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 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: Joint pricing and replenishment of freshness inventory or products having different qualities determines the joint pricing and inventory strategies to optimize the retailer's benefits from product freshness, employing a dynamic programming formulation that incorporates a demand model that captures consumer choice based on both the freshness and price.

Abstract: Joint pricing and replenishment of freshness inventory or products having different qualities determines the joint pricing and inventory strategies to optimize the retailer's benefits from product freshness, employing a dynamic programming formulation that incorporates a demand model that captures consumer choice based on both the freshness and price.

Abstract: Freshness inventory control problem may be formulated as a stochastic dynamic program. In one aspect, a stochastic dynamic programming formulation takes as input inventory status, stochastic demand forecast and cost information associated with on-hand inventory. The stochastic dynamic programming formulation is maximized to determine order quantity and depletion quantity of the product per period.

Abstract: Freshness inventory control problem may be formulated as a stochastic dynamic program. In one aspect, a stochastic dynamic programming formulation takes as input inventory status, stochastic demand forecast and cost information associated with on-hand inventory. The stochastic dynamic programming formulation is maximized to determine order quantity and depletion quantity of the product per period.

Abstract: Win probability estimation model that statistically computes the probability of winning a bid at a given price, and profit optimization models that compute the optimal price for a bid balancing the probability of winning a bid at a price with the profitability of the bid at the given price. In one stage, an expected profit margin of a product may be formulated as a function of its profit margin and win probability to compute its optimal profit margin. In another stage, an expected profit for one or more product accessories may be formulated as a function of the profit margin and their conditional win probability given the server win to compute their optimal profit margins. The conditional win probabilities for the product accessories may be modeled as a function of the utilities of the various purchase options that contain the product and that accessory.

Abstract: Win probability estimation model that statistically computes the probability of winning a bid at a given price, and profit optimization models that compute the optimal price for a bid balancing the probability of winning a bid at a price with the profitability of the bid at the given price. In one stage, an expected profit margin of a product may be formulated as a function of its profit margin and win probability to compute its optimal profit margin. In another stage, an expected profit for one or more product accessories may be formulated as a function of the profit margin and their conditional win probability given the server win to compute their optimal profit margins. The conditional win probabilities for the product accessories may be modeled as a function of the utilities of the various purchase options that contain the product and that accessory.

Abstract: Freshness inventory control problem may be formulated as a stochastic dynamic program. In one aspect, a stochastic dynamic programming formulation takes as input inventory status, stochastic demand forecast and cost information associated with on-hand inventory. The stochastic dynamic programming formulation is maximized to determine order quantity and depletion quantity of the product per period.