Gaussian process-based storage location assignments with risk assessments for progressive zone picking systems

Abstract

E-commerce warehouses are under constant pressure to adapt their order picking systems and reassign product storage locations to meet fluctuating customer demands. Most existing approaches optimize storage location reassignments based on customer orders and operational configurations to maintain high order picking performance. This paper presents a Gaussian process surrogate model (GPSM) approach to predict the performance metrics for storage location reassignments. The GPSM estimates the expected flow time of orders from the historical data on previous storage location assignments and aids in identifying the new assignments that yield the minimum estimated average flow times. Management can also take advantage of the GPSM’s uncertainty quantification capability to assess the probability of improvement for a given storage reassignment and its implementation. The proposed model and assignment policy are validated using discrete-event simulations and industrial data. Experimental results demonstrate that the GPSM can improve expected flow time by 7.51% and reduce unnecessary reassignment operations by 43.25%.