Abstract: An online concierge identifies orders to shoppers, allowing shoppers to select orders for fulfillment. The online concierge system may generate batches that include multiple orders, allowing a shopper to select a batch to fulfill multiple orders. As orders are continuously being received, delaying identification of orders to shoppers may allow greater batching of orders. To allow greater opportunities for batching, the online concierge system estimates a benefit for delaying identification of an order by different time intervals and predicts an amount of time to fulfill the order. The online concierge system then delays assigning orders for which there is a threshold benefit for delaying and selects a time interval for delaying identification of the order that does not result in greater than a threshold likelihood of a late fulfillment of the order.

Abstract: An online concierge system generates order batches for pickers and offers those order batches. The online concierge system generates a set of candidate order batches, which are subsets of a received set of orders. The online concierge system generates a set of order batch scores for each of a set of candidate pickers, and offers the candidate order batches to the candidate pickers for the candidate pickers to service based on the set of order batch scores. If a candidate picker accepts the offered order batch, the online concierge system identifies candidate order batches with overlapping orders with the order batch accepted by the candidate picker, and rescinds the offer for each of the candidate pickers to service the order batches.

Abstract: An online concierge identifies orders to shoppers, allowing shoppers to select orders for fulfillment. The online concierge system may generate batches that include multiple orders, allowing a shopper to select a batch to fulfill multiple orders. As orders are continuously being received, delaying identification of orders to shoppers may allow greater batching of orders. To allow greater opportunities for batching, the online concierge system estimates a benefit for delaying identification of an order by different time intervals and predicts an amount of time to fulfill the order. The online concierge system then delays assigning orders for which there is a threshold benefit for delaying and selects a time interval for delaying identification of the order that does not result in greater than a threshold likelihood of a late fulfillment of the order.

Abstract: An online concierge identifies orders to shoppers, allowing shoppers to select orders for fulfillment. The online concierge system may generate batches that include multiple orders, allowing a shopper to select a batch to fulfill multiple orders. As orders are continuously being received, delaying identification of orders to shoppers may allow greater batching of orders. To allow greater opportunities for batching, the online concierge system estimates a benefit for delaying identification of an order by different time intervals and predicts an amount of time to fulfill the order. The online concierge system then delays assigning orders for which there is a threshold benefit for delaying and selects a time interval for delaying identification of the order that does not result in greater than a threshold likelihood of a late fulfillment of the order.

Abstract: An online concierge system receives orders from users and assigns orders to shoppers for fulfillment. Each order specifies a destination location and a warehouse from which items in the order are obtained. When assigning orders to shoppers, the online concierge system seeks to minimize distances traveled by shoppers fulfilling orders. To more efficiently assign orders to shoppers, the online concierge system trains a distance prediction model to predict a distance traveled between a starting location and a destination location from the starting location, the destination location, and a Haversine distance between the destination location and the starting location. Information identifying distances traveled by shoppers when fulfilling previous orders or information about distances between locations from a third party system may be used to train the distance prediction model.