Abstract: A customer order is received and associated with a list of items being ordered from an in-store counter of a store. An estimated time of preparing the order by attendants of the in-store counter is calculated. The customer selects a time to pickup the order after presented with the estimated required time to fulfill and prepare the order. The order is placed on behalf of the customer with the store. Status updates for the order are sent to the customer on a customer-operated device. Once the status of the order indicates the order is completed, the customer travels to the in-store counter within the store and picks up the order.

Abstract: When a new order is received and is to be inserted into an unprepared order queue for order preparation. The new order's items and items' ingredients are obtained along with expected preparation/cook time for each ingredient. Items and ingredients for the orders that are already in the queue are also inspected along with the times that each order was placed in the queue. A machine-learning model is processed with the data associated with the orders and the new order. The model returns an optimized rearrangement of the queue with the new order inserted into the queue that levels out the expected order wait times, reduces variations in order wait times, and minimizes order preparation times based on the rearranged/modified queue. The optimized queue is presented on a display associated with staff responsible for preparing the orders of the queue.

Abstract: Cameras capture time-stamped images of customers, carts, and items possessed by the customers. Customer queues at checkout stations/transaction terminals are monitored for queue lengths, opened terminals, closed terminals, and a total number of items possessed by each customer within each queue. Queue wait times are calculated for each queue and the queue wait times are displayed on monitors adjacent to the queues. Customers approaching the checkout stations are directed to specific queues based on the queue wait times. Closed terminals are opened in advance of a time when a shortest expected wait time for the queues is expected to exceed a threshold wait time. In an embodiment, each queue length is managed based on staff performance metrics associated with staff operating the terminals.

Abstract: Cameras capture time-stamped images of customers, carts, and items possessed by the customers. Customer queues at checkout stations/transaction terminals are monitored for queue lengths, opened terminals, closed terminals, and a total number of items possessed by each customer within each queue. Queue wait times are calculated for each queue and the queue wait times are displayed on monitors adjacent to the queues. Customers approaching the checkout stations are directed to specific queues based on the queue wait times. Closed terminals are opened in advance of a time when a shortest expected wait time for the queues is expected to exceed a threshold wait time. In an embodiment, each queue length is managed based on staff performance metrics associated with staff operating the terminals.