Abstract: Provided are systems and processes for optimizing assignments of deliveries for perishable goods. In one aspect, a method is provided for pairing a set of created orders with a set of available couriers. The set of created orders may include orders confirmed by the merchant and the set of available couriers include couriers that are online with an active status. Feasible pairings are generated between each courier and each created order. Infeasible pairings are eliminated based on factors such as transportation mode. Possible routes for each pairing are generated and scored based on weighted factors. The scores are optimized to achieve a set of routes with a maximum score. The routes are then offered to the corresponding courier if the courier will arrive at or after the created order is completed by the merchant. A neural network may be implemented to recognize the optimal score for a given duration.

Abstract: Provided are various mechanisms and processes for optimizing real-time, on-demand deliveries of perishable goods. In one aspect, a method is provided for aggregating on-demand deliveries using a depot dispatch protocol. The method comprises dispatching runners to pick up on-demand orders for drop off at a depot where the orders are aggregated and batched based on optimized delivery routes and destination proximities. Batches of orders are then assigned to couriers who may arrive at the depot to receive the batched orders without having to navigate through areas of high congestion. Such delivery routing system may be implemented alongside a delivery tracking system for generating estimated time of arrival predictive updates for real-time delivery of perishable goods.

Abstract: Described are systems and processes for generating dynamic estimated time of arrival predictive updates for delivery of perishable goods. In one aspect a system is configured for generating dynamic estimated time of arrival (ETA) predictive updates between a series of successive events for real-time delivery of orders. For each order, a plurality of delivery events and corresponding timestamps are received from devices operated by customers, restaurants, and couriers. Based on the timestamps, the system generates a plurality of ETA time predictions for one or more of the delivery events with trained predictive models that use weighted factors including historical restaurant data and historical courier performance. As additional timestamps are received for a delivery event, the trained predictive models dynamically update the ETA time predictions for successive events. The predictive models may be continuously trained by updating the weighted factors based on the received timestamps.