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: Devices, systems, and methods are provided for enhancing delivery option user interfaces. A method may include receiving, at a first device and at a first time, a delivery request from a second device, and determining a location for the delivery request. The method may include determining, based on the first time and the location, a first available delivery time window and a second available delivery time window, each having respective start and end times. The method may include facilitating presentation of first interface data and second interface data at the second device, including a first indication that a third available delivery time window associated with a no-charge delivery begins at the first time and ends at the second end time, and including a second indication of the first available delivery time window.

Abstract: Devices, systems, and methods are provided for enhancing delivery option user interfaces. A method may include receiving, at a first device and at a first time, a delivery request from a second device, and determining a location for the delivery request. The method may include determining, based on the first time and the location, a first available delivery time window and a second available delivery time window, each having respective start and end times. The method may include facilitating presentation of first interface data and second interface data at the second device, including a first indication that a third available delivery time window associated with a no-charge delivery begins at the first time and ends at the second end time, and including a second indication of the first available delivery time window.

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.