Abstract: A decision support tool which takes a high-level view of supply and demand in the realm of labor resources and produces globally-optimal recommendations for assignment of practitioners to open seats. These assignments preferably optimize a utility function composed of different business metrics and can be customizable for different organizations and priorities.

Abstract: Equipment failures are diagnosed using an integrated approach of case-based reasoning (CBR) and statistical reliability analysis. The method outputs a single list of suggested failed components, ranked by an overall probability of failure, and their associated past solutions from the case base. The overall probability of failure is calculated using the combined logic of case based reasoning and statistical reliability analysis. The method is typically used in a real-time decision support system to aid equipment diagnosis by a maintenance technician working in the field.

Abstract: A method for determining an alternative representation of an optimization model reduces model input through compact representation of model parameters. Model generation is performed at varying levels of complexity (approximation) depending on pre-defined, business approved thresholds.

Abstract: The present invention provides a system and method for strategic budgeting of initial response for managing wildfires. According to the present invention, stochastic-integer-programming-based constrained optimization techniques are employed to develop a strategic budget by optimally allocating disaster management resources to disaster events belonging to scenarios associated with occurrence probabilities. According to the invention, certain machine-readable data describing fires may be subjected to computerized data processing, ultimately producing a determination of valid disaster management resources for each fire event, which may be used for strategic budgeting of initial responses for managing wildfires.

Abstract: A method (and system) of providing optimization repeatability in an on-demand computing environment removes variability in an optimization model instance and can be exemplarily implemented in a service architecture. The method and system receives a plurality of physical data instances, which are different representations of the same logical data model, and transforms the plurality of physical data instances into a normalized physical data instance, which can be combined with an optimization model to form a unique optimization model instance, thereby providing repeatability in solving optimization problems.

Abstract: A method (and structure) of calculating an optimal mix of inputs for a problem having a plurality of possible scenarios, includes providing data for parameters for each of the scenarios, wherein the data includes a probability of each of the possible scenarios. A mix of parameters is determined for the problem by evaluating the plurality of scenarios, given the scenario probability inputs.

Abstract: A method and structure for a computerized method for providing an optimization solution, includes, for a process, wherein is defined a linear functional form y=f (X,c), where X comprises a set of independent variables X={x1, . . . xn}, c includes a set of functional parameters c={c1, . . . cn}, and y comprises a dependent variable, where the independent variables set X is partitioned into two subsets, X1 and X2, receiving data for the process and minimizing y with respect to X1. Dependent variable y is maximized with respect to X2, subject to a set of constraints. The maximizing y includes a global optimum for the process.

Abstract: A stochastic integer programming based constrained optimization technique enables optimal allocation of classrooms and instructors to requested classes associated with cancellation probabilities. An analytical tool allows optimization of overall operational revenue/profit under different planning scenarios involving chaining of various classes, prerequisite relationships, and inter-class spacing requirements. This system allows the description and input of a list of classes, their cancellation probabilities and the input of available classrooms and instructors for determining the most revenue-generating/profitable class schedule. The revenue/profit optimization model corresponds to a two-stage mixed integer program.

Abstract: The present invention provides a method and apparatus for developing an optimal sales plan for multiple products with multiple price classes contingent on different possible realizations of uncertain demand over multiple time periods with the objective of maximizing expected revenue over a constrained capacity. The present invention formulates a multistage stochastic program that generates a quantity of each of the multiple products to be sold in each of the multiple time periods and a recommendation for when realized demand for at least one of the multiple time periods exceeds a planned sales volume; estimates a likelihood that the realized demand for the at least one of the multiple time periods exceeds the planned sales volume; and collects realized order data for each of the multiple time periods; and executes the multistage stochastic program using the likelihood and the realized order data to generate a sales plan.

Abstract: Equipment failures are diagnosed using an integrated approach of case-based reasoning (CBR) and statistical reliability analysis. The method outputs a single list of suggested failed components, ranked by an overall probability of failure, and their associated past solutions from the case base. The overall probability of failure is calculated using the combined logic of case based reasoning and statistical reliability analysis. The method is typically used in a real-time decision support system to aid equipment diagnosis by a maintenance technician working in the field.