公开(公告)号：US08930235B2

公开(公告)日：2015-01-06

申请号：US13673347

申请日：2012-11-09

Applicant: Oracle International Corporation

Inventor： Kresimir Mihic ,  Andrew Vakhutinsky ,  David Vengerov

IPC: G06Q10/00 ,  G06Q10/06 ,  G06Q10/04 ,  G06Q30/06

CPC classification number: G06Q10/06313 ,  G06Q10/04 ,  G06Q30/06

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 translation: 用于优化产品的货架空间布局的系统接收决策变量和约束，并且使用决策变量和约束来执行随机搜索（“RS”），直到RS解低于预定的改进阈值。 系统然后使用决策变量和约束解决混合整数线性规划（“MILP”）问题，并使用RS解决方案作为起点，以生成MILP解决方案。 只要MILP解决方案不在预定精度内或者不超过预定的持续时间，则该系统重复RS执行和MILP求解。 然后，系统基于最终的MILP解决方案，输出产品的货架位置和多个面板。

公开(公告)号：US12014310B2

公开(公告)日：2024-06-18

申请号：US17399342

申请日：2021-08-11

Applicant: Oracle International Corporation

Inventor： Sanghoon Cho ,  Andrew Vakhutinsky ,  Alan Wood ,  Jorge Luis Rivero Perez ,  Jean-Philippe Dumont ,  John Thomas Coulthurst ,  Denysse Diaz

IPC: G06Q10/067 ,  G06F18/2321 ,  G06F18/2415 ,  G06N20/00 ,  G06Q10/02 ,  G06Q10/06 ,  G06Q10/0631 ,  G06Q10/0637 ,  G06Q10/10 ,  G06Q30/0202

CPC classification number: G06Q10/067 ,  G06F18/2321 ,  G06F18/2415 ,  G06N20/00 ,  G06Q10/02 ,  G06Q30/0202

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.

公开(公告)号：US11514374B2

公开(公告)日：2022-11-29

申请号：US16736284

申请日：2020-01-07

Applicant: Oracle International Corporation

Inventor： Andrew Vakhutinsky ,  Setareh Borjian Boroujeni ,  Saraswati Yagnavajhala ,  Jorge Luis Rivero Perez ,  Dhruv Agarwal ,  Akash Chatterjee

IPC: G06Q10/02 ,  G06Q10/06 ,  G06Q50/12 ,  G06Q10/04

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.

公开(公告)号：US11410117B2

公开(公告)日：2022-08-09

申请号：US16167900

申请日：2018-10-23

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Su-Ming Wu ,  Andrew Vakhutinsky

IPC: G06Q10/08 ,  G06Q30/02

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.

公开(公告)号：US12243067B2

公开(公告)日：2025-03-04

申请号：US18049402

申请日：2022-10-25

Applicant: Oracle International Corporation

Inventor： Andrew Vakhutinsky ,  Jorge Luis Rivero Perez ,  Kirby Bosch ,  Recep Yusuf Bekci

IPC: G06Q30/0201 ,  G06N7/01 ,  G06N20/20 ,  G06Q10/02 ,  G06Q50/12

Abstract: Embodiments upsell a hotel room selection by generating a first hierarchical prediction model corresponding to a first hotel chain, the first hierarchical prediction model receiving reservation data from one or more corresponding first hotel properties, and generating a second hierarchical prediction model corresponding to a second hotel chain, the second hierarchical prediction model receiving reservation data from one or more corresponding second hotel properties. At each of the first hierarchical prediction model and the second hierarchical prediction model, embodiments generate corresponding model parameters. At a horizontal federated server, embodiments receive the corresponding model parameters and average the model parameters to be used as a new probability distribution, and distribute the new probability distribution to the first hotel properties and the second hotel properties.

公开(公告)号：US11704611B2

公开(公告)日：2023-07-18

申请号：US17231252

申请日：2021-04-15

Applicant: Oracle International Corporation

Inventor： Andrew Vakhutinsky

IPC: G06Q10/0631 ,  G06Q30/0201

CPC classification number: G06Q10/06315 ,  G06Q30/0206

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.

公开(公告)号：US11270326B2

公开(公告)日：2022-03-08

申请号：US16380185

申请日：2019-04-10

Applicant: Oracle International Corporation

Inventor： Su-Ming Wu ,  Andrew Vakhutinsky ,  Setareh Borjian Boroujeni ,  Santosh Bai Reddy ,  Kiran V. Panchamgam ,  Sajith Vijayan ,  Mengzhenyu Zhang

IPC: G06Q30/02 ,  G06F17/17 ,  G06Q10/06

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.