Amazon Technologies, Inc.

John Schroder

Prashanth Paramanandan

Yenchao Chua

Wilko Ziggy Schulz-Mahlendorf

Ziyan Huang

Meghna Singla

Felipe Lagos

Rajeev Damani

Robert McDonald

Damon Alexander

Al Ashkar

Thomas Fillebeen

Saurabh Roongta

Joshua Hum

Margaret P Pierson

Jaymin Chinubhai Shah

Embodiments herein describe a predictive delivery planning system that includes a forecaster that predicts simulated orders (e.g., forecasted orders) for multiple different demand scenarios. Once the simulated orders are selected, a route planner can generate routes for delivering the simulated and actual customer orders for each scenario. The planning system then converts these routes in labor plans indicating the amount of time a delivery driver would need to deliver the orders. The planning system identifies a set of labor blocks from the labor plans and determines whether these blocks satisfy a utilization threshold. Put differently, the planning system uses a releasing policy that releases labor blocks whose expected utilization is higher than a predetermined threshold. The released labor blocks are then displayed to delivery drivers who can then select how many of the labor blocks they would like to work.

Predicting demand for route generation

A system and method for capacity management based on location and status of flexible (e.g., non-scheduled) service resources. Features are included to determine if the system can accept a request for a pick-up point (e.g., a restaurant, a quick order fulfillment center, grocery store, network location) based on the density, type and characteristics of the delivery resources in proximity (e.g., time and/or distance) to the pick-up point. The system also leverages historic delivery performance of the resources to assess whether to offer service or a service level for a given request.

Resource capacity management

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Bothell, WA, United States of America

John Schroder