Patents by Inventor Andrew Vakhutinsky

Andrew Vakhutinsky has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20210117998
    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.
    Type: Application
    Filed: February 7, 2020
    Publication date: April 22, 2021
    Inventors: Sanghoon CHO, Andrew VAKHUTINSKY, Saraswati YAGNAVAJHALA
  • Publication number: 20210117873
    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.
    Type: Application
    Filed: January 7, 2020
    Publication date: April 22, 2021
    Inventors: Andrew VAKHUTINSKY, Setareh Borjian BOROUJENI, Saraswati YAGNAVAJHALA, Jorge Luis Rivero PEREZ, Dhruv AGARWAL, Akash CHATTERJEE
  • Publication number: 20200380452
    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.
    Type: Application
    Filed: May 30, 2019
    Publication date: December 3, 2020
    Inventors: Andrew VAKHUTINSKY, Kiran V. PANCHAMGAM, Su-Ming WU
  • Publication number: 20200342475
    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.
    Type: Application
    Filed: April 10, 2019
    Publication date: October 29, 2020
    Inventors: Su-Ming WU, Andrew VAKHUTINSKY, Setareh Borjian BOROUJENI, Santosh Bai REDDY, Kiran V. PANCHAMGAM, Sajith VIJAYAN, Mengzhenyu ZHANG
  • Publication number: 20190122176
    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.
    Type: Application
    Filed: October 23, 2018
    Publication date: April 25, 2019
    Inventors: Su-Ming WU, Andrew VAKHUTINSKY
  • Patent number: 10095989
    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.
    Type: Grant
    Filed: November 23, 2011
    Date of Patent: October 9, 2018
    Assignee: ORACLE INTERNATIONAL CORPORATION
    Inventors: Andrew Vakhutinsky, Ngai-Hang Zachary Leung
  • Patent number: 8930235
    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.
    Type: Grant
    Filed: November 9, 2012
    Date of Patent: January 6, 2015
    Assignee: Oracle International Corporation
    Inventors: Kresimir Mihic, Andrew Vakhutinsky, David Vengerov
  • Publication number: 20140200964
    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.
    Type: Application
    Filed: January 15, 2013
    Publication date: July 17, 2014
    Applicant: ORACLE INTERNATIONAL CORPORATION
    Inventors: Anahita HASSANZADEH, Andrew VAKHUTINSKY, Kiran PANCHAMGAM
  • Publication number: 20130275277
    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.
    Type: Application
    Filed: November 9, 2012
    Publication date: October 17, 2013
    Applicant: ORACLE INTERNATIONAL CORPORATION
    Inventors: Kresimir MIHIC, Andrew VAKHUTINSKY, David VENGEROV
  • Publication number: 20130275183
    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.
    Type: Application
    Filed: May 25, 2012
    Publication date: October 17, 2013
    Applicant: ORACLE INTERNATIONAL CORPORATION
    Inventors: Maxime COHEN, Andrew VAKHUTINSKY, Kiran PANCHAMGAM
  • Publication number: 20130166353
    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.
    Type: Application
    Filed: December 21, 2011
    Publication date: June 27, 2013
    Applicant: ORACLE INTERNATIONAL CORPORATION
    Inventors: Kresimir MIHIC, David VENGEROV, Andrew VAKHUTINSKY
  • Publication number: 20130132153
    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.
    Type: Application
    Filed: November 23, 2011
    Publication date: May 23, 2013
    Applicant: ORACLE INTERNATIONAL CORPORATION
    Inventors: Andrew VAKHUTINSKY, Ngai-Hang Zachary LEUNG
  • Publication number: 20130073341
    Abstract: A system determines a revised price on a pricing ladder for a product over a pricing markdown period. For a first time interval of the markdown period, the system computes an optimal price for the product based on an inventory level of the product, where the inventory level is based on a ratio of a current on-hand inventory and a maximal on-hand inventory. The system then determines if the optimal price is approximately less than a current price of the product. When the optimal price is approximately less than a current price of the product, the system assigns to the product a lower price on the price ladder as the current price of the product.
    Type: Application
    Filed: September 19, 2011
    Publication date: March 21, 2013
    Applicant: ORACLE INTERNATIONAL CORPORATION
    Inventors: Andrew VAKHUTINSKY, Alexander KUSHKULEY, Manish GUPTE
  • Publication number: 20120284071
    Abstract: A system for determining an optimized pre-pack solution receives demand data and constraints and initializes a current pre-pack configuration comprising a current pre-pack design that comprises a plurality of pre-pack types, each pre-pack type comprising one or more different products. The system optimizes a pre-pack allocation based on the current pre-pack configuration and determines an objective function value improvement comprising, for each product in each pre-pack type, changing a level of the product by one unit and determining if the objective function value has improved. If the objective function value has improved, the system generates a new pre-pack design based on the changed level of the product and assigns the new pre-pack design as the current pre-pack design and re-optimizes the allocation. The system repeats until the objective function value stops improving. The system then outputs an optimized pre-pack configuration and optimized pre-pack allocation.
    Type: Application
    Filed: May 5, 2011
    Publication date: November 8, 2012
    Applicant: ORACLE INTERNATIONAL CORPORATION
    Inventor: Andrew Vakhutinsky
  • Publication number: 20120284079
    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.
    Type: Application
    Filed: May 5, 2011
    Publication date: November 8, 2012
    Applicant: ORACLE INTERNATIONAL CORPORATION
    Inventors: Andrew VAKHUTINSKY, Shivaram SUBRAMANIAN, Yevgeniy POPKOV, Alex KUSHKULEY