Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: This disclosure describes a system and method for utilizing an automated aerial vehicle for rapidly transporting a priority pick item when an exception occurs (e.g., an item that was to be picked from another location or otherwise processed is found to be missing or damaged). In some implementations, a priority pick instruction may be generated that directs picking of the item from an alternative location after which the item may be transported by the automated aerial vehicle to a processing location (e.g., a problem solve station). A flight path may be determined which may have starting and ending points at designated locations where the automated aerial vehicle can safely land and take off for acquiring and/or delivering the priority pick item.

Abstract: Disclosed are various implementations for generating one or more visualization regions in a user interface indicative of a state of a fulfillment center. A visualization region may be generated comprising one or more visual identifiers, labels, and metrics describing one or more stages in a fulfillment process. In response to a manipulation of a portion of the visualization region, a predefined action may be initiated.

Abstract: A conferencing system receives information identifying a user and retrieves calendar information specifying scheduled events of the user. The conferencing system selects one of the events and joins the user to the event without the user manually entering information about the event.