Amazon Technologies, Inc.

Jingchen Wu

Jeffrey B Maurer

Deepak Bhatia

Ali Sadighian

Tolga Han Seyhan

Zhihao Cen

Jingqiao Zhang

Onur Özkök

Arjun Krishna Subramaniam

Nicholas Deming Sherman

Gordon Mitchell Goetz

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.

Efficiently computing a feature based on a plurality of variables

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.

Adaptive control of an item inventory plan

Growing community of inventors

Seattle, WA, United States of America

Jingchen Wu