I2 Technologies US, Inc.

Joachim Paul Walser

David E Joslin

Craig W Schmidt

Mukesh Dalal

James M Crawford, Jr

Vibhu K Kalyan

Srinivas Palamarthy

Generating a price schedule involves generating a graph having paths that include states with values. The graph is generated by determining the values of a successor state from the values of a predecessor state. 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. A best-fitting elasticity curve is selected. Adjusting a demand forecast value includes estimating an inventory and a demand at a number of locations. An expected number of unrealized sales at each location is calculated. An sales forecast value is determined according to the expected number.

Generating an optimized price schedule for a product

In one aspect, a computer-implemented method is provided for aggregating and scheduling product batches in a manufacturing environment. Using a batch aggregation engine implementing a mathematical programming strategy, one or more product demands are allocated to one or more product batches having suggested sizes and suggested starting times. The mathematical programming strategy includes evaluating a number of time-based penalties relative to one another in allocating the demands to the batches, the time-based penalties being based on relationships between suggested starting times for batches and times of demands being considered for allocation to batches. The suggested sizes, the suggested starting times, and feedback relating to the suggested sizes and suggested starting times are communicated from the batch aggregation engine to a scheduling engine to assist the scheduling engine in scheduling starting times for the batches.

Collaborative batch aggregation and scheduling in a manufacturing environment

In one embodiment, a method is provided for collaboratively solving an optimization problem using at least first optimization software and second optimization software each having at least partial information concerning the optimization problem. The method includes: (1) determining a solution to a first sub-problem of the optimization problem using the first optimization software based on the at least partial information concerning the optimization problem known to the first optimization software; (2) communicating from the first optimization software to the second optimization software the solution to the first sub-problem and information concerning one or more penalties for deviating from the solution to the first sub-problem; and (3) determining a solution to a second sub-problem using the second optimization software based on the at least partial information concerning the optimization problem known to the second optimization software, the communicated solution to the first sub-problem, and the communicated information concerning one or more penalties for deviating from the solution to the first sub-problem.

Collaboratively solving an optimization problem using first and second optimization software each having at least partial information concerning the optimization problem

A computer-based system ( ) for aggregating and scheduling product batches ( ) includes a batch aggregation engine ( ) that allocates one or more product demands ( ) to one or more product batches ( ) having suggested sizes and suggested starting times. The batch aggregation engine ( ) communicates to a scheduling engine ( ) the suggested batch sizes, the suggested starting times, and feedback ( ) relating to the suggested sizes and suggested starting times to assist the scheduling engine ( ) in scheduling a start time for each product batch ( ). The system ( ) may also include a scheduling engine ( ) coupled to the batch aggregation engine ( ) that receives the suggested sizes, suggested starting times, and feedback ( ) from the batch aggregation engine ( ). The scheduling engine ( ) schedules a starting time for one or more of the batches ( ) according to the suggested sizes, suggested starting times, and feedback ( ) received from the batch aggregation engine ( ). The scheduling engine ( ) also communicates the scheduled starting times and feedback ( ) relating to the scheduled starting times to the batch aggregation engine ( ) to assist the batch aggregation engine ( ) in allocating the demands ( ) to the batches ( ). The batch aggregation engine ( ) and the scheduling engine ( ) may communicate their respective outputs ( ) to each other in an iterative cycle until they have collaboratively reached a sufficiently optimal batch aggregation and scheduling solution or until a predetermined number of iterations has been reached.

System and method for collaborative batch aggregation and scheduling

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.

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Dallas, TX, United States of America

Joachim Paul Walser