Picker Routing

Picker routing optimizes the paths taken by warehouse or store employees to fulfill orders, aiming to minimize travel time and maximize efficiency. Current research heavily utilizes reinforcement learning, particularly deep reinforcement learning models and attention-based neural networks, to dynamically adapt routes to fluctuating order demands and constraints like avoiding customer congestion in retail settings. These advancements offer significant potential for improving warehouse and retail operations by reducing order fulfillment times, optimizing resource allocation, and enhancing the customer experience in omnichannel environments.