Abstract: Resource allocation techniques for robust optimization of a set of assets. In these techniques, a user defines or selects scenarios that model investment conditions including normal and/or extreme conditions. The set of assets is optimized across the scenarios to produce weights for the assets in the set that optimize the worst-case value of the assets. A resource allocation system is disclosed which first selects a reliable set of assets for optimization and then optimizes the reliable set of assets. Optimization of the set of assets may involve robust or non-robust optimization, many different kinds of constraints and/or multiple constraints, different objective functions, and different adjustments for the objective functions. Selection of the set of assets and selection of the kind of optimization, of the constraints, of the objective function, and of the adjustments to the objective function is done using a graphical user interface.

Abstract: The present invention relates to business to business market price control and management systems. More particularly, the present invention relates to systems and methods for generating price modeling and optimization modules in a business to business market setting wherein price changes are optimized to achieve desired business results.

Abstract: An improved resource allocation system comprising a reliability decision engine (323), which allocates the portfolio's assets as required for the desired reliability portfolio. The reliability decision engine including two reliability decision engines, a basic reliability decision engine (325) and a robust reliability decision engine (327). The use of robust optimization makes it possible to determine the sensitivity of the optimized portfolio. Scenarios can be specified directly by the user or automatically generated by the system in response to a selection by the user. Inputs (329, 331) are applied to basic the basic reliability decision engine (325) and inputs (311) are applied to robust reliability decision engine (327).

Abstract: The present invention relates to business to business market price control and management systems. More particularly, the present invention relates to systems and methods for generating price modeling and optimization modules in a business to business market setting wherein price changes are optimized to achieve desired business results.

Abstract: An improved resource allocation system comprising a reliability decision engine (323), which allocates the portfolio's assets as required for the desired reliability portfolio. The reliability decision engine including two reliability decision engines, a basic reliability decision engine (325) and a robust reliability decision engine (327). The use of robust optimization makes it possible to determine the sensitivity of the optimized portfolio. Scenarios can be specified directly by the user or automatically generated by the system in response to a selection by the user. Inputs (329, 331) are applied to basic the basic reliability decision engine (325) and inputs (311) are applied to robust reliability decision engine (327).

Abstract: Resource allocation techniques for determining an allocation of investment funds among a set of at least two asset classes for a period of time which maximizes return on the investment funds over the period of time. In one aspect of the techniques, the return on the investment funds is determined using real options. In another aspect of the techniques, reliability of return is used to quantify the effects of the diversification resulting from the use of different classes of assets (203). The reliability of return is then used as a constraint on the maximization of the return. The reliability of return is determined (205) by establishing correlations between the asset classes with regard to risk, using the correlations with the standard deviations for the asset classes to determine covariances between the asset classes, and using the covariances to determine the standard deviation for the risk for the entire set.