Optimal Order Batching for Automated Warehouse Picking

Abstract

With the unexpected increase in demand and the need to minimize human interaction during the Covid-19 pandemic, companies have been forced to accelerate the transition from traditional to robotic mobile fulfillment systems. The key to a successful warehouse management system, whether traditional or automated, is an efficient order-picking process. In this study, we focus on the order batching problem, where items and orders are grouped into batches for simultaneous picking in automated warehouses that use autonomous picking carts. We propose five different mathematical models, including a generalized quadratic assignment model. We focus on the latter as it provides the best results and propose a Lagrangian relaxation to obtain lower bounds and an iterative Simulated Annealing (SA) algorithm that generates an initial solution using a K-means clustering algorithm. We carry out testing using an open-source dataset to assess the iterative SA algorithm in minimizing congestion and travel distance in an automated warehouse. We find that it finds solutions of good quality as measured by Lagrangian relaxation and is capable of solving large realistic instances. The solutions successfully minimize travel distance and reduce congestion by limiting path intersections.