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公开(公告)号:US10185980B1
公开(公告)日:2019-01-22
申请号:US14703751
申请日:2015-05-04
Applicant: Amazon Technologies, Inc.
Inventor: Jingchen Wu , Zhihao Cen , Jingqiao Zhang , Jeffrey B. Maurer , Deepak Bhatia , Ali Sadighian
Abstract: Techniques for computing a feature based on variables may be provided. For example, historical realizations associated with a first variable may be accessed. The first variable may be associated with an item. Realizations of the first variable may be based on one or more factors associated with the item. Historical realization of a second variable associated with the item may also be accessed. The historical realizations of the first and second variables may be analyzed to generate an expected realization of the first variable as a function of the second variable. The feature may be computed based on the generated function.
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公开(公告)号:US20180096290A1
公开(公告)日:2018-04-05
申请号:US14579505
申请日:2014-12-22
Applicant: Amazon Technologies, Inc.
Inventor: Hernan P. Awad , Ehsan Amiri , Deepak Bhatia , Ali Sadighian , Tolga Han Seyhan , Nicholas Deming Sherman
CPC classification number: G06Q10/087 , G06Q30/0635
Abstract: Techniques for generating a decision to store units of an item may be provided. The units of the item may be offered from a network-based resource. For example, a first ordering channel and a second ordering channel may be provided to facilitate orders of the item from the network-based resource. The first and second ordering channels may allow random orders and deterministic orders of the item, respectively. Additionally, a value of a look ahead window associated with the second ordering channel may be accessed. The value may shift a time for an automatic generation of a deterministic order. Further, a first loss and a second loss may be computed based on the value. The first and second losses may be associated with losing a random order and losing the deterministic order, respectively. The decision may be generated based at least in part on the first loss and the second loss.
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公开(公告)号:US09818082B1
公开(公告)日:2017-11-14
申请号:US15009607
申请日:2016-01-28
Applicant: Amazon Technologies, Inc.
Inventor: Ali Sadighian , Salal Humair , Jun Tong , Xiaochang Hu , Mahmoud Ahmed Bishr , Chao Liu , Di Wu , Travis Francis Brayak , Patrick Ludvig Bajari , Deepak Bhatia
CPC classification number: G06Q10/087
Abstract: Techniques for managing a removal channel may be described. In an example, a first estimate may be generated. The first estimate may be associated with removing a volume of items from an inventory space within a planning horizon through a removal channel. A decision to remove the volume of items may be accessed. The decision may be based on the first estimate and a second estimate associated with adding a capacity for inventorying an equivalent volume of items. Based on the decision, a constraint may be generated and imposed on an inventory level of an item during a time period of the planning horizon. A quantity of the item to remove within the time period through the removal channel may be estimated based on the constraint. Usage of the removal channel during the time period may be set based on the quantity of the item to remove.
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公开(公告)号:US09805402B1
公开(公告)日:2017-10-31
申请号:US14499037
申请日:2014-09-26
Applicant: Amazon Technologies, Inc.
Inventor: Jeffrey B. Maurer , Deepak Bhatia , Gordon Mitchell Goetz , Onur Özkök , Tolga Han Seyhan , Nicholas Deming Sherman , Arjun Krishna Subramaniam , Jingchen Wu
CPC classification number: G06Q30/0605 , G06Q10/087
Abstract: Techniques for determining a decision to acquire units of an item to be inventoried may be provided. For example, a demand for an item may be simulated to determine a consumption of a capacity for inventorying the item. A discrepancy between the consumption of the capacity and the capacity may be determined. An opportunity cost associated with the capacity may be updated based at least in part on determining that the discrepancy fails a convergence criterion. The opportunity cost may indicate a value associated with using the capacity. The consumption of the capacity may be simulated based at least in part on the updated opportunity cost. A resulting discrepancy may be determined. If the resulting discrepancy meets the convergence criterion, the decision to acquire the units of the item may be generated based at least in part on the updated opportunity cost.
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公开(公告)号:US10922646B1
公开(公告)日:2021-02-16
申请号:US15716299
申请日:2017-09-26
Applicant: Amazon Technologies, Inc.
Inventor: Salal Humair , Hernan P. Awad , Deepak Bhatia , Onur Özkök , Tolga Han Seyhan , Arjun Krishna Subramaniam , Yan Xia , Chengliang Zhang
Abstract: Features related to systems and methods for predictive estimation for network inventory planning for items for distribution through a multi-echelon network are disclosed. The problem may be modeled to account for demand for an item that may be satisfied by fulfillment centers within the network. The model considers benefits to ensuring adequate inventory to meet expected demand streams along with any savings accruing from a lower transfer and salvage costs.
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公开(公告)号:US10185927B1
公开(公告)日:2019-01-22
申请号:US14973487
申请日:2015-12-17
Applicant: Amazon Technologies, Inc.
Inventor: Tolga Han Seyhan , Ali Sadighian , Salal Humair , Jialei Rong , Haiyun Wang , Deepak Bhatia , Zhihao Chen , Abid Kapadya , Julie Wei-Shu Chang
Abstract: Techniques are provided herein for utilizing an inventory engine to optimize the selection of a set of items to be stored as inventory at a storage location. A candidate set of items may be identified based at least in part on a selection model. In accordance with at least one embodiment, the selection model may be based at least in part on a capacity of the storage location and a threshold time duration by which purchased items of the set of items are to be transported from the storage location to a purchaser. A plurality of probability values corresponding to the candidate set of items may be determined. An optimal set of items may be determined based at least in part on the candidate set of items and the plurality of probability values.
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