first surrogate models were applied to continuous optimization problems. In recent years, surrogate models gained importance for discrete optimization problems. This article, which consists of three parts, takes care of this development. The first part presents a survey of modelbased meth...

Uncertainty quantification analyses often employ surrogate models as computationally efficient approximations of computer codes simulating the physical phenomena. The accuracy and economy in the construction of surrogate models depends on the quality and quantity of data collected from the computationally expensive system models. Computationally efficient methods for accurate surrogate model tr...