Abstract: Embodiments upsell a hotel room selection by providing a first plurality of hotel room choices, each first plurality of hotel room choices comprising a first type of hotel room and a corresponding first price. Embodiments receive a first selection of one of the first plurality of hotel room choices. In response to the first selection, embodiments provide a second plurality of hotel room choices, the second plurality of hotel room choices comprising a subset of the first types of hotel room choices and a corresponding optimized price that is different from the respective corresponding first price.

Abstract: Embodiments optimize inventory allocation of a retail item, where the retail item is allocated from a plurality of different fulfillment centers to a plurality of different customer groups. Embodiments receive historical sales data for the retail item and estimate demand model parameters. Embodiments generate a network including first nodes corresponding to the fulfillment centers, second nodes corresponding to the customer groups, and third nodes between the first nodes and the second nodes, each of the third nodes corresponding to one of the second nodes. Embodiments generate an initial feasible inventory allocation from the first nodes to the second nodes and solves a minimum cost flow problem for the network to generate an optimal inventory allocation.

Abstract: Embodiments optimize display ordering of reservable hotel room choices for a hotel. Embodiments receive a trained prediction demand model for the hotel, the trained prediction model including estimated coefficients, and receive a total inventory of hotel rooms for the hotel. Embodiments determine optimal Lagrangian coefficients from the estimated coefficients using a first iterative gradient search and determine optimized prices per customer based on the estimated coefficients and the optimal Lagrangian coefficients using a second iterative gradient search. Embodiments determine an offer order optimization per customer based on the optimal Lagrangian coefficients and using linear programming. Embodiments receive a request for a hotel room from a first customer, the request including one or more attributes. Based on the one or more attributes and the optimized prices per customer and the offer order optimization per customer, embodiments display an optimized ordered list of hotel room choices.

Abstract: Embodiments generate a demand model for a potential hotel customer of a hotel room. Embodiments, based on features of the potential hotel customer, form a plurality of clusters, each cluster including a corresponding weight and cluster probabilities. Embodiments generate an initial estimated mixture of multinomial logit (“MNL”) models corresponding to each of the plurality of clusters, the mixture of MNL models including a weighted likelihood function based on the features and the weights. Embodiments determine revised cluster probabilities and update the weights. Embodiments estimate an updated estimated mixture of MNL models and maximize the weighted likelihood function based on the revised cluster probabilities and updated weights. Based on the update weights and updated estimated mixture of MNL models, embodiments generate the demand model that is adapted to predict a choice probability of room categories and rate code combinations for the potential hotel customer.

Abstract: Embodiments provide optimized room assignments for a hotel in response to receiving a plurality of hard constraints and soft constraints and receiving reservation preferences and room features. The optimization includes determining a guest satisfaction assignment cost based on the reservation preferences and room features, determining an operational efficiency assignment cost, generating a weighted cost matrix based on the guest satisfaction assignment cost and the operational efficiency assignment cost, and generating preliminary room assignments based on the weighted cost matrix. When the preliminary room assignments are feasible, the preliminary room assignments are the optimized room assignments comprising a feasible selection of elements of the matrix. When the preliminary room assignments are infeasible, embodiments relax one or more constraints and repeat the performing optimization until the preliminary room assignments are feasible.

Abstract: Embodiments optimize inventory allocation of a retail item, where the retail item is allocated from a plurality of different fulfillment centers to a plurality of different customer groups. Embodiments receive historical sales data for the retail item and estimate demand model parameters. Embodiments generate a network including first nodes corresponding to the fulfillment centers, second nodes corresponding to the customer groups, and third nodes between the first nodes and the second nodes, each of the third nodes corresponding to one of the second nodes. Embodiments generate an initial feasible inventory allocation from the first nodes to the second nodes and solves a minimum cost flow problem for the network to generate an optimal inventory allocation.

