Omnichannel services, such as buy-online-pickup-in-store, curbside pickup, and ship-from-store, have shifted the order-picking tasks that used to be completed by in-store customers doing their own shopping to the responsibility of retailers. To fulfill these orders, many retailers have deployed a store fulfillment strategy, where online orders are picked from inventory in brick-and-mortar stores. As store fulfillment is currently a labor-intensive operation, we propose an innovative approach that relies on the assistance of in-store customers for item extraction from the store shelves and a fleet of collaborative robots to collect and transport them to a designated station. While collaborative robots are manageable by the store, the arrival of in-store customers who are willing to assist a collaborative robot at a given location in the store is out of the store's control, and therefore, uncertain. We model the stochastic order-picking problem with uncertain synchronization times of in-store customers and collaborative robots as a Markov Decision Process to determine how a retailer should dynamically assign tasks to a set of collaborative robots and dedicated pickers. We develop a heuristic solution framework that generates a set of initial assignments and routes for picking resources and dynamically updates them as the actual synchronization times between collaborative robots and in-store customers unfold. We analyze multiple strategies to generate the initial set of task assignments and routes as well as update such decisions based on the system state. We test our proposed approaches using actual online grocery data. Computational results illustrate the potential for collaborative robots and in-store customers to achieve equivalent pick rates as systems with only dedicated pickers. Lastly, our solution approach is capable of generating high-quality solutions at a pace suitable for practical settings.
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