Energy companies typically face asset investment decisions which must satisfy a number of constraints in the form of annual corporate goals and complex asset dependencies. The aim of portfolio optimization is to select a portfolio, which maximizes/minimizes one or more value or risk measures, and satisfies these constraints.
In many cases, these problems can be readily expressed in the form of a set of linear equations, such that, global optimum values can be determined through the method of Linear Programming. As the complexity of corporate goals increase, non-linearities are often introduced, which require more advanced optimization techniques. In this paper we will describe a linear model for solving many optimization problems. In the case of non-linear problems we will describe the utilization of Genetic Programming as an efficient search algorithm to seek global optimum values. Finally, we will combine the two methods into a strategy for solving complex optimization problems, utilizing hybrid techniques that combine both linear and non-linear methods.