Abstract: Systems, methods, and other embodiments associated with controlling inventory depletion by offering different prices to different customers are described. In one embodiment, a method includes establishing first and second allocations of fulfillment centers to different geographic regions during a markdown phase. Different price schedules are determined for the orders to be fulfilled during the markdown phase based on the first and second allocations. A predicted profit is generated for the orders fulfilled under each of the different price schedules. A price schedule corresponding to the first allocation is selected as resulting in a greater predicted profit than another one of the different price schedules. A sale terminal is controlled to enact the selected price schedule during the markdown phase to cause fulfillment of the incoming orders according to the first allocation of the fulfillment centers.

Abstract: Embodiments model the demand and pricing for hotel rooms. Embodiments receive historical data regarding a plurality of previous guests and generate a multinomial logit (“MNL”) model with demand shock variables, the demand shock variables expressed using MNL utility parameters. Embodiments estimate the MNL utility parameters using a likelihood maximization and determine demand shock parameters using the estimating the MNL utility parameters. Embodiments then predict a future demand of the hotel rooms based on the demand shock parameters.

Abstract: Embodiments determine a price schedule for an item by, for each item, receiving a set of prices for the item, an inventory quantity for the item, a per-segment demand model for the item, and an objective function that is a function of the per-segment demand model and maximizes revenue based at least on a probability of a return of the item and a cost of the return. Embodiments allocate the inventory quantity among a plurality of customer segments based at least on a predicted contribution of each customer segment to the objective function. Embodiments determine a markdown portion of the price schedule for the item that maximizes the objective function, where the markdown portion assigns a series of prices selected from the set of prices for respective time periods during a clearance season for the item.

Abstract: Embodiments model demand and pricing for hotel rooms. Embodiments receive historical data regarding a plurality of previous guests, the historical data including a plurality of attributes including guest attributes, travel attributes and external factors attributes. Embodiments generate a plurality of distinct clusters based the plurality of attributes using machine learning soft clustering and segment each of the previous guests into one or more of the distinct clusters. Embodiments build a model for each of the distinct clusters, the model predicting a probability of a guest selecting a certain room category and including a plurality of variables corresponding to the attributes. Embodiments eliminate insignificant variables of the models and estimate model parameters of the models, the model parameters including coefficients corresponding to the variables. Embodiments determine optimal pricing of the hotel rooms using the model parameters and a personalized pricing algorithm.

Abstract: Embodiments provide optimized room assignments for a hotel in response to receiving a plurality of hard constraints and soft constraints and receiving reservation preferences and room features. The optimization includes determining a guest satisfaction assignment cost based on the reservation preferences and room features, determining an operational efficiency assignment cost, generating a weighted cost matrix based on the guest satisfaction assignment cost and the operational efficiency assignment cost, and generating preliminary room assignments based on the weighted cost matrix. When the preliminary room assignments are feasible, the preliminary room assignments are the optimized room assignments comprising a feasible selection of elements of the matrix. When the preliminary room assignments are infeasible, embodiments relax one or more constraints and repeat the performing optimization until the preliminary room assignments are feasible.

Abstract: Embodiments optimize the inventory allocation of a retail item that is provided from a plurality of warehouses to a plurality of price zones, each of the warehouses adapted to allocate inventory of the retail item to at least two of the price zones via links. Embodiments generate an initial inventory allocation for each warehouse to price zone link to generate a plurality of warehouse to price zone allocations. For each of the warehouse to price zone allocations, embodiments determine a marginal profit as a function of inventory allocated. Embodiments construct a bi-partite graph corresponding to each warehouse to price zone allocation, each bi-partite graph having a link weight equal to the marginal profit. Embodiments determine when there is a positive weight path between any two price zones and then reallocate the initial inventory allocation and repeat the functionality.

Abstract: Embodiments determine a price schedule for an item by, for each item, receiving a set of prices for the item, an inventory quantity for the item, a per-segment demand model for the item, and an objective function that is a function of the per-segment demand model and maximizes revenue based at least on a probability of a return of the item and a cost of the return. Embodiments allocate the inventory quantity among a plurality of customer segments based at least on a predicted contribution of each customer segment to the objective function. Embodiments determine a markdown portion of the price schedule for the item that maximizes the objective function, where the markdown portion assigns a series of prices selected from the set of prices for respective time periods during a clearance season for the item.

