Abstract: An online concierge system receives orders from users identifying items and a warehouses from which the items are obtained. The online concierge system displays groups of one or more orders to shoppers, allowing a shopper to select a group of orders for fulfillment. When selecting groups of orders to display to shoppers, the online concierge system accounts for costs for fulfilling different groups and displays groups having costs satisfying one or more criteria, while maintaining one or more restrictions on times to fulfill orders. The online concierge system trains a selection prediction model to predict an amount of time for a shopper to select a group of orders and determines an estimated fulfillment time for the group from the predicted amount of time. Accounting for the predicted selection time allows the online concierge system to identify a larger number of groups for which costs of fulfillment are determined.

Abstract: An online concierge system receives information describing the progress of a picker servicing a batch of existing orders and predicts a first likelihood the picker will finish servicing the batch within a threshold amount of time based on the picker's progress and information describing the batch. If the first likelihood exceeds a threshold likelihood, the system accesses a machine learning model trained to predict a second likelihood the picker will accept a batch of new orders for servicing while servicing the batch of existing orders. The system applies the model to inputs including a set of attributes of the picker and the picker's progress to predict the second likelihood. The system matches batches of new orders with pickers based on the second likelihood and sends one or more requests to service one or more batches matched with the picker to a client device associated with the picker.

Abstract: An online concierge system receives information describing the progress of a picker servicing a batch of existing orders and a service request for an order. The system identifies picker attributes of the picker and order attributes of the order and each existing order of the set and accesses a machine learning model trained to predict a likelihood the picker will accept an add-on request to add the order to the batch of existing orders. To predict the likelihood, the system applies the model to the picker attributes, the progress of the picker, and the order attributes. The system determines a cost associated with sending the add-on request to the picker based on the likelihood and assigns the order to a set of orders based on the cost. The system sends the add-on request to the picker responsive to determining the order is assigned to the batch of existing orders.

Abstract: An online concierge system generates a set of candidate estimated times of arrival (ETAs) for delivery of a set of items being purchased by a user. Each candidate ETA is scored by using a machine-learned model to estimate values for different criteria of interest, such as likelihood of acceptance of the ETA, cost of delivery of the items to the user, and the like. The values for the different criteria may be combined to generate the overall score for a candidate ETA. One or more of the highest-scoring ETAs are selected and provided to the user, who may then approve one of the ETAs for use with delivery of the user's set of items.

Abstract: An online concierge system accesses and applies a model to predict likelihoods of acceptance of a service request for an order by pickers. The system accesses timespan distributions for accepted service requests and identifies sets of pickers based on the order. Based on the likelihoods and distributions, the system generates simulated responses of the sets of pickers to the service request and trains an additional model based on attributes of the order, the simulated responses, and information associated with corresponding sets of pickers. The system receives a new order, identifies additional sets of pickers based on the new order, and applies the additional model to predict responses of the additional sets of pickers to an additional service request for the new order. Based on the predicted responses and a delivery time associated with the new order, a minimum number of pickers to send the additional service request is determined.