On Formulating a Portfolio of Airspace Infrastructure Investments for NAS Modernization

Modernization of the National Airspace System (NAS) has in large part been the result of infrastructure investments by the Federal Aviation Administration (FAA). These investments have typically required large capital expenditures over long periods of time. Given current budget constraints and the uncertain environment characteristic of air transportation, the FAA is increasingly pressed to be more selective in terms of the investments that they make. As part of this effort they must invest in a portfolio of technologies that in combination provide the best return on investment. This problem is akin to what financial investors face when trying to determine the optimal combination of assets in which to invest. In this paper we present a method to determine the optimum portfolio of technology investment given the value of and uncertainty in the expected return, as well as the correlations between technologies in terms of potential synergies. The key insight in portfolio theory is that investors seek to maximize the return relative to the risk and that each individual asset should be thought of in this context rather than in isolation Brealey and Myers, 1996]. This concept is known as “diversification” in the financial literature. Since the variance in the expected performance of a product during the design process characterizes the risk in not meeting that requirement, the covariance between two products relates how the performance of one product affects the performance of another product. For example, the expertise gained in the development of product i may be helpful in developing product j. Thus, some of the benefits of investing in product development i will carry over to product development j. We have previously shown that the optimal investment schedule can be determined for a Real Options-based model of the Product Development process [Abad, Clarke, and Miller, 2004]. Miller and Clarke (2005) have developed a detailed Systems Dynamics model for various air transportation systems. Melconian (2003) has demonstrated the fidelity of the MIT Extensible Air Network Simulation (MEANS) when used as a validation platform for a variety of new technologies. In this paper we combine each of these contributions. The paper is structured as follows. The necessary