Amazon Technologies, Inc.

Senmao Liu

Robert Dreaper McDonald, Jr

Jose Ramon Algara Allegre

Kreethigha Thinakaran

Wilko Ziggy Schulz-Mahlendorf

Ziyan Huang

Rowan William Hale

Sharath Selvaraj

Eric M Hayward

Jingnan Li

Carl Morris

Features related to a system and method for scheduling a resources to perform discrete tasks are described. The scheduling features include generating schedules predicted to appeal to the tasked resource (e.g., delivery partner) such as by time, day of the week, location, item types, etc. Using machine learning, the schedule and terms thereof can be dynamically generated to suit the tastes of each tasked resource and the overall demand for services. Using historical data, the modeling also accounts for likelihood an offer will be accepted and risk of cancellation for a given resource. The machine learning may be based on a mixed integer problem as constrained by partner and system capacity parameters.

On-demand resource scheduling

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Seattle, WA, United States of America

Senmao Liu