Abstract: This application relates to apparatus and methods for automatic delivery route generation and assignment. In some examples, a computing device determines delivery locations for each of a plurality of previous routes taken by each of a plurality of drivers over a period of time. The computing device also obtains a plurality of current routes to be assigned to the plurality of drivers. Further, the computing device determines a familiarity value for each of the plurality of drivers for each of the plurality of current routes based on the delivery locations of the previous routes taken by each driver. The computing device assigns each of the plurality of drivers to a current route based on the determined familiarity values, and stores the assignments in a data repository. In some examples, the computing device transmits the assignments to another computing device, such as a computing device of each driver.

Abstract: A customer order prediction system can include a computing device configured to obtain customer order data characterizing a customer’s interaction with an electronic marketplace and to obtain environmental data characterizing at least one event expected to effect the customer’s ordering behavior. The computing device can be further configured to generate feature data based on the customer data and the environmental data wherein the feature data organizes the customer data and the environmental data based on characteristics of an order size estimation model. The computing device is also configured to determine a predicted customer order size using the order size estimation model and to adjust a delivery availability of the electronic marketplace based on the predicted order size.

Abstract: An approach generates a speed profile for one or more delivery locations. The speed profile corresponds to location data of a delivery order for a respective delivery location, and indicates a plurality of events associated with a movement of a delivery order to the respective delivery location. The approach generates, based on the speed profile and location data corresponding to the delivery order, feature data of the respective delivery location. The approach applies a machine learning model to the generated feature data to output a door-step time prediction for the respective delivery location. The door-step time prediction is based on a time difference between timestamps of two events of the plurality of events associated with the movement of the delivery order. The approach generates a planned delivery route for the one or more delivery locations, based on the one or more delivery locations and respective the door-step time predictions.

Abstract: A delivery route planning system can include a computing device configured to obtain order delivery data that includes information characterizing delivery locations and delivery time windows associated with a first plurality of orders and to determine a delivery plan based on the order delivery data. The computing device can be further configured to identify recyclable time in the delivery time and to obtain supplemental order delivery data that includes information characterizing supplemental orders characterizing delivery locations and delivery time windows associated a second plurality of orders. The computing system can add one or more supplemental order deliveries from the second plurality of orders to the delivery plan based on the recyclable time, the supplemental order delivery data and at least one predetermined delivery constraint.

Abstract: This application relates to apparatus and methods for automatic delivery route generation and assignment. In some examples, a computing device determines delivery locations for each of a plurality of previous routes taken by each of a plurality of drivers over a period of time. The computing device also obtains a plurality of current routes to be assigned to the plurality of drivers. Further, the computing device determines a familiarity value for each of the plurality of drivers for each of the plurality of current routes based on the delivery locations of the previous routes taken by each driver. The computing device assigns each of the plurality of drivers to a current route based on the determined familiarity values, and stores the assignments in a data repository. In some examples, the computing device transmits the assignments to another computing device, such as a computing device of each driver.