Abstract: A set of SKUs is divided into a plurality of different Mean Field clusters, and a tracker (or sensor) is identified for each cluster. Product decisions for each Mean Field cluster are generated based on the tracker (or sensor) and each Mean Field cluster is then deconstructed to obtain product decisions for individual SKUs in the Mean Field cluster.

Abstract: Delivery of a plurality of messages by a satellite is scheduled taking energy into account. An amount of time needed to deliver each of the plurality of messages received from a plurality of ground sources and intended for a plurality of ground recipients at respective different destinations is estimated based on the size of each of the messages. Each different destination has an associated contact time window during which the satellite will be in range of the ground recipients at that destination. An amount of energy that will be available from a power source for delivering the messages during each contact window is determined. Delivery of the plurality of messages to the ground recipients is scheduled such that a total time for delivery of the messages is minimized.

Abstract: Delivery of a plurality of messages by a satellite is scheduled taking energy into account. An amount of time needed to deliver each of the plurality of messages received from a plurality of ground sources and intended for a plurality of ground recipients at respective different destinations is estimated based on the size of each of the messages. Each different destination has an associated contact time window during which the satellite will be in range of the ground recipients at that destination. An amount of energy that will be available from a power source for delivering the messages during each contact window is determined. Delivery of the plurality of messages to the ground recipients is scheduled such that a total time for delivery of the messages is minimized.

Abstract: A set of SKUs is divided into a plurality of different Mean Field clusters, and a tracker (or sensor) is identified for each cluster. Product decisions for each Mean Field cluster are generated based on the tracker (or sensor) and each Mean Field cluster is then deconstructed to obtain product decisions for individual SKUs in the Mean Field cluster.

Abstract: An optimization solver divides time-indexed historical data into intervals that have temporal boundaries. A discrete coefficient evaluator calculates coefficient values in a forecasting model at the temporal boundaries of the training data. An incremental parameter evaluator evaluates incremental parameter changes between the temporal boundaries in the training data. The incremental parameter evaluator updates the parameter values, based upon the incremental changes in the parameters, so that the updated parameter values can be used by the discrete coefficient evaluator for evaluating coefficient values at a next temporal boundary. The trained forecasting modes is deployed in a system to forecast phenomena.

Abstract: A set of SKUs is divided into a plurality of different Mean Field clusters, and a tracker (or sensor) is identified for each cluster. Product decisions for each Mean Field cluster are generated based on the tracker (or sensor) and each Mean Field cluster is then deconstructed to obtain product decisions for individual SKUs in the Mean Field cluster. An interrogation system operates an interpretation of rules that were used to generate the product discussion.