Abstract: Generating a price schedule involves generating a graph (50) having paths that include states (52) with values (54, 56, 58). The graph (50) is generated by determining the values (56, 58) of a successor state (52) from the values (56, 58) of a predecessor state (52). An optimal path is selected, and a price schedule is determined from the optimal path. Computing an elasticity curve involves having a demand model, values for demand model, and filter sets that restrict the values. Elasticity curves are determined by filtering the values using filter sets, and calculating the elasticity curve using the demand model. An best-fitting elasticity curve is selected. Adjusting a demand forecast value (56) includes estimating an inventory and a demand at a number of locations (24). An expected number of unrealized sales at each location (24) is calculated. An sales forecast value (56) is determined according to the expected number.

Abstract: A system for optimizing request-promise workflows includes a first entity that provides supplies to a second entity. The second entity optimizes its production to generate a request for the supplies and reoptimizes in response to promises received from the first entity. The first entity may send a culprit promise that identifies the supply that is creating the shortage.

Abstract: According to one embodiment, a computer-implemented method for multi-party constrained optimization is provided. The method includes accessing a first optimization problem corresponding to a first party to a negotiation, the first optimization problem including at least one first objective and one or more first constraints to which the first objective relates, and accessing a second optimization problem corresponding to a second party to the negotiation, the second optimization problem including at least one second objective and one or more second constraints to which the second objective relates. A first optimal value is determined for the at least one first objective considering the one or more first constraints for the first optimization problem, and a second optimal value is determined for the at least one second objective considering the one or more second constraints for the second optimization problem.

Abstract: A method and system for solving constrained optimization problems. An initial abstract solution represents a prioritized set of decisions. The abstract solution is used as the basis for building a concrete solution. The concrete solution is analyzed to determine one or more local moves that represent a re-prioritization of the abstract solution. After a local moves is made, the process begins again with a new abstract solution, that is closer to an optimal solution. This process continues interactively until an optimal solution is reached or approached. The prioritized set of decisions can be implemented as a priority vector or a priority graph.

Abstract: Processing techniques for enhancing execution of programs written in a logic-oriented programming language such as PROLOG are disclosed. The techniques are particularly useful for programs having class predicates and subclass predicates which are definitive of a class/subclass hierarchy, such as the case with PROLOG's application in object-oriented programming systems, expert systems, object-oriented databases, object-oriented deductive databases, knowledge representations, etc. The techniques ensure that searching within the type hierarchy takes precedence over searching of instances of types. Important to accomplishing this function is the pre-assigning of ranks to predicates and clauses within the program to be processed. Query processing on the program is then based upon the pre-assigned predicate and clause rankings.