Metamodel-Based Order Picking for Automated Storage and Retrieval Systems

Automated warehouses, a key component of modern supply chain processes, have been widely introduced in various industries. Their automation, including sophisticated systems such as automated storage and retrieval systems, plays a crucial role in improving efficiency and reducing operational costs. This paper focuses on the order picking problem in a multi-level shuttle system, which aims at predicting the time required to fulfill a set of picking orders, i.e., the makespan. A metamodel based on a neural network architecture that exploits long short-term memory and linear layers is proposed. The metamodel was trained and tested on synthetic data from a stochastic discrete event simulation model. Extensive experiments illustrate the validity of the metamodel in accurately predicting the makespan. The study not only advances theoretical modeling in the context of automated warehousing, but also outlines future research directions for improved metamodel performance and broader applications, such as stochastic optimization and deadlock prediction.