Abstract: Systems, methods, and other embodiments associated with controlling inventory depletion by offering different prices to different customers are described. In one embodiment, a method includes establishing first and second allocations of fulfillment centers to different geographic regions during a markdown phase. Different price schedules are determined for the orders to be fulfilled during the markdown phase based on the first and second allocations. A predicted profit is generated for the orders fulfilled under each of the different price schedules. A price schedule corresponding to the first allocation is selected as resulting in a greater predicted profit than another one of the different price schedules. A sale terminal is controlled to enact the selected price schedule during the markdown phase to cause fulfillment of the incoming orders according to the first allocation of the fulfillment centers.

Abstract: A system for determining product pricing for a product category receives a non-linear problem for the product category, in which the non-linear problem includes a demand model. For a plurality of pair of products in the product category, the system determines coefficients for a change in demand of a first product when a price of a second product is changed. The system then generates an approximate Mixed Integer Linear Programming (“MILP”) problem that includes a change of demand based on a sum of the determined coefficients. The system then solves the MILP problem to obtain a MILP solution, which provides the product pricing.

Abstract: A system for optimizing shelf space placement for a product receives decision variables and constraints, and executes a Randomized Search (“RS”) using the decision variables and constraints until an RS solution is below a pre-determined improvement threshold. The system then solves a Mixed-Integer Linear Program (“MILP”) problem using the decision variables and constraints, and using the RS solution as a starting point, to generate a MILP solution. The system repeats the RS executing and MILP solving as long as the MILP solution is not within a predetermined accuracy or does not exceed a predetermined time duration. The system then, based on the final MILP solution, outputs a shelf position and a number of facings for the product.

Abstract: A system that determines markdown pricing for a plurality of items over a plurality of time periods receives a non-linear time-dependent problem, where the non-linear time-dependent problem comprises a demand model. The system determines approximate inventory levels for each item in each time period and, for a plurality of pair of items in a product category, determines coefficients for a change in demand of a first product at each of the plurality of time periods when a price of a second product is changed using initial prices and initial approximate inventory levels. The system generates an approximate MILP problem comprising a change of demand based on a sum of the determined coefficients. The system then solves the MILP problem to generate revised prices and revised inventory levels. The functionality is repeated until a convergence criteria is satisfied, and then the system assigns the revised prices as the markdown product pricing.

Abstract: A system for optimizing shelf space placement for a product receives decision variables and constraints, and executes a Randomized Search (“RS”) using the decision variables and constraints until an RS solution is below a pre-determined improvement threshold. The system then solves a Mixed-Integer Linear Program (“MILP”) problem using the decision variables and constraints, and using the RS solution as a starting point, to generate a MILP solution. The system repeats the RS executing and MILP solving as long as the MILP solution is not within a predetermined accuracy or does not exceed a predetermined time duration. The system then, based on the final MILP solution, outputs a shelf position and a number of facings for the product.

Abstract: A system for determining time-dependent product pricing for a product category receives a non-linear problem for the product category, in which the non-linear problem includes a demand model. For a plurality of pair of products in the product category, the system determines coefficients for a change in demand of a first product at each of a plurality of time periods when a price of a second product is changed. The system then generates an approximate Mixed Integer Linear Programming (“MILP”) problem that includes a change of demand based on a sum of the determined coefficients. The system then solves the MILP problem to obtain a MILP solution, which provides the product pricing.

Abstract: A price optimization system determines the pricing of a plurality of items. The system receives an initial price vector for the items and an objective function, and assigns the initial price vector as a current price vector. The system determines a first new price vector by randomly choosing a first set of allowed prices for the items, and assigning the first set of allowed prices as the current price vector when the objective function is improved. The system then determines a second new price vector by randomly choosing a second set of allowed prices for the items and assigning the second set of allowed prices as the current price vector when the objective function does not decrease by more than a predetermined value. The system sequentially repeats this functionality until a terminating criteria is reached and then it determines the pricing.