Abstract: A top-down and bottom-up approach that decomposes product bundles to components, classifies them into different groups corresponding to a component similarity measure, and detects their inherent values. The bundles are reassembled and characterized by several key attributes according to their component inherent values, and classified into segments. A normalized utility model is constructed for each product bundle segment, taking into account the additive effect among different commodity types and product families. The goodness of fit of the top-down and the bottom-up model may be validated. The model may be applied in an RFQ pricing environment.

Abstract: A product pricing system determines a product price. The system receives product pricing constraints and multinomial logit (“MNL”) calibration parameters. The system then generates a calibrated MNL model using the calibration parameters and encodes the MNL model and the product pricing constraints into a mixed-integer program (“MIP”). The system then solves the MIP to generate the product price.

Abstract: A system generates a parameter estimation of a plurality of variables. The system receives input data that includes user acceptance criteria and user objectives. The system encodes the input data into a matrix and transforms the input data using a dual linear program. The system then solves the dual linear program using a dual simplex method with boxed variables, and recovers parameter values for the parameter estimation. The parameter estimation may be used to provide retail forecasting.

Abstract: A pricing optimization system can solve a constrained optimization problem by generating one or more prices for one or more items that satisfy a maximum number of prioritized constraints. The one or more generated prices can be one or more prices that represent a minimal deviation between the generated one or more prices and one or more original prices for the one or more items. By satisfying a maximum number of prioritized constraints, and by minimizing a deviation from the one or more original prices for the one or more items, the generated one or more prices are one or more optimal prices.

Abstract: A system determines an optimized pre-pack configuration with an optimized pre-pack allocation and an optimized pre-pack design. The system receives demand data and constraints and initializes a current pre-pack allocation and a current pre-pack design. For the current pre-pack allocation, the system determines a new pre-pack design by solving a multi-choice integer knapsack problem, and then determines if the new pre-pack design is different than the current pre-pack design. When the new pre-pack design is different than the current pre-pack design, the system determines a new pre-pack allocation and assigns the new pre-pack allocation as the current pre-pack allocation and the new pre-pack design as the current pre-pack design and repeats the determining the new pre-pack design and the determining if the new pre-pack design is different than the current pre-pack design until the new pre-pack design is the same as the current pre-pack design.

Abstract: A pricing optimization system can solve a constrained optimization problem by generating one or more prices for one or more items that satisfy a maximum number of prioritized constraints. The one or more generated prices can be one or more prices that represent a minimal deviation between the generated one or more prices and one or more original prices for the one or more items. By satisfying a maximum number of prioritized constraints, and by minimizing a deviation from the one or more original prices for the one or more items, the generated one or more prices are one or more optimal prices.

Abstract: A product pricing system determines a product price. The system receives product pricing constraints and multinomial logit (“MNL”) calibration parameters. The system then generates a calibrated MNL model using the calibration parameters and encodes the MNL model and the product pricing constraints into a mixed-integer program (“MIP”). The system then solves the MIP to generate the product price.