Comparison of static and dynamic order-picking in warehouse systems

Abstract

This study examines the optimization of orderpicking routes in warehouse logistics, with a focus on comparing static and dynamic models. The objective is to determine how different methods impact computational costs and efficiency. While dynamic models offer greater flexibility but require higher computational effort, static models, which use fixed order lists, perform well under stable conditions. Despite advancements in dynamic routing, a direct comparison of the performance of Ant Colony Optimization and Nearest Distance Optimization under such conditions has not yet been conducted. This study analyzes their efficiency through a structured comparison of order-picking models. This comparison is based on a simulated warehouse setup with orders that arrive dynamically during the ongoing picking process. The results show that Ant Colony Optimization achieves total travel distances during order picking which are 8% to 13% shorter than those from Nearest Distance Optimization.