Abstract: Embodiments are described for leveraging machine-learning models to determine a collection sequence of items of an order. Sequences for collecting a plurality of items of an order are determined, and each of the sequences has a different arrangement of the plurality of items. Total appeasement values are determined for each of the sequences based in part on an appeasement model. Collection times are predicted for each of the sets of sequences. The sequences of the set are scored based in part on the total appeasement values and the collection times. A sequence is selected from the set based in part on the scoring. The selected sequence is provided to a picker client device. A picker associated with the picker client device may fulfill the order and collect the plurality of items in accordance with the selected sequence.

Abstract: An online concierge system schedules pickers (shoppers) to fulfill orders from users. During periods of peak demand, the system increases compensation to shoppers to encourage more to participate, thereby reducing missed orders. The system determines an optimal multiplier to increase compensation based on predictive models of supply and demand and then applying an optimization algorithm to search different hyperparameters that affect how the models generate the multipliers. The system selects the optimal multipliers for different time periods and locations. The system may further present the multipliers being offered during future time periods and enable users to activate reminder alerts for select periods. The offers may be presented in a ranked list using a model trained to infer likelihoods of the user accepting participation and/or setting a reminder notification.

Abstract: A method or system for dynamically optimizing order fulfillment wait times for shoppers using machine learning. The system identifies a shopper's current location via their client device. A trained machine learning model predicts: (i) a first wait time for receiving orders at the current location, (ii) a second wait time at an alternate location, and (iii) a travel time between the two locations. The model is trained using labeled data of shopper wait times, iteratively refining parameters to minimize prediction error. A combined wait time for the alternate location is computed by summing the predicted second wait time and the travel time. If the combined wait time is shorter than the wait time at the current location, the system suggests the alternate location to the shopper. Instructions are transmitted to the shopper's device to display a user interface with a map, the suggested route, and a recommendation indicator.

Abstract: An online concierge system schedules pickers (shoppers) to fulfill orders from users. During periods of peak demand, the system increases compensation to shoppers to encourage more to participate, thereby reducing missed orders. The system determines an optimal multiplier to increase compensation based on predictive models of supply and demand and then applying an optimization algorithm to search different hyperparameters that affect how the models generate the multipliers. The system selects the optimal multipliers for different time periods and locations. The system may further present the multipliers being offered during future time periods and enable users to activate reminder alerts for select periods. The offers may be presented in a ranked list using a model trained to infer likelihoods of the user accepting participation and/or setting a reminder notification.

Abstract: Techniques for predicting a wait time for a shopper based on a location the shopper's client device are presented. A system identifies a shopper's current location and uses a machine learning model to predict a wait time until the shopper will receive one or more orders. The machine learning model is trained to use input features including a number of orders received during a current time period for fulfillment near the current location, a number of other shoppers available for fulfilling orders during the current time period near the current location, historical information about a presentation of a plurality of orders to a plurality of shoppers near the current location, and historical information about the shopper and the other nearby available shoppers. The system then sends the predicted wait time to the client device for presentation to the shopper.

Abstract: An online concierge system uses a machine-learned parking quality model to quantify the suitability of a particular parking location (e.g., a parking lot, or a street) for use when performing purchases at a retail location on behalf of customers. The parking quality model's output is determined according to input features related to parking at a candidate parking location, such as a current time, a current degree of demand for shoppers at the retail location, or a current average shopper wait time at the retail location before receiving an order. The online concierge system provides suggested alternate parking locations to a client device of the shopper, where they may be displayed, e.g., as part of an electronic map. Use of the suggested alternate parking locations helps to preserve parking availability in restricted areas such as retailer parking lots and to reduce traffic congestion in the area of the retailer.

Abstract: An online concierge system schedules pickers (shoppers) to fulfill orders from users. During periods of peak demand, the system increases compensation to shoppers to encourage more to participate, thereby reducing missed orders. The system determines an optimal multiplier to increase compensation based on predictive models of supply and demand and then applying an optimization algorithm to search different hyperparameters that affect how the models generate the multipliers. The system selects the optimal multipliers for different time periods and locations. The system may further present the multipliers being offered during future time periods and enable users to activate reminder alerts for select periods. The offers may be presented in a ranked list using a model trained to infer likelihoods of the user accepting participation and/or setting a reminder notification.

Abstract: Techniques for predicting a wait time for a shopper based on a location the shopper's client device are presented. A system identifies a shopper's current location and uses a machine learning model to predict a wait time until the shopper will receive one or more orders. The machine learning model is trained to use input features including a number of orders received during a current time period for fulfillment near the current location, a number of other shoppers available for fulfilling orders during the current time period near the current location, historical information about a presentation of a plurality of orders to a plurality of shoppers near the current location, and historical information about the shopper and the other nearby available shoppers. The system then sends the predicted wait time to the client device for presentation to the shopper.