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: A system and method that includes displaying a graphical depiction of projected demand, with values of the projected demand calculated using a constraint, over a future period of time; displaying, over the graphical depiction, a selectable graphical element at a first position that is associated with a first value of the constraint; and wherein the selectable graphical element is adjustable to change the value of the constraint; receiving an indication that the selectable graphical element has been adjusted to a second position associated with a second value of the constraint; and updating the graphical depiction using updated values for projected demand over the future period of time. Projected demand may be part of transportation-related data, on which basis a recommended management plan for a disruption may be identified, the plan being identified with a network model and including strategic travel leg delays and strategic travel leg cancellations.

Abstract: The online concierge system generates task units based on orders and assigns batches of task units to pickers. The online concierge system generates task units based on received orders. The online concierge system generates permutations of these task units to generate candidate sets of task batches. The online concierge system scores each of these candidate sets, and selects a set of task batches to assign to pickers based on the scores. Additionally, to determine which task UI to display to the picker, the picker client device uses a UI state machine. The UI state machine is a state machine where each state corresponds to a task UI to display on the picker client device. The state transitions between the UI states of the UI state machine indicate which UI state to transition to from a current UI state based on the next task unit in the received task batch.