Amazon Technologies, Inc.

Kreethigha Thinakaran

Robert Dreaper McDonald, Jr

Jose Ramon Algara Allegre

Wilko Ziggy Schulz-Mahlendorf

Ziyan Huang

Senmao Liu

Carl Morris

Sharath Selvaraj

Rowan William Hale

Eric M Hayward

Jingnan Li

Serguei Iakhnine

James McTavish

Kirill Volgin

Vadim Bachmutsky

Vitalii Fedorenko

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

Disclosed are various embodiments for determining capacities for work buffers. Data is received that indicates past work cycles for a first stage and a second stage of a pipelined process. The pipelined process includes a work buffer between the first stage and the second stage. Staffing levels for the first stage and the second stage are received. An optimal buffer capacity for the work buffer is generated based at least in part on a predicted workflow variance for the pipelined process, the staffing levels, and the past work cycles.

Determining capacities of work buffers

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Toronto, Canada

Kreethigha Thinakaran