Abstract: A system and method for slot selection is provided. A customer order is received for an in-store pickup at a retail store and a set of inputs is received. The set of inputs includes slot-related information related to a first set of timeslots available at the retail store within a first time-period, inventory information, and order preparation constraints. Based on the set of inputs, a capacity constraint is determined. The capacity constraint sets a number of customer orders to be scheduled for the in-store pickup within each of the first set of timeslots. Thereafter, a first timeslot is determined from among the first set of timeslots. The first timeslot accommodates the customer order in accordance with the capacity constraint and a pickup wait time within the first timeslot is below a threshold. The first timeslot is displayed on a customer device.

Abstract: A system and method for order fulfillment is provided. A pickup timeslot in which a customer order is scheduled for pickup from a retail store is received along with order servicing constraints of human workers of the retail store. The customer order is divided into a set of suborders and one or more timeslots in which the set of suborders is to be serviced are determined based on the pickup timeslot and the order servicing constraints. A set of human workers available to work at the retail store within the one or more timeslots is identified and an optimization problem is solved to determine an assignment to service the set of suborders. The assignment maps the set of suborders to the set of human workers and is such that utilization of total hours available with the set of human workers to service the set of suborders is above a threshold.

Abstract: A system for personalized selection of pickup slots using machine learning is disclosed. A historical data is received related to past customer orders placed by a customer at a retail store within a first period. A set of inputs which includes slot-related information related to timeslots available at the retail store within a second period, inventory information, and order preparation constraints are received. A set of training data is prepared based on the historical data and the set of inputs. Thereafter, a machine learning model is trained on the prepared set of training data for a timeslot prediction task. A customer order is received for an in-store pickup at the retail store within the second period. By using the trained machine learning model, a first timeslot is determined for the in-store pickup of the customer order. The first timeslot is displayed on a customer device.

Abstract: A system and method for slot selection is provided. A customer order is received for an in-store pickup at a retail store and a set of inputs is received. The set of inputs includes slot-related information related to a first set of timeslots available at the retail store within a first time-period, inventory information, and order preparation constraints. Based on the set of inputs, a capacity constraint is determined. The capacity constraint sets a number of customer orders to be scheduled for the in-store pickup within each of the first set of timeslots. Thereafter, a first timeslot is determined from among the first set of timeslots. The first timeslot accommodates the customer order in accordance with the capacity constraint and a pickup wait time within the first timeslot is below a threshold. The first timeslot is displayed on a customer device.